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Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. 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Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- review: Review a pull request\n- security-review: Complete a security review of the pending changes on the current branch","skillCount":14,"isInitial":true,"names":["deep-research","update-config","keybindings-help","verify","code-review","simplify","fewer-permission-prompts","loop","schedule","claude-api","run","init","review","security-review"]},"type":"attachment","uuid":"5baf9817-cc31-4852-96e5-d03b158a0e52","timestamp":"2026-06-15T21:33:16.047Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"224c91df-c68e-49c7-acbb-9244a6ef78e2","isSidechain":false,"promptId":"31657957-0949-41eb-aad4-cd95d7c838e9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012MTcdmc5QJE2DqDhHkXxpA","type":"tool_result","content":"1\t---\n2\tname: project-agent-gui-lam\n3\tdescription: \"GUI pour agents IA avec avatars LAM 3D parlants en temps réel — état d'avancement complet\"\n4\tmetadata: \n5\t  node_type: memory\n6\t  type: project\n7\t  originSessionId: 7de9b929-9050-430b-9b36-f74849c1c0a7\n8\t---\n9\t\n10\t## Projet principal\n11\t\n12\t`/mnt/DATA/WORK/agent-gui` — web app locale (Python stdlib + vanilla JS) pour piloter des agents LLM avec interface graphique. V1 fonctionnelle avec avatar ASCII, canvas pixel-art animé, chat, sessions JSONL, adaptateurs CLI.\n13\t\n14\t**Why:** Créer une surcouche graphique universelle pour agents IA (Hermes, Claude Code, etc.) avec avatars visuels modélisables.\n15\t\n16\t**How to apply:** Quand on reprend le travail, partir de cet état et continuer vers l'intégration LAM.\n17\t\n18\t---\n19\t\n20\t## Intégration LAM — état au 2026-06-15\n21\t\n22\t### Objectif\n23\tAjouter des avatars 3D photoréalistes parlants en temps réel dans agent-gui. Pipeline :\n24\t```\n25\t[Agent répond] → [TTS → audio 16kHz mono]\n26\t    → [Python: Audio2Expression → 52 blendshapes ARKit @ 30fps]\n27\t        → [WebSocket JSON] → [Browser: WebGL Gaussian Splatting renderer]\n28\t```\n29\t\n30\t### Ce qui est installé et fonctionnel ✅\n31\t\n32\t**Conda envs sur cette machine :**\n33\t- `lam` (Python 3.11) — pour LAM core (génération avatar depuis photo)\n34\t  - PyTorch 2.11+cu128, pytorch3d 0.7.9, diff-gaussian-rasterization, nvdiffrast, simple-knn\n35\t  - gcc 13 installé dans l'env (nécessaire pour compiler pytorch3d avec CUDA 12.8)\n36\t- `lam_a2e` (Python 3.10) — pour Audio2Expression (live talking)\n37\t  - PyTorch 2.11+cu128, transformers, librosa, gradio-gaussian-render\n38\t\n39\t**Repos clonés :**\n40\t- `/mnt/DATA/WORK/LAM` — modèle LAM core (génère avatar 3D depuis photo)\n41\t- `/mnt/DATA/WORK/LAM_Audio2Expression` — Audio2Expression (audio → blendshapes)\n42\t  - Poids téléchargés et extraits : `pretrained_models/lam_audio2exp_flow/`\n43\t  - **Testé et fonctionnel** : 52 canaux ARKit, 30 FPS, 4ms/seconde d'audio après warm-up\n44\t\n45\t**Résultat test Audio2Expression :**\n46\t- Input : `assets/sample_audio/BarackObama_english.wav`\n47\t- Output : `bsData.json` — 240 frames à 30fps, 52 blendshapes ARKit par frame\n48\t- Format : `{ \"names\": [\"browDownLeft\", \"jawOpen\", ...], \"frames\": [[...], ...], \"metadata\": {\"fps\": 30} }`\n49\t\n50\t**Hardware :**\n51\t- GPU : RTX 5090, 32 Go VRAM\n52\t- Drivers NVIDIA : 595.71, CUDA 13.2 (max supporté)\n53\t- CUDA Toolkit 12.8 installé via conda dans les envs\n54\t\n55\t---\n56\t\n57\t### Ce qui reste à faire\n58\t\n59\t**Étape 1 — Exporter l'avatar 3D (bloquant : nécessite Blender 4.0+)**\n60\t- Vérifier si Blender est installé : `blender --version`\n61\t- Si oui : lancer `app_lam.py` dans l'env `lam`, charger une photo portrait, exporter le ZIP avatar\n62\t- Le ZIP contient : `animation.glb`, `skin.glb`, `offset.ply`, `bsData.json`, `vertex_order.json`\n63\t- Des avatars sample sont déjà dispo dans `LAM_Audio2Expression/assets/sample_lam/` (barbara, james, status, vfhq_case1) — peut être utilisé pour tester sans Blender\n64\t\n65\t**Étape 2 — Ajouter TTS dans agent-gui**\n66\t- Options légères : `pyttsx3` (offline, stdlib-like), `edge-tts` (Microsoft gratuit), `kokoro` (qualité)\n67\t- Output : fichier WAV 16kHz mono → passé à Audio2Expression\n68\t\n69\t**Étape 3 — Ajouter WebSocket dans agent_gui/app.py**\n70\t- Ajouter endpoint WebSocket `/ws/avatar`\n71\t- Au moment où l'agent répond : TTS → Audio2Expression → stream blendshapes JSON à 30fps vers browser\n72\t\n73\t**Étape 4 — Remplacer canvas pixel-art par renderer WebGL**\n74\t- Package npm : `gaussian-splat-renderer-for-lam` (repo : `aigc3d/LAM_WebRender`)\n75\t- API : charge le ZIP avatar une fois, polling `getExpressionData()` à chaque frame rAF\n76\t- Ou utiliser les avatars sample GLB directement si le format est compatible\n77\t\n78\t**Alternative rapide sans Blender :**\n79\tUtiliser les avatars sample fournis (`barbara`, `james`) pour tester le pipeline complet avant d'avoir son propre avatar.\n80\t\n81\t---\n82\t\n83\t### Architecture finale visée\n84\t\n85\t```\n86\tagent_gui/\n87\t  app.py          ← ajouter WebSocket + appel subprocess Audio2Expression\n88\t  avatar_lam.py   ← nouveau module : lance lam_a2e env, streame blendshapes\n89\t  tts.py          ← nouveau module : TTS → WAV 16kHz\n90\tstatic/\n91\t  app.js          ← remplacer canvas pixel-art par WebGL renderer LAM\n92\t  avatar.zip      ← avatar exporté depuis photo (ou sample barbara/james)\n93\t```\n94\t\n95\t### Commandes utiles pour reprendre\n96\t\n97\t```bash\n98\t# Tester Audio2Expression\n99\tcd /mnt/DATA/WORK/LAM_Audio2Expression\n100\t$HOME/miniconda3/bin/conda run -n lam_a2e python inference_streaming_audio.py\n101\t\n102\t# Lancer app Gradio LAM (pour exporter avatar depuis photo, nécessite Blender)\n103\tcd /mnt/DATA/WORK/LAM\n104\t$HOME/miniconda3/bin/conda run -n lam python app_lam.py\n105\t\n106\t# Lancer app Gradio Audio2Expression (demo complète avec WebGL)\n107\tcd /mnt/DATA/WORK/LAM_Audio2Expression\n108\t$HOME/miniconda3/bin/conda run -n lam_a2e python app_lam_audio2exp.py\n109\t```\n110\t"}]},"uuid":"db78b263-a8c0-482f-9223-acd29f7712d1","timestamp":"2026-06-15T21:33:19.702Z","toolUseResult":{"type":"text","file":{"filePath":"/home/wildlama/.claude/projects/-home-wildlama/memory/project_agent_gui_lam.md","content":"---\nname: project-agent-gui-lam\ndescription: \"GUI pour agents IA avec avatars LAM 3D parlants en temps réel — état d'avancement complet\"\nmetadata: \n  node_type: memory\n  type: project\n  originSessionId: 7de9b929-9050-430b-9b36-f74849c1c0a7\n---\n\n## Projet principal\n\n`/mnt/DATA/WORK/agent-gui` — web app locale (Python stdlib + vanilla JS) pour piloter des agents LLM avec interface graphique. V1 fonctionnelle avec avatar ASCII, canvas pixel-art animé, chat, sessions JSONL, adaptateurs CLI.\n\n**Why:** Créer une surcouche graphique universelle pour agents IA (Hermes, Claude Code, etc.) avec avatars visuels modélisables.\n\n**How to apply:** Quand on reprend le travail, partir de cet état et continuer vers l'intégration LAM.\n\n---\n\n## Intégration LAM — état au 2026-06-15\n\n### Objectif\nAjouter des avatars 3D photoréalistes parlants en temps réel dans agent-gui. Pipeline :\n```\n[Agent répond] → [TTS → audio 16kHz mono]\n    → [Python: Audio2Expression → 52 blendshapes ARKit @ 30fps]\n        → [WebSocket JSON] → [Browser: WebGL Gaussian Splatting renderer]\n```\n\n### Ce qui est installé et fonctionnel ✅\n\n**Conda envs sur cette machine :**\n- `lam` (Python 3.11) — pour LAM core (génération avatar depuis photo)\n  - PyTorch 2.11+cu128, pytorch3d 0.7.9, diff-gaussian-rasterization, nvdiffrast, simple-knn\n  - gcc 13 installé dans l'env (nécessaire pour compiler pytorch3d avec CUDA 12.8)\n- `lam_a2e` (Python 3.10) — pour Audio2Expression (live talking)\n  - PyTorch 2.11+cu128, transformers, librosa, gradio-gaussian-render\n\n**Repos clonés :**\n- `/mnt/DATA/WORK/LAM` — modèle LAM core (génère avatar 3D depuis photo)\n- `/mnt/DATA/WORK/LAM_Audio2Expression` — Audio2Expression (audio → blendshapes)\n  - Poids téléchargés et extraits : `pretrained_models/lam_audio2exp_flow/`\n  - **Testé et fonctionnel** : 52 canaux ARKit, 30 FPS, 4ms/seconde d'audio après warm-up\n\n**Résultat test Audio2Expression :**\n- Input : `assets/sample_audio/BarackObama_english.wav`\n- Output : `bsData.json` — 240 frames à 30fps, 52 blendshapes ARKit par frame\n- Format : `{ \"names\": [\"browDownLeft\", \"jawOpen\", ...], \"frames\": [[...], ...], \"metadata\": {\"fps\": 30} }`\n\n**Hardware :**\n- GPU : RTX 5090, 32 Go VRAM\n- Drivers NVIDIA : 595.71, CUDA 13.2 (max supporté)\n- CUDA Toolkit 12.8 installé via conda dans les envs\n\n---\n\n### Ce qui reste à faire\n\n**Étape 1 — Exporter l'avatar 3D (bloquant : nécessite Blender 4.0+)**\n- Vérifier si Blender est installé : `blender --version`\n- Si oui : lancer `app_lam.py` dans l'env `lam`, charger une photo portrait, exporter le ZIP avatar\n- Le ZIP contient : `animation.glb`, `skin.glb`, `offset.ply`, `bsData.json`, `vertex_order.json`\n- Des avatars sample sont déjà dispo dans `LAM_Audio2Expression/assets/sample_lam/` (barbara, james, status, vfhq_case1) — peut être utilisé pour tester sans Blender\n\n**Étape 2 — Ajouter TTS dans agent-gui**\n- Options légères : `pyttsx3` (offline, stdlib-like), `edge-tts` (Microsoft gratuit), `kokoro` (qualité)\n- Output : fichier WAV 16kHz mono → passé à Audio2Expression\n\n**Étape 3 — Ajouter WebSocket dans agent_gui/app.py**\n- Ajouter endpoint WebSocket `/ws/avatar`\n- Au moment où l'agent répond : TTS → Audio2Expression → stream blendshapes JSON à 30fps vers browser\n\n**Étape 4 — Remplacer canvas pixel-art par renderer WebGL**\n- Package npm : `gaussian-splat-renderer-for-lam` (repo : `aigc3d/LAM_WebRender`)\n- API : charge le ZIP avatar une fois, polling `getExpressionData()` à chaque frame rAF\n- Ou utiliser les avatars sample GLB directement si le format est compatible\n\n**Alternative rapide sans Blender :**\nUtiliser les avatars sample fournis (`barbara`, `james`) pour tester le pipeline complet avant d'avoir son propre avatar.\n\n---\n\n### Architecture finale visée\n\n```\nagent_gui/\n  app.py          ← ajouter WebSocket + appel subprocess Audio2Expression\n  avatar_lam.py   ← nouveau module : lance lam_a2e env, streame blendshapes\n  tts.py          ← nouveau module : TTS → WAV 16kHz\nstatic/\n  app.js          ← remplacer canvas pixel-art par WebGL renderer LAM\n  avatar.zip      ← avatar exporté depuis photo (ou sample barbara/james)\n```\n\n### Commandes utiles pour reprendre\n\n```bash\n# Tester Audio2Expression\ncd /mnt/DATA/WORK/LAM_Audio2Expression\n$HOME/miniconda3/bin/conda run -n lam_a2e python inference_streaming_audio.py\n\n# Lancer app Gradio LAM (pour exporter avatar depuis photo, nécessite Blender)\ncd /mnt/DATA/WORK/LAM\n$HOME/miniconda3/bin/conda run -n lam python app_lam.py\n\n# Lancer app Gradio Audio2Expression (demo complète avec WebGL)\ncd /mnt/DATA/WORK/LAM_Audio2Expression\n$HOME/miniconda3/bin/conda run -n lam_a2e python app_lam_audio2exp.py\n```\n","numLines":110,"startLine":1,"totalLines":110}},"sourceToolAssistantUUID":"224c91df-c68e-49c7-acbb-9244a6ef78e2","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"c1d434b4-3dd0-4899-bde7-16dc36f6ae69","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01XGVQeMb9m6zc27qRpPmKxx","type":"message","role":"assistant","content":[{"type":"text","text":"Blender 5.1.2 est opérationnel. Voyons ce que `app_lam.py` attend comme chemin Blender."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":148,"cache_read_input_tokens":27111,"output_tokens":148,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":148,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":148,"cache_read_input_tokens":27111,"cache_creation_input_tokens":148,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":148},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mAbTn4HhfVpk45DJ6r","type":"assistant","uuid":"4be71b55-a123-498d-b0de-d4f6eb6ed98d","timestamp":"2026-06-15T21:33:38.859Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"4be71b55-a123-498d-b0de-d4f6eb6ed98d","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01XGVQeMb9m6zc27qRpPmKxx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01K1iAvc6MdEaVmJnAYPgFL9","name":"Bash","input":{"command":"grep -n -i \"blender\" /mnt/DATA/WORK/LAM/app_lam.py | head -20","description":"Check how app_lam.py references Blender"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":148,"cache_read_input_tokens":27111,"output_tokens":148,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":148,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":148,"cache_read_input_tokens":27111,"cache_creation_input_tokens":148,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":148},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mAbTn4HhfVpk45DJ6r","type":"assistant","uuid":"424d394f-4ae4-4692-8909-f38fb6a5b76a","timestamp":"2026-06-15T21:33:39.901Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"424d394f-4ae4-4692-8909-f38fb6a5b76a","isSidechain":false,"promptId":"31657957-0949-41eb-aad4-cd95d7c838e9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01K1iAvc6MdEaVmJnAYPgFL9","type":"tool_result","content":"131:    parser.add_argument(\"--blender_path\", type=str,\n132:                        default='blender' ,help=\"Path to Blender executable\")\n138:    cfg.blender_path = args.blender_path\n300:            cmd = \"thirdparties/blender/blender --background --python 'tools/generateGLBWithBlender_v2.py'\"\n306:                from tools.generateARKITGLBWithBlender import generate_glb\n318:                    blender_exec=Path(cfg.blender_path)\n458:                                              visible=os.path.exists(cfg.blender_path))\n466:                    visible=os.path.exists(cfg.blender_path)","is_error":false}]},"uuid":"05c79ecd-cc94-44ec-829c-9d56a635b432","timestamp":"2026-06-15T21:33:39.975Z","toolUseResult":{"stdout":"131:    parser.add_argument(\"--blender_path\", type=str,\n132:                        default='blender' ,help=\"Path to Blender executable\")\n138:    cfg.blender_path = args.blender_path\n300:            cmd = \"thirdparties/blender/blender --background --python 'tools/generateGLBWithBlender_v2.py'\"\n306:                from tools.generateARKITGLBWithBlender import generate_glb\n318:                    blender_exec=Path(cfg.blender_path)\n458:                                              visible=os.path.exists(cfg.blender_path))\n466:                    visible=os.path.exists(cfg.blender_path)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"424d394f-4ae4-4692-8909-f38fb6a5b76a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"05c79ecd-cc94-44ec-829c-9d56a635b432","isSidechain":false,"attachment":{"type":"task_reminder","content":[],"itemCount":0},"type":"attachment","uuid":"d54b7772-9e30-4e46-9bbc-71905e71a85e","timestamp":"2026-06-15T21:33:40.025Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"d54b7772-9e30-4e46-9bbc-71905e71a85e","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_019sjY5q2X5LH92ErErmVhB5","type":"message","role":"assistant","content":[{"type":"text","text":"Il attend `--blender_path` ou cherche `blender` dans le PATH. Il y a aussi un `thirdparties/blender/blender` en dur à la ligne 300. Deux options :\n\n1. **Ajouter Blender au PATH** (le plus simple)\n2. **Passer `--blender_path`** au lancement\n\nJe recommande d'ajouter un lien symbolique ou de passer le path directement. On lance `app_lam.py` avec :\n\n```bash\ncd /mnt/DATA/WORK/LAM\n$HOME/miniconda3/bin/conda run -n lam python app_lam.py \\\n  --blender_path ~/Downloads/blender-5.1.2-linux-x64/blender\n```\n\nTu veux qu'on lance directement, ou tu préfères d'abord tester avec les avatars sample (`barbara`, `james`) qui sont déjà dispo dans `LAM_Audio2Expression/assets/sample_lam/` — sans avoir besoin de Blender pour cette étape ?"}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":435,"cache_read_input_tokens":27259,"output_tokens":264,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":435,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":264,"cache_read_input_tokens":27259,"cache_creation_input_tokens":435,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":435},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mAwyZKQHvt97fdva47","type":"assistant","uuid":"eb460b38-37ce-4fbe-a436-0d7b329a5101","timestamp":"2026-06-15T21:33:46.650Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"9c90c3e1-d26b-4453-90dc-b8f3d92c791d","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01GCL56hEzi2GPxgysUJ5Ab8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DkkYVYYyrsPYoDF1xDRdMG","name":"Read","input":{"file_path":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1340,"cache_read_input_tokens":29315,"output_tokens":125,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1340,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":125,"cache_read_input_tokens":29315,"cache_creation_input_tokens":1340,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1340},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mK31Q6Hq4YC3XhcPtn","type":"assistant","uuid":"f6422e9b-e862-43d2-80cc-8a3c78c18924","timestamp":"2026-06-15T21:35:32.046Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"f6422e9b-e862-43d2-80cc-8a3c78c18924","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DkkYVYYyrsPYoDF1xDRdMG","type":"tool_result","content":"1\tfrom __future__ import annotations\n2\t\n3\timport argparse\n4\timport json\n5\timport mimetypes\n6\tfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\n7\tfrom pathlib import Path\n8\tfrom urllib.parse import parse_qs, urlparse\n9\t\n10\tfrom .adapters import adapter_for\n11\tfrom .avatar import available_states, get_avatar\n12\tfrom .config import find_agent, load_agents\n13\tfrom .sessions import SessionStore, new_session_id, utc_now\n14\t\n15\tPROJECT_ROOT = Path(__file__).resolve().parent.parent\n16\tSTATIC_ROOT = PROJECT_ROOT / \"static\"\n17\tDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\n18\tDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"\n19\t\n20\t\n21\tclass AgentGuiServer(ThreadingHTTPServer):\n22\t    def __init__(self, server_address, RequestHandlerClass, config_path: Path, sessions_root: Path):\n23\t        super().__init__(server_address, RequestHandlerClass)\n24\t        self.config_path = config_path\n25\t        self.sessions = SessionStore(sessions_root)\n26\t\n27\t    @property\n28\t    def agents(self):\n29\t        return load_agents(self.config_path)\n30\t\n31\t\n32\tclass Handler(BaseHTTPRequestHandler):\n33\t    server: AgentGuiServer\n34\t\n35\t    def log_message(self, format: str, *args):\n36\t        print(\"[agent-gui] \" + format % args)\n37\t\n38\t    def do_GET(self):\n39\t        parsed = urlparse(self.path)\n40\t        if parsed.path == \"/health\":\n41\t            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n42\t        if parsed.path == \"/api/agents\":\n43\t            return self.json_response({\"agents\": self.server.agents})\n44\t        if parsed.path == \"/api/avatar\":\n45\t            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n46\t            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n47\t        if parsed.path == \"/api/sessions\":\n48\t            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n49\t        if parsed.path == \"/api/history\":\n50\t            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n51\t            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n52\t        return self.serve_static(parsed.path)\n53\t\n54\t    def do_POST(self):\n55\t        if self.path == \"/api/session\":\n56\t            return self.json_response({\"session_id\": new_session_id()})\n57\t        if self.path == \"/api/chat\":\n58\t            return self.handle_chat()\n59\t        return self.json_response({\"error\": \"Endpoint inconnu\"}, status=404)\n60\t\n61\t    def handle_chat(self):\n62\t        try:\n63\t            payload = self.read_json()\n64\t            agent_id = str(payload.get(\"agent\", \"echo\"))\n65\t            message = str(payload.get(\"message\", \"\")).strip()\n66\t            session_id = str(payload.get(\"session_id\") or new_session_id())\n67\t            if not message:\n68\t                return self.json_response({\"error\": \"Message vide\"}, status=400)\n69\t\n70\t            agent = find_agent(self.server.agents, agent_id)\n71\t            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n72\t            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n73\t\n74\t            result = adapter_for(agent).run(message, agent, session_id)\n75\t            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n76\t            return self.json_response(\n77\t                {\n78\t                    \"session_id\": session_id,\n79\t                    \"agent\": agent_id,\n80\t                    \"events\": stored_events,\n81\t                    \"avatar\": get_avatar(result.avatar_state),\n82\t                }\n83\t            )\n84\t        except KeyError as exc:\n85\t            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n86\t        except Exception as exc:  # defensive boundary for local UI\n87\t            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)\n88\t\n89\t    def serve_static(self, request_path: str):\n90\t        relative = \"index.html\" if request_path in (\"/\", \"\") else request_path.lstrip(\"/\")\n91\t        path = (STATIC_ROOT / relative).resolve()\n92\t        if STATIC_ROOT.resolve() not in path.parents and path != STATIC_ROOT.resolve():\n93\t            return self.json_response({\"error\": \"Chemin interdit\"}, status=403)\n94\t        if not path.exists() or not path.is_file():\n95\t            return self.json_response({\"error\": \"Fichier introuvable\"}, status=404)\n96\t        content_type = mimetypes.guess_type(str(path))[0] or \"application/octet-stream\"\n97\t        body = path.read_bytes()\n98\t        self.send_response(200)\n99\t        self.send_header(\"Content-Type\", content_type)\n100\t        self.send_header(\"Content-Length\", str(len(body)))\n101\t        self.end_headers()\n102\t        self.wfile.write(body)\n103\t\n104\t    def read_json(self):\n105\t        length = int(self.headers.get(\"Content-Length\", \"0\"))\n106\t        body = self.rfile.read(length).decode(\"utf-8\") if length else \"{}\"\n107\t        return json.loads(body)\n108\t\n109\t    def json_response(self, payload, status: int = 200):\n110\t        body = json.dumps(payload, ensure_ascii=False, indent=2).encode(\"utf-8\")\n111\t        self.send_response(status)\n112\t        self.send_header(\"Content-Type\", \"application/json; charset=utf-8\")\n113\t        self.send_header(\"Content-Length\", str(len(body)))\n114\t        self.end_headers()\n115\t        self.wfile.write(body)\n116\t\n117\t\n118\tdef main(argv: list[str] | None = None):\n119\t    parser = argparse.ArgumentParser(description=\"Agent GUI local\")\n120\t    parser.add_argument(\"--host\", default=\"127.0.0.1\")\n121\t    parser.add_argument(\"--port\", type=int, default=8765)\n122\t    parser.add_argument(\"--config\", type=Path, default=DEFAULT_CONFIG)\n123\t    parser.add_argument(\"--sessions\", type=Path, default=DEFAULT_SESSIONS)\n124\t    args = parser.parse_args(argv)\n125\t\n126\t    server = AgentGuiServer((args.host, args.port), Handler, args.config, args.sessions)\n127\t    print(f\"Agent GUI lancé: http://{args.host}:{args.port}\")\n128\t    print(f\"Config agents: {args.config}\")\n129\t    print(\"Ctrl+C pour arrêter.\")\n130\t    server.serve_forever()\n131\t\n132\t\n133\tif __name__ == \"__main__\":\n134\t    main()\n135\t"}]},"uuid":"c78f8d07-b8b8-4cfb-b9b7-6700f52e9b83","timestamp":"2026-06-15T21:35:32.056Z","toolUseResult":{"type":"text","file":{"filePath":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py","content":"from __future__ import annotations\n\nimport argparse\nimport json\nimport mimetypes\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .adapters import adapter_for\nfrom .avatar import available_states, get_avatar\nfrom .config import find_agent, load_agents\nfrom .sessions import SessionStore, new_session_id, utc_now\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nSTATIC_ROOT = PROJECT_ROOT / \"static\"\nDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\nDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"\n\n\nclass AgentGuiServer(ThreadingHTTPServer):\n    def __init__(self, server_address, RequestHandlerClass, config_path: Path, sessions_root: Path):\n        super().__init__(server_address, RequestHandlerClass)\n        self.config_path = config_path\n        self.sessions = SessionStore(sessions_root)\n\n    @property\n    def agents(self):\n        return load_agents(self.config_path)\n\n\nclass Handler(BaseHTTPRequestHandler):\n    server: AgentGuiServer\n\n    def log_message(self, format: str, *args):\n        print(\"[agent-gui] \" + format % args)\n\n    def do_GET(self):\n        parsed = urlparse(self.path)\n        if parsed.path == \"/health\":\n            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n        if parsed.path == \"/api/agents\":\n            return self.json_response({\"agents\": self.server.agents})\n        if parsed.path == \"/api/avatar\":\n            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n        if parsed.path == \"/api/sessions\":\n            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n        if parsed.path == \"/api/history\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n        return self.serve_static(parsed.path)\n\n    def do_POST(self):\n        if self.path == \"/api/session\":\n            return self.json_response({\"session_id\": new_session_id()})\n        if self.path == \"/api/chat\":\n            return self.handle_chat()\n        return self.json_response({\"error\": \"Endpoint inconnu\"}, status=404)\n\n    def handle_chat(self):\n        try:\n            payload = self.read_json()\n            agent_id = str(payload.get(\"agent\", \"echo\"))\n            message = str(payload.get(\"message\", \"\")).strip()\n            session_id = str(payload.get(\"session_id\") or new_session_id())\n            if not message:\n                return self.json_response({\"error\": \"Message vide\"}, status=400)\n\n            agent = find_agent(self.server.agents, agent_id)\n            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n\n            result = adapter_for(agent).run(message, agent, session_id)\n            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n            return self.json_response(\n                {\n                    \"session_id\": session_id,\n                    \"agent\": agent_id,\n                    \"events\": stored_events,\n                    \"avatar\": get_avatar(result.avatar_state),\n                }\n            )\n        except KeyError as exc:\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n        except Exception as exc:  # defensive boundary for local UI\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)\n\n    def serve_static(self, request_path: str):\n        relative = \"index.html\" if request_path in (\"/\", \"\") else request_path.lstrip(\"/\")\n        path = (STATIC_ROOT / relative).resolve()\n        if STATIC_ROOT.resolve() not in path.parents and path != STATIC_ROOT.resolve():\n            return self.json_response({\"error\": \"Chemin interdit\"}, status=403)\n        if not path.exists() or not path.is_file():\n            return self.json_response({\"error\": \"Fichier introuvable\"}, status=404)\n        content_type = mimetypes.guess_type(str(path))[0] or \"application/octet-stream\"\n        body = path.read_bytes()\n        self.send_response(200)\n        self.send_header(\"Content-Type\", content_type)\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n    def read_json(self):\n        length = int(self.headers.get(\"Content-Length\", \"0\"))\n        body = self.rfile.read(length).decode(\"utf-8\") if length else \"{}\"\n        return json.loads(body)\n\n    def json_response(self, payload, status: int = 200):\n        body = json.dumps(payload, ensure_ascii=False, indent=2).encode(\"utf-8\")\n        self.send_response(status)\n        self.send_header(\"Content-Type\", \"application/json; charset=utf-8\")\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n\ndef main(argv: list[str] | None = None):\n    parser = argparse.ArgumentParser(description=\"Agent GUI local\")\n    parser.add_argument(\"--host\", default=\"127.0.0.1\")\n    parser.add_argument(\"--port\", type=int, default=8765)\n    parser.add_argument(\"--config\", type=Path, default=DEFAULT_CONFIG)\n    parser.add_argument(\"--sessions\", type=Path, default=DEFAULT_SESSIONS)\n    args = parser.parse_args(argv)\n\n    server = AgentGuiServer((args.host, args.port), Handler, args.config, args.sessions)\n    print(f\"Agent GUI lancé: http://{args.host}:{args.port}\")\n    print(f\"Config agents: {args.config}\")\n    print(\"Ctrl+C pour arrêter.\")\n    server.serve_forever()\n\n\nif __name__ == \"__main__\":\n    main()\n","numLines":135,"startLine":1,"totalLines":135}},"sourceToolAssistantUUID":"f6422e9b-e862-43d2-80cc-8a3c78c18924","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"c78f8d07-b8b8-4cfb-b9b7-6700f52e9b83","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01GCL56hEzi2GPxgysUJ5Ab8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UKfuHYrukBqsQZvsoy9A6a","name":"Read","input":{"file_path":"/mnt/DATA/WORK/agent-gui/static/index.html"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1340,"cache_read_input_tokens":29315,"output_tokens":125,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1340,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":125,"cache_read_input_tokens":29315,"cache_creation_input_tokens":1340,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1340},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mK31Q6Hq4YC3XhcPtn","type":"assistant","uuid":"f704f7f0-be8e-4d53-8d9c-49d869348011","timestamp":"2026-06-15T21:35:32.105Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"f704f7f0-be8e-4d53-8d9c-49d869348011","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UKfuHYrukBqsQZvsoy9A6a","type":"tool_result","content":"1\t<!doctype html>\n2\t<html lang=\"fr\">\n3\t<head>\n4\t  <meta charset=\"utf-8\" />\n5\t  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n6\t  <title>Agent GUI Local</title>\n7\t  <link rel=\"stylesheet\" href=\"/styles.css\" />\n8\t</head>\n9\t<body>\n10\t  <div class=\"scanlines\"></div>\n11\t  <div class=\"app-shell\">\n12\t    <aside class=\"sidebar\">\n13\t      <div class=\"brand\">\n14\t        <div class=\"logo\">AG</div>\n15\t        <div>\n16\t          <div class=\"eyebrow\">Pixel command center</div>\n17\t          <h1>Agent GUI</h1>\n18\t          <p>Surcouche locale universelle</p>\n19\t        </div>\n20\t      </div>\n21\t\n22\t      <label for=\"agentSelect\">Agent actif</label>\n23\t      <select id=\"agentSelect\"></select>\n24\t      <p id=\"agentDescription\" class=\"muted\"></p>\n25\t\n26\t      <div class=\"button-row\">\n27\t        <button id=\"newSessionBtn\">Nouvelle session</button>\n28\t        <button id=\"reloadBtn\" class=\"secondary\">Reload crew</button>\n29\t      </div>\n30\t\n31\t      <section class=\"sessions\">\n32\t        <h2>Sessions</h2>\n33\t        <div id=\"sessionsList\"></div>\n34\t      </section>\n35\t    </aside>\n36\t\n37\t    <main class=\"center-column\">\n38\t      <section class=\"office-card\">\n39\t        <div class=\"card-head\">\n40\t          <div>\n41\t            <div class=\"eyebrow\">Virtual office</div>\n42\t            <h2>Bureau animé</h2>\n43\t          </div>\n44\t          <div class=\"office-meta\">\n45\t            <span id=\"statusPill\" class=\"pill idle\">idle</span>\n46\t            <span id=\"sessionLabel\" class=\"session-tag\">Session</span>\n47\t          </div>\n48\t        </div>\n49\t        <canvas id=\"officeCanvas\" width=\"960\" height=\"540\"></canvas>\n50\t        <div id=\"officeCaption\" class=\"office-caption\">Les agents arrivent au bureau.</div>\n51\t      </section>\n52\t\n53\t      <section class=\"chat-card\">\n54\t        <div class=\"card-head compact\">\n55\t          <div>\n56\t            <div class=\"eyebrow\">Conversation</div>\n57\t            <h2>Chat</h2>\n58\t          </div>\n59\t          <div class=\"muted\">Ctrl+Enter pour envoyer</div>\n60\t        </div>\n61\t\n62\t        <section id=\"chatLog\" class=\"chat-log\"></section>\n63\t\n64\t        <form id=\"chatForm\" class=\"composer\">\n65\t          <textarea id=\"messageInput\" rows=\"3\" placeholder=\"Écris à l'agent...\"></textarea>\n66\t          <button type=\"submit\">Envoyer</button>\n67\t        </form>\n68\t      </section>\n69\t    </main>\n70\t\n71\t    <aside class=\"details-column\">\n72\t      <section class=\"panel\">\n73\t        <div class=\"eyebrow\">Crew</div>\n74\t        <h2>Agents présents</h2>\n75\t        <div id=\"crewList\" class=\"crew-list\"></div>\n76\t      </section>\n77\t\n78\t      <section class=\"panel\">\n79\t        <div class=\"eyebrow\">Selected</div>\n80\t        <h2 id=\"selectedAgentName\">Agent</h2>\n81\t        <p id=\"selectedAgentState\" class=\"muted\">idle</p>\n82\t        <div id=\"selectedAgentMeta\" class=\"meta-box\"></div>\n83\t      </section>\n84\t\n85\t      <section class=\"panel\">\n86\t        <div class=\"eyebrow\">Timeline</div>\n87\t        <h2>Activity</h2>\n88\t        <div id=\"timeline\" class=\"timeline\"></div>\n89\t      </section>\n90\t    </aside>\n91\t  </div>\n92\t\n93\t  <script src=\"/app.js\"></script>\n94\t</body>\n95\t</html>\n96\t"}]},"uuid":"b0f1648d-5568-4065-bf5a-b55ed59b7c15","timestamp":"2026-06-15T21:35:32.115Z","toolUseResult":{"type":"text","file":{"filePath":"/mnt/DATA/WORK/agent-gui/static/index.html","content":"<!doctype html>\n<html lang=\"fr\">\n<head>\n  <meta charset=\"utf-8\" />\n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n  <title>Agent GUI Local</title>\n  <link rel=\"stylesheet\" href=\"/styles.css\" />\n</head>\n<body>\n  <div class=\"scanlines\"></div>\n  <div class=\"app-shell\">\n    <aside class=\"sidebar\">\n      <div class=\"brand\">\n        <div class=\"logo\">AG</div>\n        <div>\n          <div class=\"eyebrow\">Pixel command center</div>\n          <h1>Agent GUI</h1>\n          <p>Surcouche locale universelle</p>\n        </div>\n      </div>\n\n      <label for=\"agentSelect\">Agent actif</label>\n      <select id=\"agentSelect\"></select>\n      <p id=\"agentDescription\" class=\"muted\"></p>\n\n      <div class=\"button-row\">\n        <button id=\"newSessionBtn\">Nouvelle session</button>\n        <button id=\"reloadBtn\" class=\"secondary\">Reload crew</button>\n      </div>\n\n      <section class=\"sessions\">\n        <h2>Sessions</h2>\n        <div id=\"sessionsList\"></div>\n      </section>\n    </aside>\n\n    <main class=\"center-column\">\n      <section class=\"office-card\">\n        <div class=\"card-head\">\n          <div>\n            <div class=\"eyebrow\">Virtual office</div>\n            <h2>Bureau animé</h2>\n          </div>\n          <div class=\"office-meta\">\n            <span id=\"statusPill\" class=\"pill idle\">idle</span>\n            <span id=\"sessionLabel\" class=\"session-tag\">Session</span>\n          </div>\n        </div>\n        <canvas id=\"officeCanvas\" width=\"960\" height=\"540\"></canvas>\n        <div id=\"officeCaption\" class=\"office-caption\">Les agents arrivent au bureau.</div>\n      </section>\n\n      <section class=\"chat-card\">\n        <div class=\"card-head compact\">\n          <div>\n            <div class=\"eyebrow\">Conversation</div>\n            <h2>Chat</h2>\n          </div>\n          <div class=\"muted\">Ctrl+Enter pour envoyer</div>\n        </div>\n\n        <section id=\"chatLog\" class=\"chat-log\"></section>\n\n        <form id=\"chatForm\" class=\"composer\">\n          <textarea id=\"messageInput\" rows=\"3\" placeholder=\"Écris à l'agent...\"></textarea>\n          <button type=\"submit\">Envoyer</button>\n        </form>\n      </section>\n    </main>\n\n    <aside class=\"details-column\">\n      <section class=\"panel\">\n        <div class=\"eyebrow\">Crew</div>\n        <h2>Agents présents</h2>\n        <div id=\"crewList\" class=\"crew-list\"></div>\n      </section>\n\n      <section class=\"panel\">\n        <div class=\"eyebrow\">Selected</div>\n        <h2 id=\"selectedAgentName\">Agent</h2>\n        <p id=\"selectedAgentState\" class=\"muted\">idle</p>\n        <div id=\"selectedAgentMeta\" class=\"meta-box\"></div>\n      </section>\n\n      <section class=\"panel\">\n        <div class=\"eyebrow\">Timeline</div>\n        <h2>Activity</h2>\n        <div id=\"timeline\" class=\"timeline\"></div>\n      </section>\n    </aside>\n  </div>\n\n  <script src=\"/app.js\"></script>\n</body>\n</html>\n","numLines":96,"startLine":1,"totalLines":96}},"sourceToolAssistantUUID":"f704f7f0-be8e-4d53-8d9c-49d869348011","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"cool ca fonctionne, implemente ça sur mon gui d'avatar","leafUuid":"b0f1648d-5568-4065-bf5a-b55ed59b7c15","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"permission-mode","permissionMode":"bypassPermissions","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"parentUuid":"b0f1648d-5568-4065-bf5a-b55ed59b7c15","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01PN7mSzRcgQLDsvZ5vHqP8f","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01X4eqZ6BHJcj4gXQFSVq6p1","name":"Read","input":{"file_path":"/mnt/DATA/WORK/agent-gui/static/app.js"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3310,"cache_read_input_tokens":30655,"output_tokens":159,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3310,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":159,"cache_read_input_tokens":30655,"cache_creation_input_tokens":3310,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3310},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mKDRUDYV4JmChJdkiT","type":"assistant","uuid":"e07678c7-3aba-4bf5-91bc-b38647f10d37","timestamp":"2026-06-15T21:35:34.492Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e07678c7-3aba-4bf5-91bc-b38647f10d37","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01X4eqZ6BHJcj4gXQFSVq6p1","type":"tool_result","content":"1\tconst state = {\n2\t  sessionId: localStorage.getItem('agentGuiSession') || null,\n3\t  agents: [],\n4\t  crew: [],\n5\t  selectedAgentId: null,\n6\t  sceneMessage: 'Les agents arrivent au bureau.',\n7\t  selectedStatus: 'idle',\n8\t};\n9\t\n10\tconst OFFICE = {\n11\t  baseWidth: 960,\n12\t  baseHeight: 540,\n13\t};\n14\t\n15\tconst $ = (id) => document.getElementById(id);\n16\t\n17\tfunction clamp(value, min, max) {\n18\t  return Math.max(min, Math.min(max, value));\n19\t}\n20\t\n21\tfunction lerp(a, b, t) {\n22\t  return a + (b - a) * t;\n23\t}\n24\t\n25\tfunction hashString(text) {\n26\t  let hash = 2166136261;\n27\t  for (let i = 0; i < text.length; i += 1) {\n28\t    hash ^= text.charCodeAt(i);\n29\t    hash = Math.imul(hash, 16777619);\n30\t  }\n31\t  return hash >>> 0;\n32\t}\n33\t\n34\tfunction colorFromHash(text, lightness = 58, saturation = 78) {\n35\t  const h = hashString(text) % 360;\n36\t  return `hsl(${h} ${saturation}% ${lightness}%)`;\n37\t}\n38\t\n39\tfunction paletteFor(id) {\n40\t  const seed = hashString(id);\n41\t  const hue = seed % 360;\n42\t  return {\n43\t    shirt: `hsl(${hue} 80% 58%)`,\n44\t    shirtDark: `hsl(${hue} 72% 42%)`,\n45\t    accent: `hsl(${(hue + 42) % 360} 80% 64%)`,\n46\t    pants: `hsl(${(hue + 210) % 360} 30% 30%)`,\n47\t    skin: '#f3c6a4',\n48\t    hair: '#2a2436',\n49\t    outline: '#10131b',\n50\t    light: '#ffffff',\n51\t  };\n52\t}\n53\t\n54\tasync function api(path, options = {}) {\n55\t  const response = await fetch(path, {\n56\t    headers: { 'Content-Type': 'application/json', ...(options.headers || {}) },\n57\t    ...options,\n58\t  });\n59\t  const data = await response.json();\n60\t  if (!response.ok) throw new Error(data.error || `HTTP ${response.status}`);\n61\t  return data;\n62\t}\n63\t\n64\tfunction setStatus(name) {\n65\t  const pill = $('statusPill');\n66\t  pill.textContent = name;\n67\t  pill.className = `pill ${name}`;\n68\t  state.selectedStatus = name;\n69\t  const member = currentCrewMember();\n70\t  if (member) member.mood = name;\n71\t}\n72\t\n73\tfunction setOfficeCaption(text) {\n74\t  state.sceneMessage = text;\n75\t  $('officeCaption').textContent = text;\n76\t}\n77\t\n78\tfunction currentCrewMember() {\n79\t  return state.crew.find((member) => member.id === state.selectedAgentId) || state.crew[0] || null;\n80\t}\n81\t\n82\tfunction updateSelectedAgentCard(member) {\n83\t  if (!member) {\n84\t    $('selectedAgentName').textContent = 'Agent';\n85\t    $('selectedAgentState').textContent = 'idle';\n86\t    $('selectedAgentMeta').textContent = '';\n87\t    return;\n88\t  }\n89\t  $('selectedAgentName').textContent = member.name;\n90\t  $('selectedAgentState').textContent = member.mood || 'idle';\n91\t  $('selectedAgentMeta').textContent = [\n92\t    `id: ${member.id}`,\n93\t    `type: ${member.type || 'unknown'}`,\n94\t    `description: ${member.description || '—'}`,\n95\t    member.command ? `command: ${member.command.join(' ')}` : '',\n96\t  ].filter(Boolean).join('\\n');\n97\t}\n98\t\n99\tfunction getSpawnPoint(index, total) {\n100\t  const cols = Math.max(3, Math.ceil(Math.sqrt(total || 1)));\n101\t  const row = Math.floor(index / cols);\n102\t  const col = index % cols;\n103\t  return {\n104\t    x: 180 + col * 120,\n105\t    y: 350 + row * 36,\n106\t  };\n107\t}\n108\t\n109\tfunction createCrewMember(agent, index, total) {\n110\t  const spawn = getSpawnPoint(index, total);\n111\t  return {\n112\t    id: agent.id,\n113\t    name: agent.name || agent.id,\n114\t    description: agent.description || '',\n115\t    type: agent.type || 'echo',\n116\t    command: agent.command || null,\n117\t    enabled: agent.enabled !== false,\n118\t    palette: paletteFor(agent.id),\n119\t    x: spawn.x,\n120\t    y: spawn.y,\n121\t    tx: spawn.x,\n122\t    ty: spawn.y,\n123\t    face: 1,\n124\t    mood: 'idle',\n125\t    bubble: '',\n126\t    bubbleUntil: 0,\n127\t    wanderUntil: performance.now() + 1000 + index * 420,\n128\t    speed: 34 + (index % 3) * 5,\n129\t    bob: 0,\n130\t    blinkUntil: performance.now() + 1200 + index * 250,\n131\t    selected: false,\n132\t    walkFrame: 0,\n133\t    active: false,\n134\t    lastTalkAt: 0,\n135\t  };\n136\t}\n137\t\n138\tfunction syncCrew() {\n139\t  const byId = new Map(state.crew.map((member) => [member.id, member]));\n140\t  const total = state.agents.length;\n141\t  state.crew = state.agents.map((agent, index) => {\n142\t    const existing = byId.get(agent.id);\n143\t    const base = existing || createCrewMember(agent, index, total);\n144\t    const spawn = getSpawnPoint(index, total);\n145\t    return {\n146\t      ...base,\n147\t      name: agent.name || agent.id,\n148\t      description: agent.description || '',\n149\t      type: agent.type || 'echo',\n150\t      command: agent.command || null,\n151\t      enabled: agent.enabled !== false,\n152\t      palette: base.palette || paletteFor(agent.id),\n153\t      tx: base.tx ?? spawn.x,\n154\t      ty: base.ty ?? spawn.y,\n155\t      x: typeof base.x === 'number' ? base.x : spawn.x,\n156\t      y: typeof base.y === 'number' ? base.y : spawn.y,\n157\t    };\n158\t  });\n159\t\n160\t  if (!state.selectedAgentId && state.crew.length) {\n161\t    state.selectedAgentId = state.crew[0].id;\n162\t  }\n163\t  if (state.selectedAgentId && !state.crew.some((member) => member.id === state.selectedAgentId)) {\n164\t    state.selectedAgentId = state.crew[0]?.id || null;\n165\t  }\n166\t\n167\t  renderCrewList();\n168\t  const selected = currentCrewMember();\n169\t  updateSelectedAgentCard(selected);\n170\t}\n171\t\n172\tfunction renderCrewList() {\n173\t  const root = $('crewList');\n174\t  root.innerHTML = '';\n175\t  for (const member of state.crew) {\n176\t    const item = document.createElement('button');\n177\t    item.type = 'button';\n178\t    item.className = `crew-item ${member.id === state.selectedAgentId ? 'active' : ''}`;\n179\t    item.innerHTML = `\n180\t      <span class=\"crew-swatch\"></span>\n181\t      <span>\n182\t        <span class=\"crew-name\"></span>\n183\t        <span class=\"crew-status\"></span>\n184\t      </span>\n185\t      <span class=\"crew-badge\"></span>\n186\t    `;\n187\t    item.querySelector('.crew-swatch').style.background = colorFromHash(member.id);\n188\t    item.querySelector('.crew-name').textContent = member.name;\n189\t    item.querySelector('.crew-status').textContent = member.mood || 'idle';\n190\t    item.querySelector('.crew-badge').textContent = member.id === state.selectedAgentId ? '●' : '⋯';\n191\t    item.onclick = () => {\n192\t      state.selectedAgentId = member.id;\n193\t      $('agentSelect').value = member.id;\n194\t      renderCrewList();\n195\t      updateSelectedAgentCard(member);\n196\t      setOfficeCaption(`${member.name} est sélectionné.`);\n197\t    };\n198\t    root.appendChild(item);\n199\t  }\n200\t}\n201\t\n202\tfunction renderAgentSelectOptions() {\n203\t  const select = $('agentSelect');\n204\t  select.innerHTML = '';\n205\t  for (const agent of state.agents) {\n206\t    const option = document.createElement('option');\n207\t    option.value = agent.id;\n208\t    option.textContent = agent.name || agent.id;\n209\t    select.appendChild(option);\n210\t  }\n211\t}\n212\t\n213\tfunction addMessage(kind, content, meta = '') {\n214\t  const node = document.createElement('div');\n215\t  node.className = `message ${kind}`;\n216\t  node.innerHTML = `<div class=\"meta\"></div><div class=\"content\"></div>`;\n217\t  node.querySelector('.meta').textContent = meta || kind;\n218\t  node.querySelector('.content').textContent = content;\n219\t  $('chatLog').appendChild(node);\n220\t  $('chatLog').scrollTop = $('chatLog').scrollHeight;\n221\t}\n222\t\n223\tfunction addTimeline(event) {\n224\t  if (!['status', 'tool_request', 'tool_result', 'error'].includes(event.type)) return;\n225\t  const item = document.createElement('div');\n226\t  item.className = `timeline-item ${event.type === 'error' ? 'error' : ''}`;\n227\t  item.innerHTML = `<small></small><div></div>`;\n228\t  item.querySelector('small').textContent = `${event.type} · ${event.agent || ''}`;\n229\t  item.querySelector('div').textContent = event.content || '';\n230\t  $('timeline').prepend(item);\n231\t}\n232\t\n233\tfunction setCrewMood(agentId, mood, bubble = '', ttl = 1600) {\n234\t  const member = state.crew.find((item) => item.id === agentId);\n235\t  if (!member) return;\n236\t  member.mood = mood;\n237\t  member.bubble = bubble;\n238\t  member.bubbleUntil = bubble ? performance.now() + ttl : 0;\n239\t  member.active = agentId === state.selectedAgentId;\n240\t  member.lastTalkAt = performance.now();\n241\t  renderCrewList();\n242\t  updateSelectedAgentCard(currentCrewMember());\n243\t}\n244\t\n245\tfunction setSelectedAgent(agentId) {\n246\t  state.selectedAgentId = agentId;\n247\t  $('agentSelect').value = agentId;\n248\t  for (const member of state.crew) {\n249\t    member.active = member.id === agentId;\n250\t  }\n251\t  const member = currentCrewMember();\n252\t  renderCrewList();\n253\t  updateSelectedAgentCard(member);\n254\t  if (member) {\n255\t    $('agentDescription').textContent = member.description || '';\n256\t  }\n257\t}\n258\t\n259\tfunction renderEvent(event) {\n260\t  if (event.type === 'user_message') {\n261\t    addMessage('user', event.content, 'toi');\n262\t    const member = currentCrewMember();\n263\t    if (member) {\n264\t      member.mood = 'thinking';\n265\t      member.bubble = '';\n266\t      member.active = true;\n267\t    }\n268\t  }\n269\t  if (event.type === 'assistant_message') {\n270\t    addMessage('assistant', event.content, event.agent || 'agent');\n271\t    setCrewMood(event.agent, 'speaking', truncateBubble(event.content), 2400);\n272\t    setOfficeCaption(`${event.agent} a répondu.`);\n273\t  }\n274\t  if (event.type === 'error') {\n275\t    addMessage('error', event.content, event.agent || 'erreur');\n276\t    setCrewMood(event.agent || state.selectedAgentId, 'error', '!', 1800);\n277\t    setOfficeCaption('Un agent a rencontré une erreur.');\n278\t  }\n279\t  if (event.type === 'status') {\n280\t    addTimeline(event);\n281\t    if (/réflexion/i.test(event.content)) {\n282\t      const member = currentCrewMember();\n283\t      if (member) member.mood = 'thinking';\n284\t      setOfficeCaption('L’agent réfléchit...');\n285\t    } else if (/Lancement commande/i.test(event.content)) {\n286\t      const member = currentCrewMember();\n287\t      if (member) member.mood = 'tool';\n288\t      setOfficeCaption('L’agent utilise un outil CLI.');\n289\t    }\n290\t    return;\n291\t  }\n292\t  addTimeline(event);\n293\t  renderCrewList();\n294\t  updateSelectedAgentCard(currentCrewMember());\n295\t}\n296\t\n297\tfunction truncateBubble(text) {\n298\t  const oneLine = String(text).replace(/\\s+/g, ' ').trim();\n299\t  if (oneLine.length <= 22) return oneLine;\n300\t  return `${oneLine.slice(0, 21)}…`;\n301\t}\n302\t\n303\tasync function ensureSession() {\n304\t  if (state.sessionId) return state.sessionId;\n305\t  const data = await api('/api/session', { method: 'POST', body: '{}' });\n306\t  state.sessionId = data.session_id;\n307\t  localStorage.setItem('agentGuiSession', state.sessionId);\n308\t  $('sessionLabel').textContent = `Session ${state.sessionId}`;\n309\t  return state.sessionId;\n310\t}\n311\t\n312\tasync function loadAgents() {\n313\t  const data = await api('/api/agents');\n314\t  state.agents = data.agents;\n315\t  renderAgentSelectOptions();\n316\t  syncCrew();\n317\t  if (!state.selectedAgentId && state.crew[0]) state.selectedAgentId = state.crew[0].id;\n318\t  if (state.selectedAgentId) {\n319\t    $('agentSelect').value = state.selectedAgentId;\n320\t    const member = currentCrewMember();\n321\t    updateSelectedAgentCard(member);\n322\t    $('agentDescription').textContent = member?.description || '';\n323\t  }\n324\t  if (state.crew.length === 0) {\n325\t    setOfficeCaption('Aucun agent disponible.');\n326\t  }\n327\t}\n328\t\n329\tasync function loadSessions() {\n330\t  const data = await api('/api/sessions');\n331\t  const list = $('sessionsList');\n332\t  list.innerHTML = '';\n333\t  for (const session of data.sessions.slice(0, 10)) {\n334\t    const btn = document.createElement('button');\n335\t    btn.type = 'button';\n336\t    btn.textContent = `${session.session_id} · ${session.events} events`;\n337\t    btn.onclick = () => openSession(session.session_id);\n338\t    list.appendChild(btn);\n339\t  }\n340\t}\n341\t\n342\tasync function openSession(sessionId) {\n343\t  state.sessionId = sessionId;\n344\t  localStorage.setItem('agentGuiSession', sessionId);\n345\t  $('sessionLabel').textContent = `Session ${sessionId}`;\n346\t  $('chatLog').innerHTML = '';\n347\t  $('timeline').innerHTML = '';\n348\t  const data = await api(`/api/history?session_id=${encodeURIComponent(sessionId)}`);\n349\t  data.events.forEach(renderEvent);\n350\t}\n351\t\n352\tasync function sendMessage(event) {\n353\t  event.preventDefault();\n354\t  const input = $('messageInput');\n355\t  const message = input.value.trim();\n356\t  if (!message) return;\n357\t  const agent = $('agentSelect').value;\n358\t  const sessionId = await ensureSession();\n359\t\n360\t  input.value = '';\n361\t  renderEvent({ type: 'user_message', content: message, agent });\n362\t  setStatus('thinking');\n363\t  setOfficeCaption(`${agent} est en train de réfléchir...`);\n364\t  setCrewMood(agent, 'thinking', '', 0);\n365\t\n366\t  try {\n367\t    const data = await api('/api/chat', {\n368\t      method: 'POST',\n369\t      body: JSON.stringify({ agent, message, session_id: sessionId }),\n370\t    });\n371\t    data.events.forEach(renderEvent);\n372\t    $('officeCaption').textContent = data.avatar?.state === 'speaking'\n373\t      ? `${agent} est retourné à son bureau.`\n374\t      : state.sceneMessage;\n375\t    setStatus(data.avatar?.state || 'speaking');\n376\t    await loadSessions();\n377\t  } catch (error) {\n378\t    addMessage('error', error.message, 'erreur');\n379\t    setStatus('error');\n380\t    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n381\t    setCrewMood(agent, 'error', '!', 1900);\n382\t  }\n383\t}\n384\t\n385\tasync function newSession() {\n386\t  const data = await api('/api/session', { method: 'POST', body: '{}' });\n387\t  state.sessionId = data.session_id;\n388\t  localStorage.setItem('agentGuiSession', state.sessionId);\n389\t  $('sessionLabel').textContent = `Session ${state.sessionId}`;\n390\t  $('chatLog').innerHTML = '';\n391\t  $('timeline').innerHTML = '';\n392\t  setStatus('idle');\n393\t  setOfficeCaption('Nouvelle session démarrée.');\n394\t  for (const member of state.crew) {\n395\t    member.mood = 'idle';\n396\t    member.bubble = '';\n397\t  }\n398\t  renderCrewList();\n399\t  updateSelectedAgentCard(currentCrewMember());\n400\t}\n401\t\n402\tfunction resizeOfficeCanvas() {\n403\t  const canvas = $('officeCanvas');\n404\t  const rect = canvas.getBoundingClientRect();\n405\t  const dpr = window.devicePixelRatio || 1;\n406\t  canvas.width = Math.max(1, Math.round(rect.width * dpr));\n407\t  canvas.height = Math.max(1, Math.round(rect.height * dpr));\n408\t  const ctx = canvas.getContext('2d');\n409\t  ctx.setTransform(dpr, 0, 0, dpr, 0, 0);\n410\t  ctx.imageSmoothingEnabled = false;\n411\t}\n412\t\n413\tfunction drawPixelRect(ctx, x, y, w, h, color) {\n414\t  ctx.fillStyle = color;\n415\t  ctx.fillRect(Math.round(x), Math.round(y), Math.round(w), Math.round(h));\n416\t}\n417\t\n418\tfunction drawOfficeBackground(ctx, w, h, now) {\n419\t  const horizon = h * 0.40;\n420\t  const floorTop = h * 0.56;\n421\t\n422\t  const wallGrad = ctx.createLinearGradient(0, 0, 0, floorTop);\n423\t  wallGrad.addColorStop(0, '#17233b');\n424\t  wallGrad.addColorStop(1, '#0e1627');\n425\t  ctx.fillStyle = wallGrad;\n426\t  ctx.fillRect(0, 0, w, floorTop);\n427\t\n428\t  const floorGrad = ctx.createLinearGradient(0, floorTop, 0, h);\n429\t  floorGrad.addColorStop(0, '#0f1726');\n430\t  floorGrad.addColorStop(1, '#060b12');\n431\t  ctx.fillStyle = floorGrad;\n432\t  ctx.fillRect(0, floorTop, w, h - floorTop);\n433\t\n434\t  // windows\n435\t  for (let i = 0; i < 3; i += 1) {\n436\t    const wx = w * (0.18 + i * 0.24);\n437\t    const wy = h * 0.10;\n438\t    const ww = w * 0.16;\n439\t    const wh = h * 0.18;\n440\t    ctx.fillStyle = '#0a1730';\n441\t    ctx.fillRect(wx, wy, ww, wh);\n442\t    ctx.fillStyle = 'rgba(110,231,255,0.09)';\n443\t    ctx.fillRect(wx + 4, wy + 4, ww - 8, wh - 8);\n444\t    ctx.strokeStyle = 'rgba(255,255,255,0.10)';\n445\t    ctx.lineWidth = 2;\n446\t    ctx.strokeRect(wx, wy, ww, wh);\n447\t    ctx.beginPath();\n448\t    ctx.moveTo(wx + ww / 2, wy);\n449\t    ctx.lineTo(wx + ww / 2, wy + wh);\n450\t    ctx.moveTo(wx, wy + wh / 2);\n451\t    ctx.lineTo(wx + ww, wy + wh / 2);\n452\t    ctx.stroke();\n453\t  }\n454\t\n455\t  // stars / city lights\n456\t  for (let i = 0; i < 34; i += 1) {\n457\t    const x = (i * 73 + 19) % w;\n458\t    const y = (i * 37 + 11) % Math.max(1, horizon - 10);\n459\t    ctx.fillStyle = i % 4 === 0 ? '#e4f7ff' : '#8ddcff';\n460\t    ctx.fillRect(x, y, 2, 2);\n461\t  }\n462\t  ctx.fillStyle = 'rgba(8, 18, 39, 0.72)';\n463\t  for (let i = 0; i < 10; i += 1) {\n464\t    const x = i * (w / 10) + ((i % 2) * 10);\n465\t    const bh = 28 + (i % 3) * 18;\n466\t    ctx.fillRect(x, horizon - bh * 0.6, 18 + (i % 2) * 12, bh);\n467\t  }\n468\t\n469\t  // wall trim\n470\t  ctx.fillStyle = '#0d1626';\n471\t  ctx.fillRect(0, floorTop - 14, w, 14);\n472\t\n473\t  // perspective floor grid\n474\t  const vanishingX = w * 0.52;\n475\t  const vanishingY = floorTop - 10;\n476\t  ctx.strokeStyle = 'rgba(161,193,236,0.14)';\n477\t  ctx.lineWidth = 1;\n478\t  for (let i = -8; i <= 8; i += 1) {\n479\t    const t = i / 8;\n480\t    ctx.beginPath();\n481\t    ctx.moveTo(w * 0.5, floorTop);\n482\t    ctx.lineTo(vanishingX + t * w * 0.7, h);\n483\t    ctx.stroke();\n484\t  }\n485\t  for (let i = 0; i <= 10; i += 1) {\n486\t    const y = lerp(floorTop, h, i / 10);\n487\t    ctx.beginPath();\n488\t    ctx.moveTo(0, y);\n489\t    ctx.lineTo(w, y);\n490\t    ctx.stroke();\n491\t  }\n492\t\n493\t  // central desk and workstation\n494\t  const deskW = w * 0.22;\n495\t  const deskH = h * 0.10;\n496\t  const deskX = w * 0.41;\n497\t  const deskY = h * 0.68;\n498\t  ctx.fillStyle = '#2a1f35';\n499\t  ctx.fillRect(deskX, deskY, deskW, deskH);\n500\t  ctx.fillStyle = '#1b2437';\n501\t  ctx.fillRect(deskX + 8, deskY + 8, deskW - 16, deskH - 16);\n502\t  ctx.fillStyle = '#0b1120';\n503\t  ctx.fillRect(deskX + deskW * 0.35, deskY - 40, deskW * 0.30, 26);\n504\t  ctx.fillStyle = '#5fe5ff';\n505\t  ctx.fillRect(deskX + deskW * 0.37, deskY - 36, deskW * 0.26, 18);\n506\t  ctx.fillStyle = '#7f8ea8';\n507\t  ctx.fillRect(deskX + deskW * 0.42, deskY - 12, deskW * 0.16, 18);\n508\t  ctx.fillStyle = '#2f3d56';\n509\t  ctx.fillRect(deskX + deskW * 0.48, deskY + 10, deskW * 0.04, 40);\n510\t  ctx.fillRect(deskX + deskW * 0.12, deskY + 24, deskW * 0.16, 12);\n511\t  ctx.fillRect(deskX + deskW * 0.72, deskY + 24, deskW * 0.16, 12);\n512\t\n513\t  // side furniture\n514\t  ctx.fillStyle = '#1a2438';\n515\t  ctx.fillRect(w * 0.08, h * 0.67, w * 0.12, h * 0.11);\n516\t  ctx.fillStyle = '#22324c';\n517\t  ctx.fillRect(w * 0.09, h * 0.69, w * 0.10, h * 0.07);\n518\t  ctx.fillStyle = '#1e2d41';\n519\t  ctx.fillRect(w * 0.80, h * 0.64, w * 0.10, h * 0.13);\n520\t  ctx.fillStyle = '#5af0a6';\n521\t  ctx.fillRect(w * 0.83, h * 0.60, 14, 26);\n522\t  ctx.fillRect(w * 0.79, h * 0.62, 22, 8);\n523\t\n524\t  // plant\n525\t  const pulse = 0.5 + Math.sin(now / 500) * 0.5;\n526\t  ctx.fillStyle = '#14311f';\n527\t  ctx.fillRect(w * 0.88, h * 0.70, 18, 20);\n528\t  ctx.fillStyle = `rgba(90, 240, 166, ${0.6 + pulse * 0.2})`;\n529\t  ctx.fillRect(w * 0.885, h * 0.66, 8, 18);\n530\t  ctx.fillRect(w * 0.895, h * 0.63, 12, 24);\n531\t  ctx.fillRect(w * 0.902, h * 0.66, 8, 18);\n532\t\n533\t  // wall board\n534\t  ctx.fillStyle = '#111a2b';\n535\t  ctx.fillRect(w * 0.06, h * 0.20, w * 0.18, h * 0.18);\n536\t  ctx.strokeStyle = 'rgba(110,231,255,.22)';\n537\t  ctx.strokeRect(w * 0.06, h * 0.20, w * 0.18, h * 0.18);\n538\t  ctx.fillStyle = 'rgba(110,231,255,.10)';\n539\t  ctx.fillRect(w * 0.08, h * 0.24, w * 0.14, 6);\n540\t  ctx.fillRect(w * 0.08, h * 0.29, w * 0.10, 6);\n541\t}\n542\t\n543\tfunction drawSpeechBubble(ctx, x, y, text, color = '#e8f1ff') {\n544\t  const paddingX = 8;\n545\t  const paddingY = 6;\n546\t  const lines = wrapText(text, 16);\n547\t  const lineHeight = 12;\n548\t  const width = Math.max(...lines.map((line) => line.length), 1) * 7 + paddingX * 2;\n549\t  const height = lines.length * lineHeight + paddingY * 2;\n550\t  const bx = x - width / 2;\n551\t  const by = y - height - 16;\n552\t\n553\t  ctx.fillStyle = 'rgba(5, 10, 18, 0.92)';\n554\t  ctx.fillRect(bx, by, width, height);\n555\t  ctx.strokeStyle = 'rgba(110,231,255,0.35)';\n556\t  ctx.lineWidth = 1;\n557\t  ctx.strokeRect(bx, by, width, height);\n558\t  ctx.fillStyle = color;\n559\t  ctx.font = '11px monospace';\n560\t  lines.forEach((line, index) => {\n561\t    ctx.fillText(line, bx + paddingX, by + paddingY + 10 + index * lineHeight);\n562\t  });\n563\t  ctx.fillStyle = 'rgba(5, 10, 18, 0.92)';\n564\t  ctx.beginPath();\n565\t  ctx.moveTo(x - 5, by + height);\n566\t  ctx.lineTo(x + 2, by + height + 8);\n567\t  ctx.lineTo(x + 8, by + height);\n568\t  ctx.closePath();\n569\t  ctx.fill();\n570\t}\n571\t\n572\tfunction wrapText(text, maxChars) {\n573\t  const words = String(text).split(/\\s+/).filter(Boolean);\n574\t  const lines = [];\n575\t  let current = '';\n576\t  for (const word of words) {\n577\t    const next = current ? `${current} ${word}` : word;\n578\t    if (next.length > maxChars && current) {\n579\t      lines.push(current);\n580\t      current = word;\n581\t    } else {\n582\t      current = next;\n583\t    }\n584\t  }\n585\t  if (current) lines.push(current);\n586\t  return lines.slice(0, 3);\n587\t}\n588\t\n589\tfunction drawLittlePerson(ctx, member, now) {\n590\t  const palette = member.palette;\n591\t  const x = member.x;\n592\t  const y = member.y + member.bob;\n593\t  const pose = member.mood;\n594\t  const blink = now > member.blinkUntil && Math.floor(now / 260) % 14 < 1;\n595\t  const outline = palette.outline;\n596\t\n597\t  ctx.save();\n598\t  ctx.translate(x, y);\n599\t  ctx.scale(member.face || 1, 1);\n600\t\n601\t  // shadow\n602\t  ctx.fillStyle = 'rgba(0,0,0,.35)';\n603\t  ctx.beginPath();\n604\t  ctx.ellipse(0, 2, 12, 4, 0, 0, Math.PI * 2);\n605\t  ctx.fill();\n606\t\n607\t  // backpack / jetpack for the Metal-Slug-like vibe\n608\t  drawPixelRect(ctx, -7, -20, 4, 12, palette.shirtDark);\n609\t  drawPixelRect(ctx, 3, -20, 4, 12, palette.shirtDark);\n610\t\n611\t  // legs / boots\n612\t  const walkPhase = Math.floor(now / 180) % 2;\n613\t  const step = pose === 'thinking' ? 0 : walkPhase;\n614\t  const leftForward = step === 0;\n615\t  const legColor = palette.pants;\n616\t  const bootColor = '#0b0f18';\n617\t  const legY = -10;\n618\t\n619\t  if (pose === 'error') {\n620\t    drawPixelRect(ctx, -6, legY, 5, 9, legColor);\n621\t    drawPixelRect(ctx, 1, legY + 1, 5, 8, legColor);\n622\t  } else {\n623\t    drawPixelRect(ctx, leftForward ? -6 : -5, legY, 5, 10, legColor);\n624\t    drawPixelRect(ctx, leftForward ? 1 : 0, legY, 5, 10, legColor);\n625\t    drawPixelRect(ctx, leftForward ? -7 : -6, legY + 8, 6, 3, bootColor);\n626\t    drawPixelRect(ctx, leftForward ? 0 : 1, legY + 8, 6, 3, bootColor);\n627\t  }\n628\t\n629\t  // torso\n630\t  const torsoY = -25;\n631\t  drawPixelRect(ctx, -7, torsoY, 14, 12, outline);\n632\t  drawPixelRect(ctx, -6, torsoY + 1, 12, 10, palette.shirt);\n633\t  drawPixelRect(ctx, -5, torsoY + 3, 10, 6, palette.shirtDark);\n634\t  drawPixelRect(ctx, -2, torsoY + 1, 4, 3, palette.accent);\n635\t\n636\t  // arms\n637\t  if (pose === 'speaking') {\n638\t    drawPixelRect(ctx, -11, torsoY + 2, 5, 3, outline);\n639\t    drawPixelRect(ctx, -10, torsoY + 3, 4, 2, palette.skin);\n640\t    drawPixelRect(ctx, 6, torsoY, 4, 3, outline);\n641\t    drawPixelRect(ctx, 7, torsoY + 1, 3, 2, palette.skin);\n642\t  } else if (pose === 'thinking' || pose === 'tool') {\n643\t    drawPixelRect(ctx, -11, torsoY + 4, 5, 3, outline);\n644\t    drawPixelRect(ctx, -10, torsoY + 5, 4, 2, palette.skin);\n645\t    drawPixelRect(ctx, 6, torsoY + 2, 5, 3, outline);\n646\t    drawPixelRect(ctx, 7, torsoY + 3, 4, 2, palette.skin);\n647\t  } else if (pose === 'error') {\n648\t    drawPixelRect(ctx, -11, torsoY + 5, 4, 3, outline);\n649\t    drawPixelRect(ctx, 6, torsoY + 5, 4, 3, outline);\n650\t  } else {\n651\t    drawPixelRect(ctx, -11, torsoY + 3, 4, 3, outline);\n652\t    drawPixelRect(ctx, 6, torsoY + 3, 4, 3, outline);\n653\t    drawPixelRect(ctx, -10, torsoY + 4, 3, 2, palette.skin);\n654\t    drawPixelRect(ctx, 7, torsoY + 4, 3, 2, palette.skin);\n655\t  }\n656\t\n657\t  // head + hair\n658\t  drawPixelRect(ctx, -7, -36, 14, 11, outline);\n659\t  drawPixelRect(ctx, -6, -35, 12, 9, palette.skin);\n660\t  drawPixelRect(ctx, -7, -38, 14, 4, palette.hair);\n661\t  drawPixelRect(ctx, -6, -37, 12, 2, palette.hair);\n662\t  drawPixelRect(ctx, 2, -34, 3, 3, outline);\n663\t  drawPixelRect(ctx, -4, -34, 2, 2, blink ? palette.hair : palette.light);\n664\t  drawPixelRect(ctx, 1, -34, 2, 2, blink ? palette.hair : palette.light);\n665\t  drawPixelRect(ctx, -3, -30, 6, 2, outline);\n666\t\n667\t  if (pose === 'error') {\n668\t    drawPixelRect(ctx, -4, -33, 2, 2, palette.accent);\n669\t    drawPixelRect(ctx, 2, -33, 2, 2, palette.accent);\n670\t    drawPixelRect(ctx, -3, -31, 6, 1, palette.accent);\n671\t  }\n672\t\n673\t  if (pose === 'tool') {\n674\t    drawPixelRect(ctx, 9, -20, 5, 2, palette.accent);\n675\t    drawPixelRect(ctx, 12, -21, 2, 6, palette.accent);\n676\t  }\n677\t\n678\t  if (member.active) {\n679\t    ctx.strokeStyle = 'rgba(110,231,255,.65)';\n680\t    ctx.lineWidth = 2;\n681\t    ctx.beginPath();\n682\t    ctx.arc(0, -18, 16, 0, Math.PI * 2);\n683\t    ctx.stroke();\n684\t  }\n685\t\n686\t  if (member.bubble && now < member.bubbleUntil) {\n687\t    ctx.restore();\n688\t    drawSpeechBubble(ctx, member.x, member.y - 18 + member.bob, member.bubble);\n689\t    ctx.save();\n690\t    ctx.translate(x, y);\n691\t    ctx.scale(member.face || 1, 1);\n692\t  }\n693\t\n694\t  ctx.fillStyle = member.selected ? '#6ee7ff' : '#8aa1c0';\n695\t  ctx.font = '10px monospace';\n696\t  ctx.fillText(member.name, -18, 22);\n697\t\n698\t  ctx.restore();\n699\t}\n700\t\n701\tfunction updateCrew(now) {\n702\t  const w = $('officeCanvas').clientWidth || OFFICE.baseWidth;\n703\t  const h = $('officeCanvas').clientHeight || OFFICE.baseHeight;\n704\t  const floorTop = h * 0.56;\n705\t  const deskX = w * 0.41;\n706\t  const deskY = h * 0.68;\n707\t  const deskW = w * 0.22;\n708\t  const targetZone = {\n709\t    x: deskX + deskW * 0.50,\n710\t    y: deskY - 18,\n711\t  };\n712\t\n713\t  for (const member of state.crew) {\n714\t    member.selected = member.id === state.selectedAgentId;\n715\t    member.active = member.selected;\n716\t    member.bob = Math.sin(now / 260 + hashString(member.id) * 0.001) * 1.1;\n717\t\n718\t    if (member.mood === 'speaking') {\n719\t      member.tx = targetZone.x;\n720\t      member.ty = targetZone.y;\n721\t      member.face = member.x <= targetZone.x ? 1 : -1;\n722\t    }\n723\t\n724\t    if (member.mood === 'thinking' || member.mood === 'tool') {\n725\t      member.tx = targetZone.x - 40 + (hashString(member.id) % 80);\n726\t      member.ty = targetZone.y + 10 + ((hashString(member.id) >> 4) % 30);\n727\t      member.face = member.x <= member.tx ? 1 : -1;\n728\t    }\n729\t\n730\t    if (member.mood === 'idle' && now > member.wanderUntil) {\n731\t      const wanderPoints = [\n732\t        { x: w * 0.16, y: floorTop + 55 },\n733\t        { x: w * 0.30, y: floorTop + 35 },\n734\t        { x: w * 0.60, y: floorTop + 40 },\n735\t        { x: w * 0.78, y: floorTop + 50 },\n736\t        { x: w * 0.50, y: floorTop + 60 },\n737\t      ];\n738\t      const point = wanderPoints[(hashString(member.id) + Math.floor(now / 5000)) % wanderPoints.length];\n739\t      member.tx = point.x;\n740\t      member.ty = point.y;\n741\t      member.wanderUntil = now + 3500 + (hashString(member.id) % 2500);\n742\t      member.face = member.x <= member.tx ? 1 : -1;\n743\t    }\n744\t\n745\t    const dx = member.tx - member.x;\n746\t    const dy = member.ty - member.y;\n747\t    const dist = Math.hypot(dx, dy);\n748\t\n749\t    if (member.mood === 'error') {\n750\t      member.x += Math.sin(now / 60 + hashString(member.id)) * 0.12;\n751\t      member.y += Math.cos(now / 54 + hashString(member.id)) * 0.10;\n752\t      continue;\n753\t    }\n754\t\n755\t    if (dist > 1.5) {\n756\t      const speed = member.speed * (member.mood === 'thinking' ? 0.6 : member.mood === 'tool' ? 0.85 : 1);\n757\t      const step = Math.min(speed * 0.016, dist);\n758\t      member.x += (dx / dist) * step;\n759\t      member.y += (dy / dist) * step;\n760\t      member.face = dx >= 0 ? 1 : -1;\n761\t      member.walkFrame = Math.floor(now / 170) % 2;\n762\t    } else {\n763\t      member.x = member.tx;\n764\t      member.y = member.ty;\n765\t      member.walkFrame = 0;\n766\t      if (member.mood === 'thinking' || member.mood === 'tool') {\n767\t        member.mood = 'idle';\n768\t        member.bubble = '';\n769\t      }\n770\t      if (member.mood === 'speaking' && now - member.lastTalkAt > 2400) {\n771\t        member.mood = 'idle';\n772\t        member.bubble = '';\n773\t      }\n774\t    }\n775\t  }\n776\t}\n777\t\n778\tfunction drawOffice(now) {\n779\t  const canvas = $('officeCanvas');\n780\t  const ctx = canvas.getContext('2d');\n781\t  const w = canvas.clientWidth || OFFICE.baseWidth;\n782\t  const h = canvas.clientHeight || OFFICE.baseHeight;\n783\t\n784\t  ctx.save();\n785\t  ctx.setTransform(1, 0, 0, 1, 0, 0);\n786\t  ctx.clearRect(0, 0, canvas.width, canvas.height);\n787\t  ctx.scale((canvas.width / canvas.clientWidth) || 1, (canvas.height / canvas.clientHeight) || 1);\n788\t  ctx.imageSmoothingEnabled = false;\n789\t\n790\t  drawOfficeBackground(ctx, w, h, now);\n791\t  updateCrew(now);\n792\t\n793\t  // desk chair glow & monitors for ambience\n794\t  const glow = 0.35 + Math.sin(now / 420) * 0.08;\n795\t  ctx.fillStyle = `rgba(110,231,255,${glow})`;\n796\t  ctx.fillRect(w * 0.47, h * 0.54, 52, 4);\n797\t\n798\t  for (const member of state.crew) {\n799\t    drawLittlePerson(ctx, member, now);\n800\t  }\n801\t\n802\t  ctx.restore();\n803\t}\n804\t\n805\tfunction animate(now) {\n806\t  drawOffice(now);\n807\t  requestAnimationFrame(animate);\n808\t}\n809\t\n810\tfunction initEvents() {\n811\t  $('chatForm').addEventListener('submit', sendMessage);\n812\t  $('newSessionBtn').addEventListener('click', newSession);\n813\t  $('reloadBtn').addEventListener('click', loadAgents);\n814\t  $('agentSelect').addEventListener('change', () => {\n815\t    setSelectedAgent($('agentSelect').value);\n816\t  });\n817\t  $('messageInput').addEventListener('keydown', (event) => {\n818\t    if (event.ctrlKey && event.key === 'Enter') sendMessage(event);\n819\t  });\n820\t  window.addEventListener('resize', resizeOfficeCanvas);\n821\t}\n822\t\n823\tasync function init() {\n824\t  initEvents();\n825\t  resizeOfficeCanvas();\n826\t  await loadAgents();\n827\t  if (!state.selectedAgentId && state.crew[0]) {\n828\t    setSelectedAgent(state.crew[0].id);\n829\t  } else if (state.selectedAgentId) {\n830\t    setSelectedAgent(state.selectedAgentId);\n831\t  }\n832\t  if (state.sessionId) await openSession(state.sessionId);\n833\t  else await newSession();\n834\t  await loadSessions();\n835\t  setOfficeCaption('Le bureau est prêt.');\n836\t  requestAnimationFrame(animate);\n837\t}\n838\t\n839\tinit().catch(async (error) => {\n840\t  console.error(error);\n841\t  setStatus('error');\n842\t  setOfficeCaption('Erreur de démarrage de l’interface.');\n843\t});\n844\t"}]},"uuid":"8fcaf955-a3a1-4437-8e7a-446825f01c93","timestamp":"2026-06-15T21:35:34.906Z","toolUseResult":{"type":"text","file":{"filePath":"/mnt/DATA/WORK/agent-gui/static/app.js","content":"const state = {\n  sessionId: localStorage.getItem('agentGuiSession') || null,\n  agents: [],\n  crew: [],\n  selectedAgentId: null,\n  sceneMessage: 'Les agents arrivent au bureau.',\n  selectedStatus: 'idle',\n};\n\nconst OFFICE = {\n  baseWidth: 960,\n  baseHeight: 540,\n};\n\nconst $ = (id) => document.getElementById(id);\n\nfunction clamp(value, min, max) {\n  return Math.max(min, Math.min(max, value));\n}\n\nfunction lerp(a, b, t) {\n  return a + (b - a) * t;\n}\n\nfunction hashString(text) {\n  let hash = 2166136261;\n  for (let i = 0; i < text.length; i += 1) {\n    hash ^= text.charCodeAt(i);\n    hash = Math.imul(hash, 16777619);\n  }\n  return hash >>> 0;\n}\n\nfunction colorFromHash(text, lightness = 58, saturation = 78) {\n  const h = hashString(text) % 360;\n  return `hsl(${h} ${saturation}% ${lightness}%)`;\n}\n\nfunction paletteFor(id) {\n  const seed = hashString(id);\n  const hue = seed % 360;\n  return {\n    shirt: `hsl(${hue} 80% 58%)`,\n    shirtDark: `hsl(${hue} 72% 42%)`,\n    accent: `hsl(${(hue + 42) % 360} 80% 64%)`,\n    pants: `hsl(${(hue + 210) % 360} 30% 30%)`,\n    skin: '#f3c6a4',\n    hair: '#2a2436',\n    outline: '#10131b',\n    light: '#ffffff',\n  };\n}\n\nasync function api(path, options = {}) {\n  const response = await fetch(path, {\n    headers: { 'Content-Type': 'application/json', ...(options.headers || {}) },\n    ...options,\n  });\n  const data = await response.json();\n  if (!response.ok) throw new Error(data.error || `HTTP ${response.status}`);\n  return data;\n}\n\nfunction setStatus(name) {\n  const pill = $('statusPill');\n  pill.textContent = name;\n  pill.className = `pill ${name}`;\n  state.selectedStatus = name;\n  const member = currentCrewMember();\n  if (member) member.mood = name;\n}\n\nfunction setOfficeCaption(text) {\n  state.sceneMessage = text;\n  $('officeCaption').textContent = text;\n}\n\nfunction currentCrewMember() {\n  return state.crew.find((member) => member.id === state.selectedAgentId) || state.crew[0] || null;\n}\n\nfunction updateSelectedAgentCard(member) {\n  if (!member) {\n    $('selectedAgentName').textContent = 'Agent';\n    $('selectedAgentState').textContent = 'idle';\n    $('selectedAgentMeta').textContent = '';\n    return;\n  }\n  $('selectedAgentName').textContent = member.name;\n  $('selectedAgentState').textContent = member.mood || 'idle';\n  $('selectedAgentMeta').textContent = [\n    `id: ${member.id}`,\n    `type: ${member.type || 'unknown'}`,\n    `description: ${member.description || '—'}`,\n    member.command ? `command: ${member.command.join(' ')}` : '',\n  ].filter(Boolean).join('\\n');\n}\n\nfunction getSpawnPoint(index, total) {\n  const cols = Math.max(3, Math.ceil(Math.sqrt(total || 1)));\n  const row = Math.floor(index / cols);\n  const col = index % cols;\n  return {\n    x: 180 + col * 120,\n    y: 350 + row * 36,\n  };\n}\n\nfunction createCrewMember(agent, index, total) {\n  const spawn = getSpawnPoint(index, total);\n  return {\n    id: agent.id,\n    name: agent.name || agent.id,\n    description: agent.description || '',\n    type: agent.type || 'echo',\n    command: agent.command || null,\n    enabled: agent.enabled !== false,\n    palette: paletteFor(agent.id),\n    x: spawn.x,\n    y: spawn.y,\n    tx: spawn.x,\n    ty: spawn.y,\n    face: 1,\n    mood: 'idle',\n    bubble: '',\n    bubbleUntil: 0,\n    wanderUntil: performance.now() + 1000 + index * 420,\n    speed: 34 + (index % 3) * 5,\n    bob: 0,\n    blinkUntil: performance.now() + 1200 + index * 250,\n    selected: false,\n    walkFrame: 0,\n    active: false,\n    lastTalkAt: 0,\n  };\n}\n\nfunction syncCrew() {\n  const byId = new Map(state.crew.map((member) => [member.id, member]));\n  const total = state.agents.length;\n  state.crew = state.agents.map((agent, index) => {\n    const existing = byId.get(agent.id);\n    const base = existing || createCrewMember(agent, index, total);\n    const spawn = getSpawnPoint(index, total);\n    return {\n      ...base,\n      name: agent.name || agent.id,\n      description: agent.description || '',\n      type: agent.type || 'echo',\n      command: agent.command || null,\n      enabled: agent.enabled !== false,\n      palette: base.palette || paletteFor(agent.id),\n      tx: base.tx ?? spawn.x,\n      ty: base.ty ?? spawn.y,\n      x: typeof base.x === 'number' ? base.x : spawn.x,\n      y: typeof base.y === 'number' ? base.y : spawn.y,\n    };\n  });\n\n  if (!state.selectedAgentId && state.crew.length) {\n    state.selectedAgentId = state.crew[0].id;\n  }\n  if (state.selectedAgentId && !state.crew.some((member) => member.id === state.selectedAgentId)) {\n    state.selectedAgentId = state.crew[0]?.id || null;\n  }\n\n  renderCrewList();\n  const selected = currentCrewMember();\n  updateSelectedAgentCard(selected);\n}\n\nfunction renderCrewList() {\n  const root = $('crewList');\n  root.innerHTML = '';\n  for (const member of state.crew) {\n    const item = document.createElement('button');\n    item.type = 'button';\n    item.className = `crew-item ${member.id === state.selectedAgentId ? 'active' : ''}`;\n    item.innerHTML = `\n      <span class=\"crew-swatch\"></span>\n      <span>\n        <span class=\"crew-name\"></span>\n        <span class=\"crew-status\"></span>\n      </span>\n      <span class=\"crew-badge\"></span>\n    `;\n    item.querySelector('.crew-swatch').style.background = colorFromHash(member.id);\n    item.querySelector('.crew-name').textContent = member.name;\n    item.querySelector('.crew-status').textContent = member.mood || 'idle';\n    item.querySelector('.crew-badge').textContent = member.id === state.selectedAgentId ? '●' : '⋯';\n    item.onclick = () => {\n      state.selectedAgentId = member.id;\n      $('agentSelect').value = member.id;\n      renderCrewList();\n      updateSelectedAgentCard(member);\n      setOfficeCaption(`${member.name} est sélectionné.`);\n    };\n    root.appendChild(item);\n  }\n}\n\nfunction renderAgentSelectOptions() {\n  const select = $('agentSelect');\n  select.innerHTML = '';\n  for (const agent of state.agents) {\n    const option = document.createElement('option');\n    option.value = agent.id;\n    option.textContent = agent.name || agent.id;\n    select.appendChild(option);\n  }\n}\n\nfunction addMessage(kind, content, meta = '') {\n  const node = document.createElement('div');\n  node.className = `message ${kind}`;\n  node.innerHTML = `<div class=\"meta\"></div><div class=\"content\"></div>`;\n  node.querySelector('.meta').textContent = meta || kind;\n  node.querySelector('.content').textContent = content;\n  $('chatLog').appendChild(node);\n  $('chatLog').scrollTop = $('chatLog').scrollHeight;\n}\n\nfunction addTimeline(event) {\n  if (!['status', 'tool_request', 'tool_result', 'error'].includes(event.type)) return;\n  const item = document.createElement('div');\n  item.className = `timeline-item ${event.type === 'error' ? 'error' : ''}`;\n  item.innerHTML = `<small></small><div></div>`;\n  item.querySelector('small').textContent = `${event.type} · ${event.agent || ''}`;\n  item.querySelector('div').textContent = event.content || '';\n  $('timeline').prepend(item);\n}\n\nfunction setCrewMood(agentId, mood, bubble = '', ttl = 1600) {\n  const member = state.crew.find((item) => item.id === agentId);\n  if (!member) return;\n  member.mood = mood;\n  member.bubble = bubble;\n  member.bubbleUntil = bubble ? performance.now() + ttl : 0;\n  member.active = agentId === state.selectedAgentId;\n  member.lastTalkAt = performance.now();\n  renderCrewList();\n  updateSelectedAgentCard(currentCrewMember());\n}\n\nfunction setSelectedAgent(agentId) {\n  state.selectedAgentId = agentId;\n  $('agentSelect').value = agentId;\n  for (const member of state.crew) {\n    member.active = member.id === agentId;\n  }\n  const member = currentCrewMember();\n  renderCrewList();\n  updateSelectedAgentCard(member);\n  if (member) {\n    $('agentDescription').textContent = member.description || '';\n  }\n}\n\nfunction renderEvent(event) {\n  if (event.type === 'user_message') {\n    addMessage('user', event.content, 'toi');\n    const member = currentCrewMember();\n    if (member) {\n      member.mood = 'thinking';\n      member.bubble = '';\n      member.active = true;\n    }\n  }\n  if (event.type === 'assistant_message') {\n    addMessage('assistant', event.content, event.agent || 'agent');\n    setCrewMood(event.agent, 'speaking', truncateBubble(event.content), 2400);\n    setOfficeCaption(`${event.agent} a répondu.`);\n  }\n  if (event.type === 'error') {\n    addMessage('error', event.content, event.agent || 'erreur');\n    setCrewMood(event.agent || state.selectedAgentId, 'error', '!', 1800);\n    setOfficeCaption('Un agent a rencontré une erreur.');\n  }\n  if (event.type === 'status') {\n    addTimeline(event);\n    if (/réflexion/i.test(event.content)) {\n      const member = currentCrewMember();\n      if (member) member.mood = 'thinking';\n      setOfficeCaption('L’agent réfléchit...');\n    } else if (/Lancement commande/i.test(event.content)) {\n      const member = currentCrewMember();\n      if (member) member.mood = 'tool';\n      setOfficeCaption('L’agent utilise un outil CLI.');\n    }\n    return;\n  }\n  addTimeline(event);\n  renderCrewList();\n  updateSelectedAgentCard(currentCrewMember());\n}\n\nfunction truncateBubble(text) {\n  const oneLine = String(text).replace(/\\s+/g, ' ').trim();\n  if (oneLine.length <= 22) return oneLine;\n  return `${oneLine.slice(0, 21)}…`;\n}\n\nasync function ensureSession() {\n  if (state.sessionId) return state.sessionId;\n  const data = await api('/api/session', { method: 'POST', body: '{}' });\n  state.sessionId = data.session_id;\n  localStorage.setItem('agentGuiSession', state.sessionId);\n  $('sessionLabel').textContent = `Session ${state.sessionId}`;\n  return state.sessionId;\n}\n\nasync function loadAgents() {\n  const data = await api('/api/agents');\n  state.agents = data.agents;\n  renderAgentSelectOptions();\n  syncCrew();\n  if (!state.selectedAgentId && state.crew[0]) state.selectedAgentId = state.crew[0].id;\n  if (state.selectedAgentId) {\n    $('agentSelect').value = state.selectedAgentId;\n    const member = currentCrewMember();\n    updateSelectedAgentCard(member);\n    $('agentDescription').textContent = member?.description || '';\n  }\n  if (state.crew.length === 0) {\n    setOfficeCaption('Aucun agent disponible.');\n  }\n}\n\nasync function loadSessions() {\n  const data = await api('/api/sessions');\n  const list = $('sessionsList');\n  list.innerHTML = '';\n  for (const session of data.sessions.slice(0, 10)) {\n    const btn = document.createElement('button');\n    btn.type = 'button';\n    btn.textContent = `${session.session_id} · ${session.events} events`;\n    btn.onclick = () => openSession(session.session_id);\n    list.appendChild(btn);\n  }\n}\n\nasync function openSession(sessionId) {\n  state.sessionId = sessionId;\n  localStorage.setItem('agentGuiSession', sessionId);\n  $('sessionLabel').textContent = `Session ${sessionId}`;\n  $('chatLog').innerHTML = '';\n  $('timeline').innerHTML = '';\n  const data = await api(`/api/history?session_id=${encodeURIComponent(sessionId)}`);\n  data.events.forEach(renderEvent);\n}\n\nasync function sendMessage(event) {\n  event.preventDefault();\n  const input = $('messageInput');\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $('agentSelect').value;\n  const sessionId = await ensureSession();\n\n  input.value = '';\n  renderEvent({ type: 'user_message', content: message, agent });\n  setStatus('thinking');\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, 'thinking', '', 0);\n\n  try {\n    const data = await api('/api/chat', {\n      method: 'POST',\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $('officeCaption').textContent = data.avatar?.state === 'speaking'\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || 'speaking');\n    await loadSessions();\n  } catch (error) {\n    addMessage('error', error.message, 'erreur');\n    setStatus('error');\n    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}\n\nasync function newSession() {\n  const data = await api('/api/session', { method: 'POST', body: '{}' });\n  state.sessionId = data.session_id;\n  localStorage.setItem('agentGuiSession', state.sessionId);\n  $('sessionLabel').textContent = `Session ${state.sessionId}`;\n  $('chatLog').innerHTML = '';\n  $('timeline').innerHTML = '';\n  setStatus('idle');\n  setOfficeCaption('Nouvelle session démarrée.');\n  for (const member of state.crew) {\n    member.mood = 'idle';\n    member.bubble = '';\n  }\n  renderCrewList();\n  updateSelectedAgentCard(currentCrewMember());\n}\n\nfunction resizeOfficeCanvas() {\n  const canvas = $('officeCanvas');\n  const rect = canvas.getBoundingClientRect();\n  const dpr = window.devicePixelRatio || 1;\n  canvas.width = Math.max(1, Math.round(rect.width * dpr));\n  canvas.height = Math.max(1, Math.round(rect.height * dpr));\n  const ctx = canvas.getContext('2d');\n  ctx.setTransform(dpr, 0, 0, dpr, 0, 0);\n  ctx.imageSmoothingEnabled = false;\n}\n\nfunction drawPixelRect(ctx, x, y, w, h, color) {\n  ctx.fillStyle = color;\n  ctx.fillRect(Math.round(x), Math.round(y), Math.round(w), Math.round(h));\n}\n\nfunction drawOfficeBackground(ctx, w, h, now) {\n  const horizon = h * 0.40;\n  const floorTop = h * 0.56;\n\n  const wallGrad = ctx.createLinearGradient(0, 0, 0, floorTop);\n  wallGrad.addColorStop(0, '#17233b');\n  wallGrad.addColorStop(1, '#0e1627');\n  ctx.fillStyle = wallGrad;\n  ctx.fillRect(0, 0, w, floorTop);\n\n  const floorGrad = ctx.createLinearGradient(0, floorTop, 0, h);\n  floorGrad.addColorStop(0, '#0f1726');\n  floorGrad.addColorStop(1, '#060b12');\n  ctx.fillStyle = floorGrad;\n  ctx.fillRect(0, floorTop, w, h - floorTop);\n\n  // windows\n  for (let i = 0; i < 3; i += 1) {\n    const wx = w * (0.18 + i * 0.24);\n    const wy = h * 0.10;\n    const ww = w * 0.16;\n    const wh = h * 0.18;\n    ctx.fillStyle = '#0a1730';\n    ctx.fillRect(wx, wy, ww, wh);\n    ctx.fillStyle = 'rgba(110,231,255,0.09)';\n    ctx.fillRect(wx + 4, wy + 4, ww - 8, wh - 8);\n    ctx.strokeStyle = 'rgba(255,255,255,0.10)';\n    ctx.lineWidth = 2;\n    ctx.strokeRect(wx, wy, ww, wh);\n    ctx.beginPath();\n    ctx.moveTo(wx + ww / 2, wy);\n    ctx.lineTo(wx + ww / 2, wy + wh);\n    ctx.moveTo(wx, wy + wh / 2);\n    ctx.lineTo(wx + ww, wy + wh / 2);\n    ctx.stroke();\n  }\n\n  // stars / city lights\n  for (let i = 0; i < 34; i += 1) {\n    const x = (i * 73 + 19) % w;\n    const y = (i * 37 + 11) % Math.max(1, horizon - 10);\n    ctx.fillStyle = i % 4 === 0 ? '#e4f7ff' : '#8ddcff';\n    ctx.fillRect(x, y, 2, 2);\n  }\n  ctx.fillStyle = 'rgba(8, 18, 39, 0.72)';\n  for (let i = 0; i < 10; i += 1) {\n    const x = i * (w / 10) + ((i % 2) * 10);\n    const bh = 28 + (i % 3) * 18;\n    ctx.fillRect(x, horizon - bh * 0.6, 18 + (i % 2) * 12, bh);\n  }\n\n  // wall trim\n  ctx.fillStyle = '#0d1626';\n  ctx.fillRect(0, floorTop - 14, w, 14);\n\n  // perspective floor grid\n  const vanishingX = w * 0.52;\n  const vanishingY = floorTop - 10;\n  ctx.strokeStyle = 'rgba(161,193,236,0.14)';\n  ctx.lineWidth = 1;\n  for (let i = -8; i <= 8; i += 1) {\n    const t = i / 8;\n    ctx.beginPath();\n    ctx.moveTo(w * 0.5, floorTop);\n    ctx.lineTo(vanishingX + t * w * 0.7, h);\n    ctx.stroke();\n  }\n  for (let i = 0; i <= 10; i += 1) {\n    const y = lerp(floorTop, h, i / 10);\n    ctx.beginPath();\n    ctx.moveTo(0, y);\n    ctx.lineTo(w, y);\n    ctx.stroke();\n  }\n\n  // central desk and workstation\n  const deskW = w * 0.22;\n  const deskH = h * 0.10;\n  const deskX = w * 0.41;\n  const deskY = h * 0.68;\n  ctx.fillStyle = '#2a1f35';\n  ctx.fillRect(deskX, deskY, deskW, deskH);\n  ctx.fillStyle = '#1b2437';\n  ctx.fillRect(deskX + 8, deskY + 8, deskW - 16, deskH - 16);\n  ctx.fillStyle = '#0b1120';\n  ctx.fillRect(deskX + deskW * 0.35, deskY - 40, deskW * 0.30, 26);\n  ctx.fillStyle = '#5fe5ff';\n  ctx.fillRect(deskX + deskW * 0.37, deskY - 36, deskW * 0.26, 18);\n  ctx.fillStyle = '#7f8ea8';\n  ctx.fillRect(deskX + deskW * 0.42, deskY - 12, deskW * 0.16, 18);\n  ctx.fillStyle = '#2f3d56';\n  ctx.fillRect(deskX + deskW * 0.48, deskY + 10, deskW * 0.04, 40);\n  ctx.fillRect(deskX + deskW * 0.12, deskY + 24, deskW * 0.16, 12);\n  ctx.fillRect(deskX + deskW * 0.72, deskY + 24, deskW * 0.16, 12);\n\n  // side furniture\n  ctx.fillStyle = '#1a2438';\n  ctx.fillRect(w * 0.08, h * 0.67, w * 0.12, h * 0.11);\n  ctx.fillStyle = '#22324c';\n  ctx.fillRect(w * 0.09, h * 0.69, w * 0.10, h * 0.07);\n  ctx.fillStyle = '#1e2d41';\n  ctx.fillRect(w * 0.80, h * 0.64, w * 0.10, h * 0.13);\n  ctx.fillStyle = '#5af0a6';\n  ctx.fillRect(w * 0.83, h * 0.60, 14, 26);\n  ctx.fillRect(w * 0.79, h * 0.62, 22, 8);\n\n  // plant\n  const pulse = 0.5 + Math.sin(now / 500) * 0.5;\n  ctx.fillStyle = '#14311f';\n  ctx.fillRect(w * 0.88, h * 0.70, 18, 20);\n  ctx.fillStyle = `rgba(90, 240, 166, ${0.6 + pulse * 0.2})`;\n  ctx.fillRect(w * 0.885, h * 0.66, 8, 18);\n  ctx.fillRect(w * 0.895, h * 0.63, 12, 24);\n  ctx.fillRect(w * 0.902, h * 0.66, 8, 18);\n\n  // wall board\n  ctx.fillStyle = '#111a2b';\n  ctx.fillRect(w * 0.06, h * 0.20, w * 0.18, h * 0.18);\n  ctx.strokeStyle = 'rgba(110,231,255,.22)';\n  ctx.strokeRect(w * 0.06, h * 0.20, w * 0.18, h * 0.18);\n  ctx.fillStyle = 'rgba(110,231,255,.10)';\n  ctx.fillRect(w * 0.08, h * 0.24, w * 0.14, 6);\n  ctx.fillRect(w * 0.08, h * 0.29, w * 0.10, 6);\n}\n\nfunction drawSpeechBubble(ctx, x, y, text, color = '#e8f1ff') {\n  const paddingX = 8;\n  const paddingY = 6;\n  const lines = wrapText(text, 16);\n  const lineHeight = 12;\n  const width = Math.max(...lines.map((line) => line.length), 1) * 7 + paddingX * 2;\n  const height = lines.length * lineHeight + paddingY * 2;\n  const bx = x - width / 2;\n  const by = y - height - 16;\n\n  ctx.fillStyle = 'rgba(5, 10, 18, 0.92)';\n  ctx.fillRect(bx, by, width, height);\n  ctx.strokeStyle = 'rgba(110,231,255,0.35)';\n  ctx.lineWidth = 1;\n  ctx.strokeRect(bx, by, width, height);\n  ctx.fillStyle = color;\n  ctx.font = '11px monospace';\n  lines.forEach((line, index) => {\n    ctx.fillText(line, bx + paddingX, by + paddingY + 10 + index * lineHeight);\n  });\n  ctx.fillStyle = 'rgba(5, 10, 18, 0.92)';\n  ctx.beginPath();\n  ctx.moveTo(x - 5, by + height);\n  ctx.lineTo(x + 2, by + height + 8);\n  ctx.lineTo(x + 8, by + height);\n  ctx.closePath();\n  ctx.fill();\n}\n\nfunction wrapText(text, maxChars) {\n  const words = String(text).split(/\\s+/).filter(Boolean);\n  const lines = [];\n  let current = '';\n  for (const word of words) {\n    const next = current ? `${current} ${word}` : word;\n    if (next.length > maxChars && current) {\n      lines.push(current);\n      current = word;\n    } else {\n      current = next;\n    }\n  }\n  if (current) lines.push(current);\n  return lines.slice(0, 3);\n}\n\nfunction drawLittlePerson(ctx, member, now) {\n  const palette = member.palette;\n  const x = member.x;\n  const y = member.y + member.bob;\n  const pose = member.mood;\n  const blink = now > member.blinkUntil && Math.floor(now / 260) % 14 < 1;\n  const outline = palette.outline;\n\n  ctx.save();\n  ctx.translate(x, y);\n  ctx.scale(member.face || 1, 1);\n\n  // shadow\n  ctx.fillStyle = 'rgba(0,0,0,.35)';\n  ctx.beginPath();\n  ctx.ellipse(0, 2, 12, 4, 0, 0, Math.PI * 2);\n  ctx.fill();\n\n  // backpack / jetpack for the Metal-Slug-like vibe\n  drawPixelRect(ctx, -7, -20, 4, 12, palette.shirtDark);\n  drawPixelRect(ctx, 3, -20, 4, 12, palette.shirtDark);\n\n  // legs / boots\n  const walkPhase = Math.floor(now / 180) % 2;\n  const step = pose === 'thinking' ? 0 : walkPhase;\n  const leftForward = step === 0;\n  const legColor = palette.pants;\n  const bootColor = '#0b0f18';\n  const legY = -10;\n\n  if (pose === 'error') {\n    drawPixelRect(ctx, -6, legY, 5, 9, legColor);\n    drawPixelRect(ctx, 1, legY + 1, 5, 8, legColor);\n  } else {\n    drawPixelRect(ctx, leftForward ? -6 : -5, legY, 5, 10, legColor);\n    drawPixelRect(ctx, leftForward ? 1 : 0, legY, 5, 10, legColor);\n    drawPixelRect(ctx, leftForward ? -7 : -6, legY + 8, 6, 3, bootColor);\n    drawPixelRect(ctx, leftForward ? 0 : 1, legY + 8, 6, 3, bootColor);\n  }\n\n  // torso\n  const torsoY = -25;\n  drawPixelRect(ctx, -7, torsoY, 14, 12, outline);\n  drawPixelRect(ctx, -6, torsoY + 1, 12, 10, palette.shirt);\n  drawPixelRect(ctx, -5, torsoY + 3, 10, 6, palette.shirtDark);\n  drawPixelRect(ctx, -2, torsoY + 1, 4, 3, palette.accent);\n\n  // arms\n  if (pose === 'speaking') {\n    drawPixelRect(ctx, -11, torsoY + 2, 5, 3, outline);\n    drawPixelRect(ctx, -10, torsoY + 3, 4, 2, palette.skin);\n    drawPixelRect(ctx, 6, torsoY, 4, 3, outline);\n    drawPixelRect(ctx, 7, torsoY + 1, 3, 2, palette.skin);\n  } else if (pose === 'thinking' || pose === 'tool') {\n    drawPixelRect(ctx, -11, torsoY + 4, 5, 3, outline);\n    drawPixelRect(ctx, -10, torsoY + 5, 4, 2, palette.skin);\n    drawPixelRect(ctx, 6, torsoY + 2, 5, 3, outline);\n    drawPixelRect(ctx, 7, torsoY + 3, 4, 2, palette.skin);\n  } else if (pose === 'error') {\n    drawPixelRect(ctx, -11, torsoY + 5, 4, 3, outline);\n    drawPixelRect(ctx, 6, torsoY + 5, 4, 3, outline);\n  } else {\n    drawPixelRect(ctx, -11, torsoY + 3, 4, 3, outline);\n    drawPixelRect(ctx, 6, torsoY + 3, 4, 3, outline);\n    drawPixelRect(ctx, -10, torsoY + 4, 3, 2, palette.skin);\n    drawPixelRect(ctx, 7, torsoY + 4, 3, 2, palette.skin);\n  }\n\n  // head + hair\n  drawPixelRect(ctx, -7, -36, 14, 11, outline);\n  drawPixelRect(ctx, -6, -35, 12, 9, palette.skin);\n  drawPixelRect(ctx, -7, -38, 14, 4, palette.hair);\n  drawPixelRect(ctx, -6, -37, 12, 2, palette.hair);\n  drawPixelRect(ctx, 2, -34, 3, 3, outline);\n  drawPixelRect(ctx, -4, -34, 2, 2, blink ? palette.hair : palette.light);\n  drawPixelRect(ctx, 1, -34, 2, 2, blink ? palette.hair : palette.light);\n  drawPixelRect(ctx, -3, -30, 6, 2, outline);\n\n  if (pose === 'error') {\n    drawPixelRect(ctx, -4, -33, 2, 2, palette.accent);\n    drawPixelRect(ctx, 2, -33, 2, 2, palette.accent);\n    drawPixelRect(ctx, -3, -31, 6, 1, palette.accent);\n  }\n\n  if (pose === 'tool') {\n    drawPixelRect(ctx, 9, -20, 5, 2, palette.accent);\n    drawPixelRect(ctx, 12, -21, 2, 6, palette.accent);\n  }\n\n  if (member.active) {\n    ctx.strokeStyle = 'rgba(110,231,255,.65)';\n    ctx.lineWidth = 2;\n    ctx.beginPath();\n    ctx.arc(0, -18, 16, 0, Math.PI * 2);\n    ctx.stroke();\n  }\n\n  if (member.bubble && now < member.bubbleUntil) {\n    ctx.restore();\n    drawSpeechBubble(ctx, member.x, member.y - 18 + member.bob, member.bubble);\n    ctx.save();\n    ctx.translate(x, y);\n    ctx.scale(member.face || 1, 1);\n  }\n\n  ctx.fillStyle = member.selected ? '#6ee7ff' : '#8aa1c0';\n  ctx.font = '10px monospace';\n  ctx.fillText(member.name, -18, 22);\n\n  ctx.restore();\n}\n\nfunction updateCrew(now) {\n  const w = $('officeCanvas').clientWidth || OFFICE.baseWidth;\n  const h = $('officeCanvas').clientHeight || OFFICE.baseHeight;\n  const floorTop = h * 0.56;\n  const deskX = w * 0.41;\n  const deskY = h * 0.68;\n  const deskW = w * 0.22;\n  const targetZone = {\n    x: deskX + deskW * 0.50,\n    y: deskY - 18,\n  };\n\n  for (const member of state.crew) {\n    member.selected = member.id === state.selectedAgentId;\n    member.active = member.selected;\n    member.bob = Math.sin(now / 260 + hashString(member.id) * 0.001) * 1.1;\n\n    if (member.mood === 'speaking') {\n      member.tx = targetZone.x;\n      member.ty = targetZone.y;\n      member.face = member.x <= targetZone.x ? 1 : -1;\n    }\n\n    if (member.mood === 'thinking' || member.mood === 'tool') {\n      member.tx = targetZone.x - 40 + (hashString(member.id) % 80);\n      member.ty = targetZone.y + 10 + ((hashString(member.id) >> 4) % 30);\n      member.face = member.x <= member.tx ? 1 : -1;\n    }\n\n    if (member.mood === 'idle' && now > member.wanderUntil) {\n      const wanderPoints = [\n        { x: w * 0.16, y: floorTop + 55 },\n        { x: w * 0.30, y: floorTop + 35 },\n        { x: w * 0.60, y: floorTop + 40 },\n        { x: w * 0.78, y: floorTop + 50 },\n        { x: w * 0.50, y: floorTop + 60 },\n      ];\n      const point = wanderPoints[(hashString(member.id) + Math.floor(now / 5000)) % wanderPoints.length];\n      member.tx = point.x;\n      member.ty = point.y;\n      member.wanderUntil = now + 3500 + (hashString(member.id) % 2500);\n      member.face = member.x <= member.tx ? 1 : -1;\n    }\n\n    const dx = member.tx - member.x;\n    const dy = member.ty - member.y;\n    const dist = Math.hypot(dx, dy);\n\n    if (member.mood === 'error') {\n      member.x += Math.sin(now / 60 + hashString(member.id)) * 0.12;\n      member.y += Math.cos(now / 54 + hashString(member.id)) * 0.10;\n      continue;\n    }\n\n    if (dist > 1.5) {\n      const speed = member.speed * (member.mood === 'thinking' ? 0.6 : member.mood === 'tool' ? 0.85 : 1);\n      const step = Math.min(speed * 0.016, dist);\n      member.x += (dx / dist) * step;\n      member.y += (dy / dist) * step;\n      member.face = dx >= 0 ? 1 : -1;\n      member.walkFrame = Math.floor(now / 170) % 2;\n    } else {\n      member.x = member.tx;\n      member.y = member.ty;\n      member.walkFrame = 0;\n      if (member.mood === 'thinking' || member.mood === 'tool') {\n        member.mood = 'idle';\n        member.bubble = '';\n      }\n      if (member.mood === 'speaking' && now - member.lastTalkAt > 2400) {\n        member.mood = 'idle';\n        member.bubble = '';\n      }\n    }\n  }\n}\n\nfunction drawOffice(now) {\n  const canvas = $('officeCanvas');\n  const ctx = canvas.getContext('2d');\n  const w = canvas.clientWidth || OFFICE.baseWidth;\n  const h = canvas.clientHeight || OFFICE.baseHeight;\n\n  ctx.save();\n  ctx.setTransform(1, 0, 0, 1, 0, 0);\n  ctx.clearRect(0, 0, canvas.width, canvas.height);\n  ctx.scale((canvas.width / canvas.clientWidth) || 1, (canvas.height / canvas.clientHeight) || 1);\n  ctx.imageSmoothingEnabled = false;\n\n  drawOfficeBackground(ctx, w, h, now);\n  updateCrew(now);\n\n  // desk chair glow & monitors for ambience\n  const glow = 0.35 + Math.sin(now / 420) * 0.08;\n  ctx.fillStyle = `rgba(110,231,255,${glow})`;\n  ctx.fillRect(w * 0.47, h * 0.54, 52, 4);\n\n  for (const member of state.crew) {\n    drawLittlePerson(ctx, member, now);\n  }\n\n  ctx.restore();\n}\n\nfunction animate(now) {\n  drawOffice(now);\n  requestAnimationFrame(animate);\n}\n\nfunction initEvents() {\n  $('chatForm').addEventListener('submit', sendMessage);\n  $('newSessionBtn').addEventListener('click', newSession);\n  $('reloadBtn').addEventListener('click', loadAgents);\n  $('agentSelect').addEventListener('change', () => {\n    setSelectedAgent($('agentSelect').value);\n  });\n  $('messageInput').addEventListener('keydown', (event) => {\n    if (event.ctrlKey && event.key === 'Enter') sendMessage(event);\n  });\n  window.addEventListener('resize', resizeOfficeCanvas);\n}\n\nasync function init() {\n  initEvents();\n  resizeOfficeCanvas();\n  await loadAgents();\n  if (!state.selectedAgentId && state.crew[0]) {\n    setSelectedAgent(state.crew[0].id);\n  } else if (state.selectedAgentId) {\n    setSelectedAgent(state.selectedAgentId);\n  }\n  if (state.sessionId) await openSession(state.sessionId);\n  else await newSession();\n  await loadSessions();\n  setOfficeCaption('Le bureau est prêt.');\n  requestAnimationFrame(animate);\n}\n\ninit().catch(async (error) => {\n  console.error(error);\n  setStatus('error');\n  setOfficeCaption('Erreur de démarrage de l’interface.');\n});\n","numLines":844,"startLine":1,"totalLines":844}},"sourceToolAssistantUUID":"e07678c7-3aba-4bf5-91bc-b38647f10d37","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"8fcaf955-a3a1-4437-8e7a-446825f01c93","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01PN7mSzRcgQLDsvZ5vHqP8f","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Vxk6HeqLMWL3eSVH5FJHSd","name":"Bash","input":{"command":"cat /mnt/DATA/WORK/LAM_Audio2Expression/app_lam_audio2exp.py | head -100","description":"Read LAM app to understand inference API"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3310,"cache_read_input_tokens":30655,"output_tokens":159,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3310,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":159,"cache_read_input_tokens":30655,"cache_creation_input_tokens":3310,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3310},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mKDRUDYV4JmChJdkiT","type":"assistant","uuid":"74bcbbb8-6662-4421-a1bd-b380c286d4b3","timestamp":"2026-06-15T21:35:35.703Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"74bcbbb8-6662-4421-a1bd-b380c286d4b3","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Vxk6HeqLMWL3eSVH5FJHSd","type":"tool_result","content":"\"\"\"\nCopyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    https://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\nimport os\nimport base64\n\nimport gradio as gr\nimport argparse\nfrom omegaconf import OmegaConf\nfrom gradio_gaussian_render import gaussian_render\n\nfrom engines.defaults import (\n    default_argument_parser,\n    default_config_parser,\n    default_setup,\n)\nfrom engines.infer import INFER\nfrom pathlib import Path\n\ntry:\n    import spaces\nexcept:\n    pass\n\nimport patoolib\n\nh5_rendering = True\n\n\ndef assert_input_image(input_image,input_zip_textbox):\n    if(os.path.exists(input_zip_textbox)):\n        return\n    if input_image is None:\n        raise gr.Error('No image selected or uploaded!')\n\n\ndef prepare_working_dir():\n    import tempfile\n    working_dir = tempfile.TemporaryDirectory()\n    return working_dir\n\ndef get_image_base64(path):\n    with open(path, 'rb') as image_file:\n        encoded_string = base64.b64encode(image_file.read()).decode()\n    return f'data:image/png;base64,{encoded_string}'\n\n\ndef do_render():\n    print('WebGL rendering ....')\n    return\n\ndef audio_loading():\n    print(\"Audio loading ....\")\n    return \"None\"\n\ndef parse_configs():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--config\", type=str)\n    parser.add_argument(\"--infer\", type=str)\n    args, unknown = parser.parse_known_args()\n\n    cfg = OmegaConf.create()\n    cli_cfg = OmegaConf.from_cli(unknown)\n\n    # parse from ENV\n    if os.environ.get(\"APP_INFER\") is not None:\n        args.infer = os.environ.get(\"APP_INFER\")\n    if os.environ.get(\"APP_MODEL_NAME\") is not None:\n        cli_cfg.model_name = os.environ.get(\"APP_MODEL_NAME\")\n\n    args.config = args.infer if args.config is None else args.config\n\n    if args.config is not None:\n        cfg_train = OmegaConf.load(args.config)\n        cfg.source_size = cfg_train.dataset.source_image_res\n        try:\n            cfg.src_head_size = cfg_train.dataset.src_head_size\n        except:\n            cfg.src_head_size = 112\n        cfg.render_size = cfg_train.dataset.render_image.high\n        _relative_path = os.path.join(\n            cfg_train.experiment.parent,\n            cfg_train.experiment.child,\n            os.path.basename(cli_cfg.model_name).split(\"_\")[-1],\n        )\n\n        cfg.save_tmp_dump = os.path.join(\"exps\", \"save_tmp\", _relative_path)\n        cfg.image_dump = os.path.join(\"exps\", \"images\", _relative_path)","is_error":false}]},"uuid":"936bbf84-8dac-47bf-a4bb-6af8aa2d301d","timestamp":"2026-06-15T21:35:35.776Z","toolUseResult":{"stdout":"\"\"\"\nCopyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    https://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\nimport os\nimport base64\n\nimport gradio as gr\nimport argparse\nfrom omegaconf import OmegaConf\nfrom gradio_gaussian_render import gaussian_render\n\nfrom engines.defaults import (\n    default_argument_parser,\n    default_config_parser,\n    default_setup,\n)\nfrom engines.infer import INFER\nfrom pathlib import Path\n\ntry:\n    import spaces\nexcept:\n    pass\n\nimport patoolib\n\nh5_rendering = True\n\n\ndef assert_input_image(input_image,input_zip_textbox):\n    if(os.path.exists(input_zip_textbox)):\n        return\n    if input_image is None:\n        raise gr.Error('No image selected or uploaded!')\n\n\ndef prepare_working_dir():\n    import tempfile\n    working_dir = tempfile.TemporaryDirectory()\n    return working_dir\n\ndef get_image_base64(path):\n    with open(path, 'rb') as image_file:\n        encoded_string = base64.b64encode(image_file.read()).decode()\n    return f'data:image/png;base64,{encoded_string}'\n\n\ndef do_render():\n    print('WebGL rendering ....')\n    return\n\ndef audio_loading():\n    print(\"Audio loading ....\")\n    return \"None\"\n\ndef parse_configs():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--config\", type=str)\n    parser.add_argument(\"--infer\", type=str)\n    args, unknown = parser.parse_known_args()\n\n    cfg = OmegaConf.create()\n    cli_cfg = OmegaConf.from_cli(unknown)\n\n    # parse from ENV\n    if os.environ.get(\"APP_INFER\") is not None:\n        args.infer = os.environ.get(\"APP_INFER\")\n    if os.environ.get(\"APP_MODEL_NAME\") is not None:\n        cli_cfg.model_name = os.environ.get(\"APP_MODEL_NAME\")\n\n    args.config = args.infer if args.config is None else args.config\n\n    if args.config is not None:\n        cfg_train = OmegaConf.load(args.config)\n        cfg.source_size = cfg_train.dataset.source_image_res\n        try:\n            cfg.src_head_size = cfg_train.dataset.src_head_size\n        except:\n            cfg.src_head_size = 112\n        cfg.render_size = cfg_train.dataset.render_image.high\n        _relative_path = os.path.join(\n            cfg_train.experiment.parent,\n            cfg_train.experiment.child,\n            os.path.basename(cli_cfg.model_name).split(\"_\")[-1],\n        )\n\n        cfg.save_tmp_dump = os.path.join(\"exps\", \"save_tmp\", _relative_path)\n        cfg.image_dump = os.path.join(\"exps\", \"images\", _relative_path)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"74bcbbb8-6662-4421-a1bd-b380c286d4b3","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"parentUuid":"c9c33264-07c9-44ee-bff9-2c2eefe1240a","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01FguSYtuwunqKCWN4RS9ZtV","type":"message","role":"assistant","content":[{"type":"text","text":"Je vais examiner l'API d'inférence A2E et les options TTS disponibles avant d'implémenter."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":13393,"cache_read_input_tokens":33965,"output_tokens":3248,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":13393,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":3248,"cache_read_input_tokens":33965,"cache_creation_input_tokens":13393,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":13393},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mKUsPz5MvHVUzPceXR","type":"assistant","uuid":"3b94d2e2-9f44-4f41-8e3f-af8588263310","timestamp":"2026-06-15T21:36:34.837Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"3b94d2e2-9f44-4f41-8e3f-af8588263310","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01FguSYtuwunqKCWN4RS9ZtV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GivFMiSrF8ghqJzbGcX4L9","name":"Read","input":{"file_path":"/mnt/DATA/WORK/LAM_Audio2Expression/inference_streaming_audio.py"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":13393,"cache_read_input_tokens":33965,"output_tokens":3248,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":13393,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":3248,"cache_read_input_tokens":33965,"cache_creation_input_tokens":13393,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":13393},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mKUsPz5MvHVUzPceXR","type":"assistant","uuid":"7d099be6-6ff4-492b-8fb2-d8dfd7012ead","timestamp":"2026-06-15T21:36:35.596Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"7d099be6-6ff4-492b-8fb2-d8dfd7012ead","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GivFMiSrF8ghqJzbGcX4L9","type":"tool_result","content":"1\t\"\"\"\n2\t# Copyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved.\n3\t\n4\tLicensed under the Apache License, Version 2.0 (the \"License\");\n5\tyou may not use this file except in compliance with the License.\n6\tYou may obtain a copy of the License at\n7\t\n8\t    https://www.apache.org/licenses/LICENSE-2.0\n9\t\n10\tUnless required by applicable law or agreed to in writing, software\n11\tdistributed under the License is distributed on an \"AS IS\" BASIS,\n12\tWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n13\tSee the License for the specific language governing permissions and\n14\tlimitations under the License.\n15\t\n16\t\"\"\"\n17\t\n18\timport numpy as np\n19\t\n20\tfrom engines.defaults import (\n21\t    default_argument_parser,\n22\t    default_config_parser,\n23\t    default_setup,\n24\t)\n25\tfrom engines.infer import INFER\n26\timport librosa\n27\tfrom tqdm import tqdm\n28\timport time\n29\t\n30\t\n31\tdef export_json(bs_array, json_path):\n32\t    from models.utils import export_blendshape_animation, ARKitBlendShape\n33\t    export_blendshape_animation(bs_array, json_path, ARKitBlendShape, fps=30.0)\n34\t\n35\tif __name__ == '__main__':\n36\t    args = default_argument_parser().parse_args()\n37\t    args.config_file = 'configs/lam_audio2exp_config_streaming.py'\n38\t    cfg = default_config_parser(args.config_file, args.options)\n39\t\n40\t\n41\t    cfg = default_setup(cfg)\n42\t    infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\n43\t    infer.model.eval()\n44\t\n45\t    audio, sample_rate = librosa.load(cfg.audio_input, sr=16000)\n46\t    context = None\n47\t    input_num = audio.shape[0]//16000+1\n48\t    gap = 16000\n49\t    all_exp = []\n50\t    for i in tqdm(range(input_num)):\n51\t\n52\t        start = time.time()\n53\t        output, context = infer.infer_streaming_audio(audio[i*gap:(i+1)*gap], sample_rate, context)\n54\t        end = time.time()\n55\t        print('Inference time {}'.format(end - start))\n56\t        all_exp.append(output['expression'])\n57\t\n58\t    all_exp = np.concatenate(all_exp,axis=0)\n59\t\n60\t    export_json(all_exp, cfg.save_json_path)"}]},"uuid":"0ce3f451-74de-4450-8888-ab72af915b62","timestamp":"2026-06-15T21:36:35.618Z","toolUseResult":{"type":"text","file":{"filePath":"/mnt/DATA/WORK/LAM_Audio2Expression/inference_streaming_audio.py","content":"\"\"\"\n# Copyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    https://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n\"\"\"\n\nimport numpy as np\n\nfrom engines.defaults import (\n    default_argument_parser,\n    default_config_parser,\n    default_setup,\n)\nfrom engines.infer import INFER\nimport librosa\nfrom tqdm import tqdm\nimport time\n\n\ndef export_json(bs_array, json_path):\n    from models.utils import export_blendshape_animation, ARKitBlendShape\n    export_blendshape_animation(bs_array, json_path, ARKitBlendShape, fps=30.0)\n\nif __name__ == '__main__':\n    args = default_argument_parser().parse_args()\n    args.config_file = 'configs/lam_audio2exp_config_streaming.py'\n    cfg = default_config_parser(args.config_file, args.options)\n\n\n    cfg = default_setup(cfg)\n    infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\n    infer.model.eval()\n\n    audio, sample_rate = librosa.load(cfg.audio_input, sr=16000)\n    context = None\n    input_num = audio.shape[0]//16000+1\n    gap = 16000\n    all_exp = []\n    for i in tqdm(range(input_num)):\n\n        start = time.time()\n        output, context = infer.infer_streaming_audio(audio[i*gap:(i+1)*gap], sample_rate, context)\n        end = time.time()\n        print('Inference time {}'.format(end - start))\n        all_exp.append(output['expression'])\n\n    all_exp = np.concatenate(all_exp,axis=0)\n\n    export_json(all_exp, cfg.save_json_path)","numLines":60,"startLine":1,"totalLines":60}},"sourceToolAssistantUUID":"7d099be6-6ff4-492b-8fb2-d8dfd7012ead","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"parentUuid":"8eb498ab-4b1a-481b-92dc-d864aab02fc5","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AjQAaEMczzv5hpbrVpPKJF","type":"tool_result","content":"type frames[0]: <class 'dict'>\n{\"weights\": [0.0009596033487468958, 0.0009596033487468958, 0.001191797899082303, 0.0008496011141687632, 0.0008496011141687632, 0.006510531529784203, 0.0020005949772894382, 0.0020005949772894382, 0.360\nmetadata: {'fps': 30.0, 'frame_count': 240, 'blendshape_names': ['browDownLeft', 'browDownRight', 'browInnerUp', 'browOuterUpLeft', 'browOuterUpRight', 'cheekPuff', 'cheekSquintLeft', 'cheekSquintRight', 'eyeBlinkLeft', 'eyeBlinkRight', 'eyeLookDownLeft', 'eyeLookDownRight', 'eyeLookInLeft', 'eyeLookInRight', 'eyeLookOutLeft', 'eyeLookOutRight', 'eyeLookUpLeft', 'eyeLookUpRight', 'eyeSquintLeft', 'eyeSquintRight', 'eyeWideLeft', 'eyeWideRight', 'jawForward', 'jawLeft', 'jawOpen', 'jawRight', 'mouthClose', 'mouthDimpleLeft', 'mouthDimpleRight', 'mouthFrownLeft', 'mouthFrownRight', 'mouthFunnel', 'mouthLeft', 'mouthLowerDownLeft', 'mouthLowerDownRight', 'mouthPressLeft', 'mouthPressRight', 'mouthPucker', 'mouthRight', 'mouthRollLower', 'mouthRollUpper', 'mouthShrugLower', 'mouthShrugUpper', 'mouthSmileLeft', 'mouthSmileRight', 'mouthStretchLeft', 'mouthStretchRight', 'mouthUpperUpLeft', 'mouthUpperUpRight', 'noseSneerLeft', 'noseSneerRight', 'tongueOut']}","is_error":false}]},"uuid":"96bf034f-fb71-402c-a7b0-4baf1dea7830","timestamp":"2026-06-15T21:36:54.306Z","toolUseResult":{"stdout":"type frames[0]: <class 'dict'>\n{\"weights\": [0.0009596033487468958, 0.0009596033487468958, 0.001191797899082303, 0.0008496011141687632, 0.0008496011141687632, 0.006510531529784203, 0.0020005949772894382, 0.0020005949772894382, 0.360\nmetadata: {'fps': 30.0, 'frame_count': 240, 'blendshape_names': ['browDownLeft', 'browDownRight', 'browInnerUp', 'browOuterUpLeft', 'browOuterUpRight', 'cheekPuff', 'cheekSquintLeft', 'cheekSquintRight', 'eyeBlinkLeft', 'eyeBlinkRight', 'eyeLookDownLeft', 'eyeLookDownRight', 'eyeLookInLeft', 'eyeLookInRight', 'eyeLookOutLeft', 'eyeLookOutRight', 'eyeLookUpLeft', 'eyeLookUpRight', 'eyeSquintLeft', 'eyeSquintRight', 'eyeWideLeft', 'eyeWideRight', 'jawForward', 'jawLeft', 'jawOpen', 'jawRight', 'mouthClose', 'mouthDimpleLeft', 'mouthDimpleRight', 'mouthFrownLeft', 'mouthFrownRight', 'mouthFunnel', 'mouthLeft', 'mouthLowerDownLeft', 'mouthLowerDownRight', 'mouthPressLeft', 'mouthPressRight', 'mouthPucker', 'mouthRight', 'mouthRollLower', 'mouthRollUpper', 'mouthShrugLower', 'mouthShrugUpper', 'mouthSmileLeft', 'mouthSmileRight', 'mouthStretchLeft', 'mouthStretchRight', 'mouthUpperUpLeft', 'mouthUpperUpRight', 'noseSneerLeft', 'noseSneerRight', 'tongueOut']}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"8eb498ab-4b1a-481b-92dc-d864aab02fc5","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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xCOQZRb3zvKpR2dmu3wZ4VV+DEMl4wg0X2A25H/O9iE7luxRH8OxpjDBmEzcd1vSZb0qcqjGAXOYJ60F45EN4PM34KFWcHmxeJPrkzb8Rny80ehVW5O1bJx640AU32J+Dy3xGyN3LIrITz0IWnqDDfYkrMSNIDFZpaylI4QpTFiv5tv4jZfk5B4RdVBrAxIN56TaqBGKuGEueqPWRuhVZV7iE7YUBLRXhBew56nVpC64e6aevVcTfNheW5Z0uP+JPLplxdq0hkelGspEYJhndaP1Aq2ky76mtX7S3O8edxdWu1yKr4UthnRHq5cSMcEfGpl5FbBu/UJQj/aKH1GiF+44uT+zPtIrxa3DDQj41Ts3ZRSy3LJrRBxZjW1wu0lo8oegBmwRQ/HG8mmLBAWvVmOd1S6D4ZrDs1UC8ByyZJoDNPMJAoKKtOGQNdDJL9Pf1VHoETKYS7DkPqq2AA7JTm9yJhgW6SDRpwpGyb9F0l8WY/gyOBGW6whafHtQNhEvm2Q5GUjaxfdkDCcDEV5OqvFiC5YsxkLICdVXm0xg45rfwPNLVGw8c80IlJ/LB7Eyj0sZMvKB1Pc7u9vZExDoPqL5HcEQXVWpKQIeiATpTdaVIL+x9ENnm0ImRjPnIh/JLW3qlXtD8uBTYdJa/mRZJS/RfkzRTG/rhoOx9D9Q868n6LaTS9z0rl8nvfnBcIc2Pau4s4sg5Tlra2io/gGj8iOKaKvb4HEX9HZ/DEn8p5wgeT700TgHaaC+9B+ITF+WSXUF54E5T2Tz+Dwjo8yOTqkbfR++O1pijAcqLZp8EkDpqVvDFSb4Rfb7AMKGCgIQuG6TqUkdHajBPVMEwhrgjqhFMRDSdNC60PKz/Qs9hSMFWwSEe6F73kl26VP1GexXDyHT0gTDN156EqKO4WFFowq2rZD5WPGcN6GBfU6DgCo4h0YIED/lL72VglyzEAlM4MbFgFy5sH2gtWwaF8g6nlwZDub2j5GXqeMASyldetAXOhGDK/qJ6Je/YpZecWEPIPRxfZ5EL5f3GSjToYIy7f3JNDXQHj12/A240GkNOwERz0Z43kqTMIgdUJEbOxlE92Dv4CVHh35E10Ud2YADVuQnvWNuu86h6x9tbMOOsmhjqK9qSj/cOGgEVRKU2gv2dcEkIySMbpcdl4ApBPQmRypMHbaxZPjwuFZGQG2XNkvk90XuG0NWAosc39OfQmjTLJwls0yzplK13/GugYP4soWu3uwzJRYVXne4ytou9UZr1WKpa6g7frkRyytelriHSxxlDbyl7dol8YhUhqIeQRBEiCrPrnx5cNpldR3mkMH+X4wZ2ng9dGFCnAinjEsQZ5e5PinHN2dRY3//BAj8yOaUcd1CjR+3uTefOfnMaC+MBGYDZ9cJExmvZzhAZXV6otkP7+o9SYAgHRQjNWD3biA8kxS4gMYh3va+iZZV94nT/UEN40OaijvJfpMlGv+SBA4ElO9QUr/sablp+jCE6iYnvBjAMsdsZxKDlL9s6h/WKbOP2WIbi2uK4i+rh6YPIFDJ0QfSF62lsQCS3MCvTA2/Kcd8P5XwkAydkbW9auaDrVJCdd7GbGg6eCIWfYjN2DTDAr5Qcd5+axFqf4smoSRnBqBprBIlPeI8vT+O2Hi+tyxjSjpYGNlUiKK96KgCW3WbXlpVIvOBzUjmn0aP40or0z6MAUWNdB6qVLffcNwDPNs9MVdLWgEe1evDBUfdJLD9h9vCz5ByqkRfSUfKpqnFCj8/tSBqXJqkdu7bMvVL/+NYklEyo+lHT0zO3A2TJY0s0ulG62mj77RUYCW27Y7VveniDvhn0dq7xNUBpb+WfUpCp7eXXYE/OJFiEO0CjaK9HPxo4QWvmM8FsSBWpYW7XzGudd8yrqpR0nEq0elZ3FL2C4m1JXplpbsPzd7ufdn77UW4PXWZTEJpS9D/7ZZgsPY3cUSQvUtykzvgRSM8kYwCeNvwjfDB3zDLsSUE5d51mgP1GxM8o2nSHs2x3BNUnBDEBpT0IR+ck9Ge+2r4YSjZ9/CMaow+RfwPhnnoMqpKHH7zZdpflc/EQd+IkAAa28wcoB1ieJVln+X3eciyUJC3AQ37HD1k0JJh+TzzVL3ZSqh38P0YtwOfL+uNZlxzacIM+mmLmwm5bsVWrwKz8xR9mzsCFutzthXcIgUen5MzqppM4YTLP7RG0kK+CX7w9WvMZALzFRVCdLKlMGqp6cBRy9lxakw18DkUpRjMuz3qqtOqz2LNL43yAVdN4H5W/blaCCKsPxsff648KXn8tvGiIgTwfey2Q/JNBcw5eEIJoj/IKfMKkCUKsRMsSBLOi0g9UhXR2BBY4QIXLGon+LsRewEFRdx6bT+bEgmLMbuIB/A4UzFEY1YI+MB0Ou7CeckXRJqI8ABcr5Gu0SPeRoqoXEVWKy+O18z1jz80x6nAzyRDH/dDlP/tN+8v/CTU99PQs2FKKYB7NY/KGOL608XwJsBwRXpUuY8bREVhtQP+vOSKrvNIIFZBXWDLADMuu/QJbj/qudZ7Dr7+LMaGT+bcC3G9mYx14FbQlfrM8Z/IBRn6c50vnzvg8i5FiHVBwK5QTiZUfMczf+IHC5B79hjlSg+MiXpqkHl2MkfIQeMgHfpaK0fXqjH6C92R7XNJivCn4/WvLYPAO8DU9Z21xp9DGCEoVQPOzpdb5+zbeBFGONMYuR7aNTj5loYUAbBcLr9nHVf2WOEalYY8qrsYLy1nr2B5hNoTnqWGtHd8sg0t2MSv3BigJZ1QYe5y+DSWRxmpC97pOfNSo/l3Q6Y5C0MoXs5CEa5SKCAJ80BEvjLR92QgfwWZLfQV08CW9AH44m8MMkoNPUmSnrQqsOfUzadTCLFbDZMiRsw3ALJVu7eve0Got6eQl70hq52M2cq5ZnrEIUrvXzb7zN8axbhDQnjvjWU2fYLC28vSvpFT5NbPUhiNO2i+07QIPZhNWv3oqd1az8VX33NOCasmYlXkMB1KD8LbEU7nyTqf978oi+ztxYT1lZOoYebT1vmlMnh/xcznKOBQ2rSh0xc0WgmM7rdVKQe5ER+eWQeDNU2aONFPlCrfPzRXXY6DbKAu6poN/eQPurETcMs2XpQdmhVAyu5TVpz4y/jGbapKvsGOJz0wG7/Mu7cfn3mggxwFwjHAEVd+eUt4eCtwYgqJrbW1tYzh0TMoLnMzbvYuXtcO7xJrjjDnVhASRE0weLZYxctwUm0qw9VHXiDhYy05aNuUwIWur8Yy5ei35YnCoyyz80UNBcMlvXgGnVVGnR88G1aKrFAeJP7Ibfy9O+1Hb2OxuRvXf4YaTNWyeI1c47UcIctaEAKykF9+xEy252RrIDx1C3gd79umzJsD883owGRZJGJ/KRwzvXXXdvHwR52X7eAto9GPC6h447OvzPw40LA/Bu1aDEf/oAMd1XsXZfJdpTZJNHE3vm4WeknT6MLmJ8DrL1jzFARYvYescADORCJHHLGpagHrzHJ31CY09X8zAqPq0JWXzbcSnhZ0L/LdEb5KmDwISOFhy5zV7vefFmh0977YOimfloofYIUi6vxKlaEtzgvx19KSPwHzR/vjcKZp62tR5eekUdLhOm41bKZ3iuRgy2To9MgA2OCyBqAtUz3Xn1lLbRtaIx8uFnMlfPGQyECxnG79wumRYNfS6DNKI5r1o1YMw40GQsgupN0X4EIhWrZVh7SrQfnzzWzjYp3xyqtpZqm4/RjxDKTRxgA59jAXdPonU7Si9II6lnqDXCunW8GmPGzUzMWlDUEvbaVr3ZISlq0bQyK3Bix4I9+IfauNPQJh3avXYiVJaRS4VcXRZxUKoD1IoOTIppe6ezlA7ccs2WPlBOQMbAPLQprGKJHm7h2O5/PWWHncllp7p9NIReI0WwdFoaEUUvagwqsxx7NYloGa+Ty36247rS0lG7CFCtdhKlbBX31DpE5jtdxz+X1lOtJuINnqDvD3ILaBF+E5TKULST+b3FL09emPRHoYV9Qnq2b4oxaZTMZuJ4GKW8ovDLgNJoslVmS4Sqzl1BVPvr4xcIlsj8ZuosT70nhsbGrCu2rSmLZ1jfRuxvE1fXzxlyPXDSW86wUfb+giF9fDduajBboKzi0uO+JRt2DhnJUUYh8ow1/QVaG/jFjRrmell3ATSRgInY684XvwjrbleUBCU9wPkVvzPP7Dt57P76sUQCJRpPx2YVOZ6gVxyOZvoM/K7nCdHRgwhKQGt2Gd0BpQzdxUTXUnOVSN9qqKj87MF6X3IYlfaq4H2G85oprjA8E6sBdszHqJv2K37AVcIXjgTpgZ9qfJCJo2mBiOl00RVRhIlvP40oKRO06vSjWDfoAKAwoot6ibnMY3UsLRH5HGbXAJ7D4mu5GLodN+tvUoUu9luHJSr7864ErD3vjd2EF9142wL/Z4UeZCdyM85C8bpqOFZonYhQ01Z79qZBDqlMuJb+cluSrT/3qiK29q8eShkhi0KqtXNUKEJcRfMEPH6BN0bm6BD5v0S1Qa8/IqXh83Lysnu8tzAmyFMqaeOhmwq6eQbOeYrT19VzbnMlyg/IGoAjAs0oKDR8awcEIOXbHrO9LZy7X0Fgv0ucnsyHlO7aIeFQJB7vTTdK7VzmEIEy76fMRfvdRgxgskvMTtSnEDQPvj7wTlDvNLR0p1tqN5o8PWtItmE3faIA86svfF2jOYfqXr8yqZisB7mJuDbJIbmCYQeKhEC+PWKACdSbvQTvmh+NkCrvWlsgb35fUOk+3ntH/mXbknSFPIRyFc5Xj7Vm3EYTR9vAnVrQfuvExyldtMS9/uu4fZoJynhgNz6H6x4a84IDoOHi1eKnTpq6NJ2P/rrzWRiZOLeXiXYaU4Y9eYkH4vPHOZv3BeBDd88gJSpOXLq4zYK9pUOErZqDlsl73W33y/Tg/27LWbmIBM6wKNw/qIyAVAWydxE9VdbTE0fcXpZ+uY+D2PPHhrkvhSWmtyfKN+4tdUG9cG/XxQfAHdX9O/rtD8h5EB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{"parentUuid":"6a2d6f67-2b48-4b4d-bbda-26811c2872fc","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01R7i9Y9uNBgPSznHe2H7k8v","type":"tool_result","content":"weight = 'pretrained_models/lam_audio2exp_streaming.tar'  # path to model weight\nex_vol = True # extract\naudio_input = './assets/sample_audio/BarackObama_english.wav'\nsave_json_path = 'bsData.json'\n\naudio_sr = 16000\nfps = 30.0\n\nmovement_smooth = False\nbrow_movement = False\nid_idx = 0\n\nresume = False  # whether to resume training process\nevaluate = True  # evaluate after each epoch training process\ntest_only = False  # test process\n\nseed = None  # train process will init a random seed and record\nsave_path = \"exp/audio2exp\"\nnum_worker = 16  # total worker in all gpu\nbatch_size = 16  # total batch size in all gpu\nbatch_size_val = None  # auto adapt to bs 1 for each gpu\nbatch_size_test = None  # auto adapt to bs 1 for each gpu\nepoch = 100  # total epoch, data loop = epoch // eval_epoch\neval_epoch = 100  # sche total eval & checkpoint epoch\n\nsync_bn = False\nenable_amp = False\nempty_cache = False\nfind_unused_parameters = False\n\nmix_prob = 0\nparam_dicts = None  # example: param_dicts = [dict(keyword=\"block\", lr_scale=0.1)]\n\n# model settings\nmodel = dict(\n    type=\"DefaultEstimator\",\n    backbone=dict(\n        type=\"Audio2Expression\",\n        pretrained_encoder_type='wav2vec',\n        pretrained_encoder_path='facebook/wav2vec2-base-960h',","is_error":false}]},"uuid":"a62f7774-8266-42cb-85c6-2ef21beb3235","timestamp":"2026-06-15T21:37:41.986Z","toolUseResult":{"stdout":"weight = 'pretrained_models/lam_audio2exp_streaming.tar'  # path to model weight\nex_vol = True # extract\naudio_input = './assets/sample_audio/BarackObama_english.wav'\nsave_json_path = 'bsData.json'\n\naudio_sr = 16000\nfps = 30.0\n\nmovement_smooth = False\nbrow_movement = False\nid_idx = 0\n\nresume = False  # whether to resume training process\nevaluate = True  # evaluate after each epoch training process\ntest_only = False  # test process\n\nseed = None  # train process will init a random seed and record\nsave_path = \"exp/audio2exp\"\nnum_worker = 16  # total worker in all gpu\nbatch_size = 16  # total batch size in all gpu\nbatch_size_val = None  # auto adapt to bs 1 for each gpu\nbatch_size_test = None  # auto adapt to bs 1 for each gpu\nepoch = 100  # total epoch, data loop = epoch // eval_epoch\neval_epoch = 100  # sche total eval & checkpoint epoch\n\nsync_bn = False\nenable_amp = False\nempty_cache = False\nfind_unused_parameters = False\n\nmix_prob = 0\nparam_dicts = None  # example: param_dicts = [dict(keyword=\"block\", lr_scale=0.1)]\n\n# model settings\nmodel = dict(\n    type=\"DefaultEstimator\",\n    backbone=dict(\n        type=\"Audio2Expression\",\n        pretrained_encoder_type='wav2vec',\n        pretrained_encoder_path='facebook/wav2vec2-base-960h',","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"6a2d6f67-2b48-4b4d-bbda-26811c2872fc","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a62f7774-8266-42cb-85c6-2ef21beb3235","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_015txfKDng3qQkfV3w4egz3f","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LB8NpdurhkCu2nsWX8bhuo","name":"Bash","input":{"command":"# Check if skin.glb has morph targets by looking at file size and checking vertex_order\nls -lh /mnt/DATA/WORK/LAM_Audio2Expression/assets/sample_lam/barbara/arkitWithBSData/\nwc -c /mnt/DATA/WORK/LAM_Audio2Expression/assets/sample_lam/barbara/arkitWithBSData/skin.glb","description":"Check skin.glb file size and assets"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":741,"cache_read_input_tokens":52743,"output_tokens":2989,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":741,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":2989,"cache_read_input_tokens":52743,"cache_creation_input_tokens":741,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":741},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mRGatVvzsUXmwitZN3","type":"assistant","uuid":"93630e52-0173-4b1c-ab37-4fecdacda281","timestamp":"2026-06-15T21:37:43.356Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3670,"cache_read_input_tokens":53484,"output_tokens":4138,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3670,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":4138,"cache_read_input_tokens":53484,"cache_creation_input_tokens":3670,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3670},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mUtgsWrEQm3M9fyNVP","type":"assistant","uuid":"d72baad9-d13d-4172-819d-060cccbb537f","timestamp":"2026-06-15T21:38:50.735Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"3c69ed4c-3c78-4cc1-a11d-cd7cc302ee4e","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_0149NVw3MWaccv2YD5UE4WDu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013kGkmBGw8BbXzBTHBLww1k","name":"Bash","input":{"command":"# Find getExpressionData usage to understand data format\ngrep -o '.{100}getExpressionData.\\{100\\}' \\\n  /home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/gradio_gaussian_render/templates/component/index.js \\\n  | head -5\n\n# Also check how the component is initialized in the Gradio app\ngrep -o '.{80}loadAvatar.\\{80\\}' \\\n  /home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/gradio_gaussian_render/templates/component/index.js \\\n  | head -3","description":"Find API call patterns in renderer"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":784,"cache_read_input_tokens":62229,"output_tokens":250,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":784,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":250,"cache_read_input_tokens":62229,"cache_creation_input_tokens":784,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":784},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mayT2gYRzNchMjRgvw","type":"assistant","uuid":"50782f30-2702-41b9-b786-2c182d7974b1","timestamp":"2026-06-15T21:39:09.731Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"50782f30-2702-41b9-b786-2c182d7974b1","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013kGkmBGw8BbXzBTHBLww1k","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"94490002-e27f-45f1-acd3-3d680e5daf44","timestamp":"2026-06-15T21:39:09.811Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"50782f30-2702-41b9-b786-2c182d7974b1","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"94490002-e27f-45f1-acd3-3d680e5daf44","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01S7Jo1bvpwCXubEsPamgKKb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AvtVz5Z2z9W37XjthiYTxu","name":"Bash","input":{"command":"grep -c \"getExpressionData\" /home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/gradio_gaussian_render/templates/component/index.js\n# Show surrounding context of getExpressionData\ngrep -o '.\\{0,120\\}getExpression.\\{0,120\\}' /home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/gradio_gaussian_render/templates/component/index.js | head -5","description":"Analyze getExpressionData context"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":268,"cache_read_input_tokens":63013,"output_tokens":217,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":268,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":217,"cache_read_input_tokens":63013,"cache_creation_input_tokens":268,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":268},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mbGFKmt4GFStZkUKRt","type":"assistant","uuid":"ff104d5c-04b0-45b5-856b-8ff51fae2f6c","timestamp":"2026-06-15T21:39:13.821Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"ff104d5c-04b0-45b5-856b-8ff51fae2f6c","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AvtVz5Z2z9W37XjthiYTxu","type":"tool_result","content":"3\nugrep: error: error at position 91\nn\\x80-\\xbf][\\x80-\\xbf]*){0,120}\n  exceeds complexity limits___/","is_error":false}]},"uuid":"30761505-94d5-4250-8006-0552907fb950","timestamp":"2026-06-15T21:40:26.429Z","toolUseResult":{"stdout":"3\nugrep: error: error at position 91\nn\\x80-\\xbf][\\x80-\\xbf]*){0,120}\n  exceeds complexity limits___/","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"ff104d5c-04b0-45b5-856b-8ff51fae2f6c","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"30761505-94d5-4250-8006-0552907fb950","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01LNyQKx78jnZWSejw8PyZPL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SMBTSgDrn3pKdtTAsqdLFf","name":"Bash","input":{"command":"grep -a -o \"getExpressionData[^;]*\" /home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/gradio_gaussian_render/templates/component/index.js | head -10","description":"Find getExpressionData pattern"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":275,"cache_read_input_tokens":63281,"output_tokens":352,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":275,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":352,"cache_read_input_tokens":63281,"cache_creation_input_tokens":275,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":275},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mguaHKbLK6s48ph6DQ","type":"assistant","uuid":"2ea75835-2272-4ad7-81f8-276d082b1eeb","timestamp":"2026-06-15T21:40:30.887Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"2ea75835-2272-4ad7-81f8-276d082b1eeb","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SMBTSgDrn3pKdtTAsqdLFf","type":"tool_result","content":"getExpressionData = n == null ? void 0 : n.getExpressionData, ws.useFlame && (_.useFlame = ws.useFlame != \"false\"), console.log(y, A), _.viewer = new vg({\ngetExpressionData && (this.expressionData = this.updateBS(this.getExpressionData())), this.viewer.useFlame == !1) {\ngetExpressionData || (this.getExpressionData = () => this.getArkitFaceFrame(o)), !this.viewer)","is_error":false}]},"uuid":"4c9b8349-4df1-446f-ba2a-a74eb6faff76","timestamp":"2026-06-15T21:40:30.952Z","toolUseResult":{"stdout":"getExpressionData = n == null ? void 0 : n.getExpressionData, ws.useFlame && (_.useFlame = ws.useFlame != \"false\"), console.log(y, A), _.viewer = new vg({\ngetExpressionData && (this.expressionData = this.updateBS(this.getExpressionData())), this.viewer.useFlame == !1) {\ngetExpressionData || (this.getExpressionData = () => this.getArkitFaceFrame(o)), !this.viewer)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"2ea75835-2272-4ad7-81f8-276d082b1eeb","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"4c9b8349-4df1-446f-ba2a-a74eb6faff76","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01LNyQKx78jnZWSejw8PyZPL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XkUTQEZoZDNRSTB4y42Yvg","name":"Bash","input":{"command":"# Try to find how LAM WebRender JS renders - look for init/canvas setup in a different way\n$HOME/miniconda3/bin/python -c \"\ndata = open('/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/gradio_gaussian_render/templates/component/index.js','rb').read().decode('utf-8','ignore')\n# find getExpressionData context\nidx = data.find('getExpressionData')\nif idx>=0:\n    print(repr(data[max(0,idx-200):idx+200]))\n\"","description":"Extract getExpressionData context from JS"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":275,"cache_read_input_tokens":63281,"output_tokens":352,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":275,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":352,"cache_read_input_tokens":63281,"cache_creation_input_tokens":275,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":275},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mguaHKbLK6s48ph6DQ","type":"assistant","uuid":"25347f9a-5315-48c0-b4c3-cd43d5718db8","timestamp":"2026-06-15T21:40:33.403Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"25347f9a-5315-48c0-b4c3-cd43d5718db8","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XkUTQEZoZDNRSTB4y42Yvg","type":"tool_result","content":"' === null || c === void 0 ? void 0 : c.z) || 0;\\n        let A = 16777215;\\n        ws.backgroundColor && (A = parseInt(ws.backgroundColor, 16)), _.getChatState = n == null ? void 0 : n.getChatState, _.getExpressionData = n == null ? void 0 : n.getExpressionData, ws.useFlame && (_.useFlame = ws.useFlame != \"false\"), console.log(y, A), _.viewer = new vg({\\n          rootElement: e,\\n          threejsCa'","is_error":false}]},"uuid":"fd6fb6ee-e542-4417-a11e-d0ec00a58bc4","timestamp":"2026-06-15T21:40:33.481Z","toolUseResult":{"stdout":"' === null || c === void 0 ? void 0 : c.z) || 0;\\n        let A = 16777215;\\n        ws.backgroundColor && (A = parseInt(ws.backgroundColor, 16)), _.getChatState = n == null ? void 0 : n.getChatState, _.getExpressionData = n == null ? void 0 : n.getExpressionData, ws.useFlame && (_.useFlame = ws.useFlame != \"false\"), console.log(y, A), _.viewer = new vg({\\n          rootElement: e,\\n          threejsCa'","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"25347f9a-5315-48c0-b4c3-cd43d5718db8","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"f84c39c6-20ce-4aa4-8aa1-1e250abee7da","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01XsJUVWWK2oMupGecp1VDLs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MbAAwtotu5sux6vjM5r8JL","name":"Bash","input":{"command":"cat /home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/gradio_gaussian_render/gaussian_render.py | head -60","description":"Read gaussian_render Python component"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":693,"cache_read_input_tokens":63556,"output_tokens":679,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":693,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":679,"cache_read_input_tokens":63556,"cache_creation_input_tokens":693,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":693},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mhRpi1hbJcyhX3AWPT","type":"assistant","uuid":"3536b3b0-d8c0-420f-a06f-9de9f302d726","timestamp":"2026-06-15T21:40:44.371Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"3536b3b0-d8c0-420f-a06f-9de9f302d726","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MbAAwtotu5sux6vjM5r8JL","type":"tool_result","content":"\"\"\"gr.GaussianRender() component.\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import (Any, Literal)\nfrom gradio.components.base import Component\nfrom gradio.data_classes import FileData\nfrom gradio.events import Events\n\nclass gaussian_render(Component):\n    \"\"\"\n    Creates an webrtc component link to aliyun-rtc-sdk which can be used to pull or push media stream.\n    \"\"\"\n\n    EVENTS: Events = [\n        'start',\n        'start_render'\n    ]\n\n    data_model = FileData\n\n    def __init__(\n        self,\n        *,\n        container: bool = True,\n        scale: int | None = None,\n        min_width: int = 160,\n        interactive: bool | None = None,\n        visible: bool = True,\n        elem_id: str | None = None,\n        elem_classes: list[str] | str | None = None,\n        render: bool = True,\n        key: int | str | None = None,\n        height: int | str | None = None,\n        width: int | str | None = None,\n        assets: str | None = None,\n        audio_url: str | None = None,\n    ):\n        \"\"\"\n        Parameters:\n            container: If True, will place the component in a container - providing some extra padding around the border.\n            scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.\n            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.\n            interactive: if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output.\n            visible: If False, component will be hidden.\n            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.\n            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.\n            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.\n            key: if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.\n            height: the height of the webrtc component\n            width: the width of the webrtc component\n        \"\"\"\n        self.height = height\n        self.width = width\n        self.assets = assets\n        self.audio_url = audio_url\n        \n        print('gaussian render init')","is_error":false}]},"uuid":"87223bee-65e4-4789-bc55-45b8c387a303","timestamp":"2026-06-15T21:40:44.452Z","toolUseResult":{"stdout":"\"\"\"gr.GaussianRender() component.\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import (Any, Literal)\nfrom gradio.components.base import Component\nfrom gradio.data_classes import FileData\nfrom gradio.events import Events\n\nclass gaussian_render(Component):\n    \"\"\"\n    Creates an webrtc component link to aliyun-rtc-sdk which can be used to pull or push media stream.\n    \"\"\"\n\n    EVENTS: Events = [\n        'start',\n        'start_render'\n    ]\n\n    data_model = FileData\n\n    def __init__(\n        self,\n        *,\n        container: bool = True,\n        scale: int | None = None,\n        min_width: int = 160,\n        interactive: bool | None = None,\n        visible: bool = True,\n        elem_id: str | None = None,\n        elem_classes: list[str] | str | None = None,\n        render: bool = True,\n        key: int | str | None = None,\n        height: int | str | None = None,\n        width: int | str | None = None,\n        assets: str | None = None,\n        audio_url: str | None = None,\n    ):\n        \"\"\"\n        Parameters:\n            container: If True, will place the component in a container - providing some extra padding around the border.\n            scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.\n            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.\n            interactive: if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output.\n            visible: If False, component will be hidden.\n            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.\n            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.\n            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.\n            key: if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.\n            height: the height of the webrtc component\n            width: the width of the webrtc component\n        \"\"\"\n        self.height = height\n        self.width = width\n        self.assets = assets\n        self.audio_url = audio_url\n        \n        print('gaussian render init')","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"3536b3b0-d8c0-420f-a06f-9de9f302d726","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"87223bee-65e4-4789-bc55-45b8c387a303","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01XsJUVWWK2oMupGecp1VDLs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0151uZGAtR6mRgdyiE9YC9m8","name":"Bash","input":{"command":"# Look at how the app_lam_audio2exp.py uses the renderer\ngrep -n \"gaussian_render\\|getExpressionData\\|GaussianRender\\|getChatState\" /mnt/DATA/WORK/LAM_Audio2Expression/app_lam_audio2exp.py | head -20","description":"Check how the Gradio app uses the renderer"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":693,"cache_read_input_tokens":63556,"output_tokens":679,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":693,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":679,"cache_read_input_tokens":63556,"cache_creation_input_tokens":693,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":693},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mhRpi1hbJcyhX3AWPT","type":"assistant","uuid":"d2bb40db-1426-4926-b168-9fd64d69991d","timestamp":"2026-06-15T21:40:45.913Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"d2bb40db-1426-4926-b168-9fd64d69991d","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0151uZGAtR6mRgdyiE9YC9m8","type":"tool_result","content":"22:from gradio_gaussian_render import gaussian_render\n257:                gs = gaussian_render(width=380, height=680)","is_error":false}]},"uuid":"2ab820a6-9894-4a4a-a36f-554efaf533a6","timestamp":"2026-06-15T21:40:45.973Z","toolUseResult":{"stdout":"22:from gradio_gaussian_render import gaussian_render\n257:                gs = gaussian_render(width=380, height=680)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"d2bb40db-1426-4926-b168-9fd64d69991d","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"cool ca fonctionne, implemente ça sur mon gui d'avatar","leafUuid":"2ab820a6-9894-4a4a-a36f-554efaf533a6","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"permission-mode","permissionMode":"bypassPermissions","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"parentUuid":"2ab820a6-9894-4a4a-a36f-554efaf533a6","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01Y6PZWePdfr1izVcn2apx5U","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DJiDvspt9ojgPGjfF4tFkP","name":"Bash","input":{"command":"# See how app_lam_audio2exp.py passes data to the renderer\nsed -n '250,320p' /mnt/DATA/WORK/LAM_Audio2Expression/app_lam_audio2exp.py","description":"Read renderer usage in Gradio app"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1479,"cache_read_input_tokens":64249,"output_tokens":254,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1479,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":254,"cache_read_input_tokens":64249,"cache_creation_input_tokens":1479,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1479},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5miMMMagFSNaf1uXMne","type":"assistant","uuid":"1a45a673-6eb7-4f09-97cb-53544ede5673","timestamp":"2026-06-15T21:40:52.270Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"1a45a673-6eb7-4f09-97cb-53544ede5673","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DJiDvspt9ojgPGjfF4tFkP","type":"tool_result","content":"                submit = gr.Button('Generate',\n                                   elem_id='lam_generate',\n                                   variant='primary')\n\n        if h5_rendering:\n            gr.set_static_paths(Path.cwd().absolute() / \"assets/\")\n            with gr.Row():\n                gs = gaussian_render(width=380, height=680)\n\n        working_dir = gr.State()\n        selected_audio = gr.Textbox(visible=False)\n        selected_render_file = gr.Textbox(visible=False)\n\n        submit.click(\n            fn=assert_input_image,\n            inputs=[input_image,input_zip_textbox],\n            queue=False,\n        ).success(\n            fn=prepare_working_dir,\n            outputs=[working_dir],\n            queue=False,\n        ).success(\n            fn=core_fn,\n            inputs=[input_image, audio_input,\n                    working_dir, input_zip_textbox],\n            outputs=[selected_audio, selected_render_file],\n            queue=False,\n        ).success(\n            fn=audio_loading,\n            outputs=[selected_audio],\n            js='''(output_component) => window.loadAudio(output_component)'''\n        ).success(\n            fn=do_render(),\n            outputs=[selected_render_file],\n            js='''(selected_render_file) => window.start(selected_render_file)'''\n        )\n\n        demo.queue()\n        demo.launch(inbrowser=True)\n\n\n\ndef launch_gradio_app():\n    os.environ.update({\n        'APP_ENABLED': '1',\n        'APP_MODEL_NAME':'',\n        'APP_INFER': 'configs/lam_audio2exp_streaming_config.py',\n        'APP_TYPE': 'infer.audio2exp',\n        'NUMBA_THREADING_LAYER': 'omp',\n    })\n\n    args = default_argument_parser().parse_args()\n    args.config_file = 'configs/lam_audio2exp_config_streaming.py'\n    cfg = default_config_parser(args.config_file, args.options)\n    cfg = default_setup(cfg)\n\n    cfg.ex_vol = True\n    infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\n\n    demo_lam_audio2exp(infer, cfg)\n\n\nif __name__ == '__main__':\n    launch_gradio_app()","is_error":false}]},"uuid":"647e0cb0-d46f-4623-8141-c4146a54fc60","timestamp":"2026-06-15T21:40:52.321Z","toolUseResult":{"stdout":"                submit = gr.Button('Generate',\n                                   elem_id='lam_generate',\n                                   variant='primary')\n\n        if h5_rendering:\n            gr.set_static_paths(Path.cwd().absolute() / \"assets/\")\n            with gr.Row():\n                gs = gaussian_render(width=380, height=680)\n\n        working_dir = gr.State()\n        selected_audio = gr.Textbox(visible=False)\n        selected_render_file = gr.Textbox(visible=False)\n\n        submit.click(\n            fn=assert_input_image,\n            inputs=[input_image,input_zip_textbox],\n            queue=False,\n        ).success(\n            fn=prepare_working_dir,\n            outputs=[working_dir],\n            queue=False,\n        ).success(\n            fn=core_fn,\n            inputs=[input_image, audio_input,\n                    working_dir, input_zip_textbox],\n            outputs=[selected_audio, selected_render_file],\n            queue=False,\n        ).success(\n            fn=audio_loading,\n            outputs=[selected_audio],\n            js='''(output_component) => window.loadAudio(output_component)'''\n        ).success(\n            fn=do_render(),\n            outputs=[selected_render_file],\n            js='''(selected_render_file) => window.start(selected_render_file)'''\n        )\n\n        demo.queue()\n        demo.launch(inbrowser=True)\n\n\n\ndef launch_gradio_app():\n    os.environ.update({\n        'APP_ENABLED': '1',\n        'APP_MODEL_NAME':'',\n        'APP_INFER': 'configs/lam_audio2exp_streaming_config.py',\n        'APP_TYPE': 'infer.audio2exp',\n        'NUMBA_THREADING_LAYER': 'omp',\n    })\n\n    args = default_argument_parser().parse_args()\n    args.config_file = 'configs/lam_audio2exp_config_streaming.py'\n    cfg = default_config_parser(args.config_file, args.options)\n    cfg = default_setup(cfg)\n\n    cfg.ex_vol = True\n    infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\n\n    demo_lam_audio2exp(infer, cfg)\n\n\nif __name__ == '__main__':\n    launch_gradio_app()","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"1a45a673-6eb7-4f09-97cb-53544ede5673","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"647e0cb0-d46f-4623-8141-c4146a54fc60","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01Y6PZWePdfr1izVcn2apx5U","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01REz7HZgaYn2ctP8Lxz71DR","name":"Bash","input":{"command":"# Check what getChatState and getExpressionData look like in the app\ngrep -n \"getChatState\\|getExpressionData\\|js=\" /mnt/DATA/WORK/LAM_Audio2Expression/app_lam_audio2exp.py | head -20","description":"Find JS callbacks in the Gradio app"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1479,"cache_read_input_tokens":64249,"output_tokens":254,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1479,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":254,"cache_read_input_tokens":64249,"cache_creation_input_tokens":1479,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1479},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5miMMMagFSNaf1uXMne","type":"assistant","uuid":"b664af3f-d137-43e1-a95d-3f5d4a70bd58","timestamp":"2026-06-15T21:40:53.382Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"b664af3f-d137-43e1-a95d-3f5d4a70bd58","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01REz7HZgaYn2ctP8Lxz71DR","type":"tool_result","content":"280:            js='''(output_component) => window.loadAudio(output_component)'''\n284:            js='''(selected_render_file) => window.start(selected_render_file)'''","is_error":false}]},"uuid":"d4a9b95f-046d-4721-a422-1251286f4716","timestamp":"2026-06-15T21:40:53.425Z","toolUseResult":{"stdout":"280:            js='''(output_component) => window.loadAudio(output_component)'''\n284:            js='''(selected_render_file) => window.start(selected_render_file)'''","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"b664af3f-d137-43e1-a95d-3f5d4a70bd58","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"d4a9b95f-046d-4721-a422-1251286f4716","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_012EwvELNN5dccbMj1iSUEzF","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":967,"cache_read_input_tokens":65728,"output_tokens":548,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":967,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":548,"cache_read_input_tokens":65728,"cache_creation_input_tokens":967,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":967},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5miu9nguQXRidtEr9xt","type":"assistant","uuid":"c41d94bd-f658-44ac-9d05-e0d8a8b521bf","timestamp":"2026-06-15T21:41:00.976Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"c4426348-6a03-4738-8ea6-1522751315b0","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_016D1E74pgxNgyJ4QYormYz2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JVJ1c1tmUpFPxPyLXFkbFJ","name":"Bash","input":{"command":"$HOME/miniconda3/bin/python -c \"\nimport struct, json, gzip\n\nwith open('/mnt/DATA/WORK/LAM_Audio2Expression/assets/sample_lam/barbara/arkitWithBSData/skin.glb', 'rb') as f:\n    # GLB header\n    magic, version, total_len = struct.unpack('<4sII', f.read(12))\n    # First chunk (JSON)\n    chunk_len, chunk_type = struct.unpack('<II', f.read(8))\n    json_data = f.read(chunk_len)\n    gltf = json.loads(json_data)\n\nprint('Meshes:', len(gltf.get('meshes', [])))\nfor i, mesh in enumerate(gltf.get('meshes', [])[:3]):\n    print(f'  mesh[{i}] name={mesh.get(\\\"name\\\")}, primitives={len(mesh.get(\\\"primitives\\\",[]))}')\n    for j, prim in enumerate(mesh.get('primitives', [])[:2]):\n        targets = prim.get('targets', [])\n        print(f'    prim[{j}]: {len(targets)} morph targets')\n        if targets:\n            print(f'    first target keys: {list(targets[0].keys())}')\n\nprint('Nodes:', len(gltf.get('nodes', [])))\nextras = gltf.get('extras', {})\nprint('extras keys:', list(extras.keys()) if extras else 'none')\n\" 2>&1 | head -30","description":"Parse skin.glb GLTF JSON to check for morph targets"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":552,"cache_read_input_tokens":67853,"output_tokens":1337,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":552,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":1337,"cache_read_input_tokens":67853,"cache_creation_input_tokens":552,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":552},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mk19j78ttwmyZn9CA5","type":"assistant","uuid":"7053733d-497f-44aa-a803-8183d304a79f","timestamp":"2026-06-15T21:41:33.313Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"296eb2d8-4c55-49d0-a09c-a88070618ee7","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01Trux4Ek4ZjrafNtBSyjRRM","type":"message","role":"assistant","content":[{"type":"text","text":"51 morph targets — Three.js peut les animer directement. Parfait."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1404,"cache_read_input_tokens":68405,"output_tokens":446,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1404,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":446,"cache_read_input_tokens":68405,"cache_creation_input_tokens":1404,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1404},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mmqsBMftdUgD31iBUb","type":"assistant","uuid":"ba60851f-e69f-4e24-945a-e1950dde7845","timestamp":"2026-06-15T21:41:39.303Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"ba60851f-e69f-4e24-945a-e1950dde7845","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01Trux4Ek4ZjrafNtBSyjRRM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GiCvvupBWm9wD51neE8xeL","name":"Bash","input":{"command":"$HOME/miniconda3/bin/python -c \"\nimport struct, json\nwith open('/mnt/DATA/WORK/LAM_Audio2Expression/assets/sample_lam/barbara/arkitWithBSData/skin.glb', 'rb') as f:\n    struct.unpack('<4sII', f.read(12))\n    chunk_len, chunk_type = struct.unpack('<II', f.read(8))\n    gltf = json.loads(f.read(chunk_len))\n\nmesh = gltf['meshes'][0]\nprim = mesh['primitives'][0]\nextras = prim.get('extras', {})\nprint('extras:', list(extras.keys()))\n# Check mesh extras for target names\nprint('mesh extras:', mesh.get('extras', {}))\n# Check if targetNames is in extras\nif 'targetNames' in extras:\n    print('targetNames:', extras['targetNames'])\n# Check extensions\nprint('extensions:', list(prim.get('extensions', {}).keys()))\n\"","description":"Check morph target names in GLB"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1404,"cache_read_input_tokens":68405,"output_tokens":446,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1404,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":446,"cache_read_input_tokens":68405,"cache_creation_input_tokens":1404,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1404},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mmqsBMftdUgD31iBUb","type":"assistant","uuid":"55976ff0-50f9-490d-bf42-0e1dd2343cdb","timestamp":"2026-06-15T21:41:41.899Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"55976ff0-50f9-490d-bf42-0e1dd2343cdb","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GiCvvupBWm9wD51neE8xeL","type":"tool_result","content":"extras: []\nmesh extras: {'targetNames': ['mouthShrugLower', 'mouthShrugUpper', 'mouthSmileLeft', 'mouthSmileRight', 'mouthStretchLeft', 'mouthStretchRight', 'mouthUpperUpLeft', 'mouthUpperUpRight', 'noseSneerLeft', 'noseSneerRight', 'mouthFrownLeft', 'mouthFrownRight', 'mouthFunnel', 'mouthLeft', 'mouthLowerDownRight', 'mouthPressLeft', 'mouthPressRight', 'mouthPucker', 'mouthRight', 'mouthRollLower', 'mouthRollUpper', 'eyeSquintRight', 'eyeWideLeft', 'eyeWideRight', 'jawForward', 'jawLeft', 'jawOpen', 'jawRight', 'mouthClose', 'mouthDimpleLeft', 'mouthDimpleRight', 'eyeBlinkLeft', 'eyeBlinkRight', 'eyeLookDownLeft', 'eyeLookDownRight', 'eyeLookInLeft', 'eyeLookInRight', 'eyeLookOutLeft', 'eyeLookOutRight', 'eyeLookUpRight', 'eyeSquintLeft', 'browDownLeft', 'browDownRight', 'browInnerUp', 'browOuterUpLeft', 'browOuterUpRight', 'cheekPuff', 'cheekSquintLeft', 'cheekSquintRight', 'eyeLookUpLeft', 'mouthLowerDownLeft']}\nextensions: []","is_error":false}]},"uuid":"fb2e0aeb-38ac-43e9-9cf8-53933f9e29fe","timestamp":"2026-06-15T21:41:42.000Z","toolUseResult":{"stdout":"extras: []\nmesh extras: {'targetNames': ['mouthShrugLower', 'mouthShrugUpper', 'mouthSmileLeft', 'mouthSmileRight', 'mouthStretchLeft', 'mouthStretchRight', 'mouthUpperUpLeft', 'mouthUpperUpRight', 'noseSneerLeft', 'noseSneerRight', 'mouthFrownLeft', 'mouthFrownRight', 'mouthFunnel', 'mouthLeft', 'mouthLowerDownRight', 'mouthPressLeft', 'mouthPressRight', 'mouthPucker', 'mouthRight', 'mouthRollLower', 'mouthRollUpper', 'eyeSquintRight', 'eyeWideLeft', 'eyeWideRight', 'jawForward', 'jawLeft', 'jawOpen', 'jawRight', 'mouthClose', 'mouthDimpleLeft', 'mouthDimpleRight', 'eyeBlinkLeft', 'eyeBlinkRight', 'eyeLookDownLeft', 'eyeLookDownRight', 'eyeLookInLeft', 'eyeLookInRight', 'eyeLookOutLeft', 'eyeLookOutRight', 'eyeLookUpRight', 'eyeSquintLeft', 'browDownLeft', 'browDownRight', 'browInnerUp', 'browOuterUpLeft', 'browOuterUpRight', 'cheekPuff', 'cheekSquintLeft', 'cheekSquintRight', 'eyeLookUpLeft', 'mouthLowerDownLeft']}\nextensions: []","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"55976ff0-50f9-490d-bf42-0e1dd2343cdb","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"cool ca fonctionne, implemente ça sur mon gui d'avatar","leafUuid":"fb2e0aeb-38ac-43e9-9cf8-53933f9e29fe","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"parentUuid":"a7583595-cfed-431c-9151-62a9d54b0473","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012pnBQ27biGDQ9Gpr1DgrWM","type":"tool_result","content":"\"\"\"\nThe code is base on https://github.com/Pointcept/Pointcept\n\"\"\"\n\nimport os\nimport sys\nimport argparse\nimport multiprocessing as mp\nfrom torch.nn.parallel import DistributedDataParallel\n\n\nimport utils.comm as comm\nfrom utils.env import get_random_seed, set_seed\nfrom utils.config import Config, DictAction\n\n\ndef create_ddp_model(model, *, fp16_compression=False, **kwargs):\n    \"\"\"\n    Create a DistributedDataParallel model if there are >1 processes.\n    Args:\n        model: a torch.nn.Module\n        fp16_compression: add fp16 compression hooks to the ddp object.\n            See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook\n        kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`.\n    \"\"\"\n    if comm.get_world_size() == 1:\n        return model\n    # kwargs['find_unused_parameters'] = True\n    if \"device_ids\" not in kwargs:\n        kwargs[\"device_ids\"] = [comm.get_local_rank()]\n        if \"output_device\" not in kwargs:\n            kwargs[\"output_device\"] = [comm.get_local_rank()]\n    ddp = DistributedDataParallel(model, **kwargs)\n    if fp16_compression:\n        from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks\n\n        ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook)\n    return ddp\n\n\ndef worker_init_fn(worker_id, num_workers, rank, seed):\n    \"\"\"Worker init func for dataloader.\n\n    The seed of each worker equals to num_worker * rank + worker_id + user_seed\n\n    Args:\n        worker_id (int): Worker id.\n        num_workers (int): Number of workers.\n        rank (int): The rank of current process.\n        seed (int): The random seed to use.\n    \"\"\"\n\n    worker_seed = num_workers * rank + worker_id + seed\n    set_seed(worker_seed)\n\n\ndef default_argument_parser(epilog=None):\n    parser = argparse.ArgumentParser(\n        epilog=epilog\n        or f\"\"\"","is_error":false}]},"uuid":"d95b4ed5-6562-4089-8288-271cf87c9826","timestamp":"2026-06-15T21:43:46.578Z","toolUseResult":{"stdout":"\"\"\"\nThe code is base on https://github.com/Pointcept/Pointcept\n\"\"\"\n\nimport os\nimport sys\nimport argparse\nimport multiprocessing as mp\nfrom torch.nn.parallel import DistributedDataParallel\n\n\nimport utils.comm as comm\nfrom utils.env import get_random_seed, set_seed\nfrom utils.config import Config, DictAction\n\n\ndef create_ddp_model(model, *, fp16_compression=False, **kwargs):\n    \"\"\"\n    Create a DistributedDataParallel model if there are >1 processes.\n    Args:\n        model: a torch.nn.Module\n        fp16_compression: add fp16 compression hooks to the ddp object.\n            See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook\n        kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`.\n    \"\"\"\n    if comm.get_world_size() == 1:\n        return model\n    # kwargs['find_unused_parameters'] = True\n    if \"device_ids\" not in kwargs:\n        kwargs[\"device_ids\"] = [comm.get_local_rank()]\n        if \"output_device\" not in kwargs:\n            kwargs[\"output_device\"] = [comm.get_local_rank()]\n    ddp = DistributedDataParallel(model, **kwargs)\n    if fp16_compression:\n        from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks\n\n        ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook)\n    return ddp\n\n\ndef worker_init_fn(worker_id, num_workers, rank, seed):\n    \"\"\"Worker init func for dataloader.\n\n    The seed of each worker equals to num_worker * rank + worker_id + user_seed\n\n    Args:\n        worker_id (int): Worker id.\n        num_workers (int): Number of workers.\n        rank (int): The rank of current process.\n        seed (int): The random seed to use.\n    \"\"\"\n\n    worker_seed = num_workers * rank + worker_id + seed\n    set_seed(worker_seed)\n\n\ndef default_argument_parser(epilog=None):\n    parser = argparse.ArgumentParser(\n        epilog=epilog\n        or f\"\"\"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"a7583595-cfed-431c-9151-62a9d54b0473","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"cool ca fonctionne, implemente ça sur mon gui d'avatar","leafUuid":"d95b4ed5-6562-4089-8288-271cf87c9826","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"parentUuid":"d95b4ed5-6562-4089-8288-271cf87c9826","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01XNWCwEWQSTqLEkgh1T7qQh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01384BaNEA275KjDdXHTTg44","name":"Bash","input":{"command":"grep -n \"audio_input\\|save_json\\|DictAction\\|options\" /mnt/DATA/WORK/LAM_Audio2Expression/engines/defaults.py | head -20","description":"Check how options are parsed in the config"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2102,"cache_read_input_tokens":74894,"output_tokens":121,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2102,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":121,"cache_read_input_tokens":74894,"cache_creation_input_tokens":2102,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2102},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mwfytmEwCbrBEaiXZF","type":"assistant","uuid":"1e6e6012-f522-4be5-b69d-71270dba66f0","timestamp":"2026-06-15T21:43:50.496Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"1e6e6012-f522-4be5-b69d-71270dba66f0","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01384BaNEA275KjDdXHTTg44","type":"tool_result","content":"14:from utils.config import Config, DictAction\n64:    Change some config options:\n99:        \"--options\", nargs=\"+\", action=DictAction, help=\"custom options\"\n104:def default_config_parser(file_path, options):\n112:    if options is not None:\n113:        cfg.merge_from_dict(options)","is_error":false}]},"uuid":"369fb0c9-f7d9-4a24-95bd-be295e7db254","timestamp":"2026-06-15T21:43:50.556Z","toolUseResult":{"stdout":"14:from utils.config import Config, DictAction\n64:    Change some config options:\n99:        \"--options\", nargs=\"+\", action=DictAction, help=\"custom options\"\n104:def default_config_parser(file_path, options):\n112:    if options is not None:\n113:        cfg.merge_from_dict(options)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"1e6e6012-f522-4be5-b69d-71270dba66f0","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"369fb0c9-f7d9-4a24-95bd-be295e7db254","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01QDv6zuEJDPLgrMnk1Sy4ND","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":216,"cache_read_input_tokens":76996,"output_tokens":490,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":216,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":490,"cache_read_input_tokens":76996,"cache_creation_input_tokens":216,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":216},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mwy63NWFn2H628s8B2","type":"assistant","uuid":"d2004fdb-4b55-4347-ba96-041f6eb861bd","timestamp":"2026-06-15T21:43:58.580Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"d2004fdb-4b55-4347-ba96-041f6eb861bd","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01QDv6zuEJDPLgrMnk1Sy4ND","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XZKbABCe7wqVVXfFtr2qJc","name":"Bash","input":{"command":"grep -n \"merge_from_dict\\|class Config\" /mnt/DATA/WORK/LAM_Audio2Expression/utils/config.py | head -10","description":"Check Config class in utils"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":216,"cache_read_input_tokens":76996,"output_tokens":490,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":216,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":490,"cache_read_input_tokens":76996,"cache_creation_input_tokens":216,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":216},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mwy63NWFn2H628s8B2","type":"assistant","uuid":"42f88093-2817-4aac-ad2d-06a53e7c99fe","timestamp":"2026-06-15T21:43:59.002Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"42f88093-2817-4aac-ad2d-06a53e7c99fe","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XZKbABCe7wqVVXfFtr2qJc","type":"tool_result","content":"35:class ConfigDict(Dict):\n72:class Config:\n553:    def merge_from_dict(self, options, allow_list_keys=True):\n562:            >>> cfg.merge_from_dict(options)\n571:            >>> cfg.merge_from_dict(options, allow_list_keys=True)","is_error":false}]},"uuid":"4016da63-b650-4048-8584-6b244c70d10e","timestamp":"2026-06-15T21:43:59.065Z","toolUseResult":{"stdout":"35:class ConfigDict(Dict):\n72:class Config:\n553:    def merge_from_dict(self, options, allow_list_keys=True):\n562:            >>> cfg.merge_from_dict(options)\n571:            >>> cfg.merge_from_dict(options, allow_list_keys=True)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"42f88093-2817-4aac-ad2d-06a53e7c99fe","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"4016da63-b650-4048-8584-6b244c70d10e","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01DD6xawRLUdW1BU7c7avZyo","type":"message","role":"assistant","content":[{"type":"text","text":"Parfait. J'ai tout ce qu'il faut. Je lance l'implémentation complète."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":576,"cache_read_input_tokens":77212,"output_tokens":216,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":576,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":216,"cache_read_input_tokens":77212,"cache_creation_input_tokens":576,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":576},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mxb4k7wqESDySsUVdW","type":"assistant","uuid":"3a6b684b-0a1e-4fb2-b111-36ecf47f963c","timestamp":"2026-06-15T21:44:02.155Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"3a6b684b-0a1e-4fb2-b111-36ecf47f963c","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01DD6xawRLUdW1BU7c7avZyo","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01YRvC7ujYoreJn5Gec3iXst","name":"Bash","input":{"command":"mkdir -p /mnt/DATA/WORK/agent-gui/static/avatar/barbara\nmkdir -p /mnt/DATA/WORK/agent-gui/data/tts\ncp /mnt/DATA/WORK/LAM_Audio2Expression/assets/sample_lam/barbara/arkitWithBSData/skin.glb \\\n   /mnt/DATA/WORK/agent-gui/static/avatar/barbara/skin.glb\necho \"Copied.\"","description":"Copy barbara skin.glb to agent-gui static"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":576,"cache_read_input_tokens":77212,"output_tokens":216,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":576,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":216,"cache_read_input_tokens":77212,"cache_creation_input_tokens":576,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":576},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mxb4k7wqESDySsUVdW","type":"assistant","uuid":"3080b66e-9bb1-4ca1-a586-bb72ed733288","timestamp":"2026-06-15T21:44:04.434Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"b6b48d14-fb89-42a8-a186-4e13b32786ce","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01LBmMSSGCdxtmZEr7j44gnx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01U5X6dNQe88dLYMAj9e15R1","name":"Write","input":{"file_path":"/mnt/DATA/WORK/LAM_Audio2Expression/run_inference.py","content":"\"\"\"CLI: audio.wav → bsData.json  (runs inside lam_a2e conda env)\"\"\"\nimport argparse\nimport sys\nfrom pathlib import Path\n\nsys.path.insert(0, str(Path(__file__).parent))\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--audio', required=True)\n    parser.add_argument('--output', required=True)\n    args = parser.parse_args()\n\n    import librosa\n    import numpy as np\n    from engines.defaults import default_argument_parser, default_config_parser, default_setup\n    from engines.infer import INFER\n    from models.utils import export_blendshape_animation, ARKitBlendShape\n\n    cfg_args = default_argument_parser().parse_args([])\n    cfg_args.config_file = 'configs/lam_audio2exp_config_streaming.py'\n    cfg = default_config_parser(cfg_args.config_file, {\n        'audio_input': args.audio,\n        'save_json_path': args.output,\n    })\n    cfg = default_setup(cfg)\n\n    infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\n    infer.model.eval()\n\n    audio, sr = librosa.load(args.audio, sr=16000)\n    context = None\n    all_exp = []\n    for i in range(audio.shape[0] // 16000 + 1):\n        out, context = infer.infer_streaming_audio(audio[i * 16000:(i + 1) * 16000], sr, context)\n        all_exp.append(out['expression'])\n\n    all_exp = np.concatenate(all_exp, axis=0)\n    export_blendshape_animation(all_exp, args.output, ARKitBlendShape, fps=30.0)\n    print(f'OK {len(all_exp)} frames', flush=True)\n\n\nif __name__ == '__main__':\n    main()\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":230,"cache_read_input_tokens":77788,"output_tokens":594,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":230,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":594,"cache_read_input_tokens":77788,"cache_creation_input_tokens":230,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":230},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mxz9Zj4ShmwGP6gcDd","type":"assistant","uuid":"78d33aad-cb59-47ec-8ae8-6fa6cafc0648","timestamp":"2026-06-15T21:44:16.436Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"78d33aad-cb59-47ec-8ae8-6fa6cafc0648","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U5X6dNQe88dLYMAj9e15R1","type":"tool_result","content":"File created successfully at: /mnt/DATA/WORK/LAM_Audio2Expression/run_inference.py (file state is current in your context — no need to Read it back)"}]},"uuid":"fd578fef-a97b-4db5-bb9c-0676009c84f4","timestamp":"2026-06-15T21:44:16.457Z","toolUseResult":{"type":"create","filePath":"/mnt/DATA/WORK/LAM_Audio2Expression/run_inference.py","content":"\"\"\"CLI: audio.wav → bsData.json  (runs inside lam_a2e conda env)\"\"\"\nimport argparse\nimport sys\nfrom pathlib import Path\n\nsys.path.insert(0, str(Path(__file__).parent))\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--audio', required=True)\n    parser.add_argument('--output', required=True)\n    args = parser.parse_args()\n\n    import librosa\n    import numpy as np\n    from engines.defaults import default_argument_parser, default_config_parser, default_setup\n    from engines.infer import INFER\n    from models.utils import export_blendshape_animation, ARKitBlendShape\n\n    cfg_args = default_argument_parser().parse_args([])\n    cfg_args.config_file = 'configs/lam_audio2exp_config_streaming.py'\n    cfg = default_config_parser(cfg_args.config_file, {\n        'audio_input': args.audio,\n        'save_json_path': args.output,\n    })\n    cfg = default_setup(cfg)\n\n    infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\n    infer.model.eval()\n\n    audio, sr = librosa.load(args.audio, sr=16000)\n    context = None\n    all_exp = []\n    for i in range(audio.shape[0] // 16000 + 1):\n        out, context = infer.infer_streaming_audio(audio[i * 16000:(i + 1) * 16000], sr, context)\n        all_exp.append(out['expression'])\n\n    all_exp = np.concatenate(all_exp, axis=0)\n    export_blendshape_animation(all_exp, args.output, ARKitBlendShape, fps=30.0)\n    print(f'OK {len(all_exp)} frames', flush=True)\n\n\nif __name__ == '__main__':\n    main()\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"78d33aad-cb59-47ec-8ae8-6fa6cafc0648","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"type":"file-history-snapshot","messageId":"c0729729-1d7d-4573-a738-14e77b3fa112","snapshot":{"messageId":"5ad3102e-5421-4f40-a2b8-833842882da5","trackedFileBackups":{"/mnt/DATA/WORK/LAM_Audio2Expression/run_inference.py":{"backupFileName":null,"version":1,"backupTime":"2026-06-15T21:44:16.449Z"},"/mnt/DATA/WORK/agent-gui/agent_gui/tts.py":{"backupFileName":null,"version":1,"backupTime":"2026-06-15T21:44:22.652Z"}},"timestamp":"2026-06-15T21:35:23.814Z"},"isSnapshotUpdate":true}
{"parentUuid":"c287e659-8cf1-451f-ac45-2cf45986b5f1","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01KT7s1rTJpSmdhLwcoZQTJT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01A6qoP6Gsx8hcXNBjpY4MxW","name":"Write","input":{"file_path":"/mnt/DATA/WORK/agent-gui/agent_gui/tts.py","content":"from __future__ import annotations\n\nimport subprocess\nfrom pathlib import Path\n\n_EDGE_TTS = Path.home() / \"miniconda3/bin/edge-tts\"\n\n\ndef synthesize(text: str, wav_path: Path, voice: str = \"en-US-AriaNeural\") -> None:\n    mp3_path = wav_path.with_suffix(\".mp3\")\n    subprocess.run(\n        [str(_EDGE_TTS), \"--text\", text, \"--voice\", voice, \"--write-media\", str(mp3_path)],\n        check=True,\n        timeout=60,\n        capture_output=True,\n    )\n    subprocess.run(\n        [\"ffmpeg\", \"-y\", \"-i\", str(mp3_path), \"-ar\", \"16000\", \"-ac\", \"1\", str(wav_path)],\n        check=True,\n        capture_output=True,\n    )\n    mp3_path.unlink(missing_ok=True)\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":746,"cache_read_input_tokens":78018,"output_tokens":322,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":746,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":322,"cache_read_input_tokens":78018,"cache_creation_input_tokens":746,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":746},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mytmduM9AezrVDoQXZ","type":"assistant","uuid":"c0729729-1d7d-4573-a738-14e77b3fa112","timestamp":"2026-06-15T21:44:22.635Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"c0729729-1d7d-4573-a738-14e77b3fa112","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01A6qoP6Gsx8hcXNBjpY4MxW","type":"tool_result","content":"File created successfully at: /mnt/DATA/WORK/agent-gui/agent_gui/tts.py (file state is current in your context — no need to Read it back)"}]},"uuid":"d8d61f86-1270-4acb-b4f8-aa9b22b10ab0","timestamp":"2026-06-15T21:44:22.656Z","toolUseResult":{"type":"create","filePath":"/mnt/DATA/WORK/agent-gui/agent_gui/tts.py","content":"from __future__ import annotations\n\nimport subprocess\nfrom pathlib import Path\n\n_EDGE_TTS = Path.home() / \"miniconda3/bin/edge-tts\"\n\n\ndef synthesize(text: str, wav_path: Path, voice: str = \"en-US-AriaNeural\") -> None:\n    mp3_path = wav_path.with_suffix(\".mp3\")\n    subprocess.run(\n        [str(_EDGE_TTS), \"--text\", text, \"--voice\", voice, \"--write-media\", str(mp3_path)],\n        check=True,\n        timeout=60,\n        capture_output=True,\n    )\n    subprocess.run(\n        [\"ffmpeg\", \"-y\", \"-i\", str(mp3_path), \"-ar\", \"16000\", \"-ac\", \"1\", str(wav_path)],\n        check=True,\n        capture_output=True,\n    )\n    mp3_path.unlink(missing_ok=True)\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"c0729729-1d7d-4573-a738-14e77b3fa112","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"file-history-snapshot","messageId":"e9204c48-ccfc-41e9-b4a9-6acdf25b2a78","snapshot":{"messageId":"5ad3102e-5421-4f40-a2b8-833842882da5","trackedFileBackups":{"/mnt/DATA/WORK/LAM_Audio2Expression/run_inference.py":{"backupFileName":null,"version":1,"backupTime":"2026-06-15T21:44:16.449Z"},"/mnt/DATA/WORK/agent-gui/agent_gui/tts.py":{"backupFileName":null,"version":1,"backupTime":"2026-06-15T21:44:22.652Z"},"/mnt/DATA/WORK/agent-gui/agent_gui/avatar_lam.py":{"backupFileName":null,"version":1,"backupTime":"2026-06-15T21:44:29.196Z"}},"timestamp":"2026-06-15T21:35:23.814Z"},"isSnapshotUpdate":true}
{"parentUuid":"d8d61f86-1270-4acb-b4f8-aa9b22b10ab0","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01T2N4Kmmhs2Ua1BezDPe9MH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VgRKXn3w86GSKDetKFbMr1","name":"Write","input":{"file_path":"/mnt/DATA/WORK/agent-gui/agent_gui/avatar_lam.py","content":"from __future__ import annotations\n\nimport json\nimport subprocess\nimport tempfile\nfrom pathlib import Path\n\n_LAM_DIR = Path(\"/mnt/DATA/WORK/LAM_Audio2Expression\")\n_CONDA = Path.home() / \"miniconda3/bin/conda\"\n_SCRIPT = _LAM_DIR / \"run_inference.py\"\n\n\ndef audio_to_blendshapes(audio_path: Path) -> dict:\n    with tempfile.NamedTemporaryFile(suffix=\".json\", delete=False) as fh:\n        out = Path(fh.name)\n    try:\n        subprocess.run(\n            [\n                str(_CONDA), \"run\", \"--no-capture-output\", \"-n\", \"lam_a2e\",\n                \"python\", str(_SCRIPT),\n                \"--audio\", str(audio_path),\n                \"--output\", str(out),\n            ],\n            check=True,\n            cwd=str(_LAM_DIR),\n            timeout=300,\n        )\n        return json.loads(out.read_text())\n    finally:\n        out.unlink(missing_ok=True)\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":375,"cache_read_input_tokens":78764,"output_tokens":365,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":375,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":365,"cache_read_input_tokens":78764,"cache_creation_input_tokens":375,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":375},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mzKm3y7srZGAs4uWCu","type":"assistant","uuid":"e9204c48-ccfc-41e9-b4a9-6acdf25b2a78","timestamp":"2026-06-15T21:44:29.175Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e9204c48-ccfc-41e9-b4a9-6acdf25b2a78","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VgRKXn3w86GSKDetKFbMr1","type":"tool_result","content":"File created successfully at: /mnt/DATA/WORK/agent-gui/agent_gui/avatar_lam.py (file state is current in your context — no need to Read it back)"}]},"uuid":"1b5f861d-7306-4684-a099-0fa211b92df8","timestamp":"2026-06-15T21:44:29.201Z","toolUseResult":{"type":"create","filePath":"/mnt/DATA/WORK/agent-gui/agent_gui/avatar_lam.py","content":"from __future__ import annotations\n\nimport json\nimport subprocess\nimport tempfile\nfrom pathlib import Path\n\n_LAM_DIR = Path(\"/mnt/DATA/WORK/LAM_Audio2Expression\")\n_CONDA = Path.home() / \"miniconda3/bin/conda\"\n_SCRIPT = _LAM_DIR / \"run_inference.py\"\n\n\ndef audio_to_blendshapes(audio_path: Path) -> dict:\n    with tempfile.NamedTemporaryFile(suffix=\".json\", delete=False) as fh:\n        out = Path(fh.name)\n    try:\n        subprocess.run(\n            [\n                str(_CONDA), \"run\", \"--no-capture-output\", \"-n\", \"lam_a2e\",\n                \"python\", str(_SCRIPT),\n                \"--audio\", str(audio_path),\n                \"--output\", str(out),\n            ],\n            check=True,\n            cwd=str(_LAM_DIR),\n            timeout=300,\n        )\n        return json.loads(out.read_text())\n    finally:\n        out.unlink(missing_ok=True)\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"e9204c48-ccfc-41e9-b4a9-6acdf25b2a78","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"1b5f861d-7306-4684-a099-0fa211b92df8","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01HzVdadPV2YyNiEVfqsKUjB","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant je modifie `app.py` pour ajouter les endpoints avatar et le thread background."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":420,"cache_read_input_tokens":79139,"output_tokens":559,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":420,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":559,"cache_read_input_tokens":79139,"cache_creation_input_tokens":420,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":420},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mzorzchLBAeVSnxXCX","type":"assistant","uuid":"8e59798c-4fe2-4e1f-8c9d-9991cafede58","timestamp":"2026-06-15T21:44:33.585Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"8e59798c-4fe2-4e1f-8c9d-9991cafede58","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01HzVdadPV2YyNiEVfqsKUjB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LVxBmgtuQVQw2u3v4uKEUz","name":"Edit","input":{"replace_all":false,"file_path":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py","old_string":"from __future__ import annotations\n\nimport argparse\nimport json\nimport mimetypes\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .adapters import adapter_for\nfrom .avatar import available_states, get_avatar\nfrom .config import find_agent, load_agents\nfrom .sessions import SessionStore, new_session_id, utc_now\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nSTATIC_ROOT = PROJECT_ROOT / \"static\"\nDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\nDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"","new_string":"from __future__ import annotations\n\nimport argparse\nimport json\nimport mimetypes\nimport threading\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .adapters import adapter_for\nfrom .avatar import available_states, get_avatar\nfrom .config import find_agent, load_agents\nfrom .sessions import SessionStore, new_session_id, utc_now\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nSTATIC_ROOT = PROJECT_ROOT / \"static\"\nDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\nDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"\nTTS_DIR = PROJECT_ROOT / \"data\" / \"tts\"\nTTS_DIR.mkdir(parents=True, exist_ok=True)\n\n# keyed by session_id: None=pending, dict=ready, str=error message\n_anim_store: dict[str, None | dict | str] = {}\n_anim_lock = threading.Lock()"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":420,"cache_read_input_tokens":79139,"output_tokens":559,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":420,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":559,"cache_read_input_tokens":79139,"cache_creation_input_tokens":420,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":420},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5mzorzchLBAeVSnxXCX","type":"assistant","uuid":"a9a2dae8-3920-460b-8490-72abb5043e18","timestamp":"2026-06-15T21:44:39.151Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a9a2dae8-3920-460b-8490-72abb5043e18","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LVxBmgtuQVQw2u3v4uKEUz","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/agent_gui/app.py has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"5ad69372-d37a-4a2e-a5b4-e85aca0810c3","timestamp":"2026-06-15T21:44:39.174Z","toolUseResult":{"filePath":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py","oldString":"from __future__ import annotations\n\nimport argparse\nimport json\nimport mimetypes\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .adapters import adapter_for\nfrom .avatar import available_states, get_avatar\nfrom .config import find_agent, load_agents\nfrom .sessions import SessionStore, new_session_id, utc_now\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nSTATIC_ROOT = PROJECT_ROOT / \"static\"\nDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\nDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"","newString":"from __future__ import annotations\n\nimport argparse\nimport json\nimport mimetypes\nimport threading\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .adapters import adapter_for\nfrom .avatar import available_states, get_avatar\nfrom .config import find_agent, load_agents\nfrom .sessions import SessionStore, new_session_id, utc_now\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nSTATIC_ROOT = PROJECT_ROOT / \"static\"\nDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\nDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"\nTTS_DIR = PROJECT_ROOT / \"data\" / \"tts\"\nTTS_DIR.mkdir(parents=True, exist_ok=True)\n\n# keyed by session_id: None=pending, dict=ready, str=error message\n_anim_store: dict[str, None | dict | str] = {}\n_anim_lock = threading.Lock()","originalFile":"from __future__ import annotations\n\nimport argparse\nimport json\nimport mimetypes\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .adapters import adapter_for\nfrom .avatar import available_states, get_avatar\nfrom .config import find_agent, load_agents\nfrom .sessions import SessionStore, new_session_id, utc_now\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nSTATIC_ROOT = PROJECT_ROOT / \"static\"\nDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\nDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"\n\n\nclass AgentGuiServer(ThreadingHTTPServer):\n    def __init__(self, server_address, RequestHandlerClass, config_path: Path, sessions_root: Path):\n        super().__init__(server_address, RequestHandlerClass)\n        self.config_path = config_path\n        self.sessions = SessionStore(sessions_root)\n\n    @property\n    def agents(self):\n        return load_agents(self.config_path)\n\n\nclass Handler(BaseHTTPRequestHandler):\n    server: AgentGuiServer\n\n    def log_message(self, format: str, *args):\n        print(\"[agent-gui] \" + format % args)\n\n    def do_GET(self):\n        parsed = urlparse(self.path)\n        if parsed.path == \"/health\":\n            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n        if parsed.path == \"/api/agents\":\n            return self.json_response({\"agents\": self.server.agents})\n        if parsed.path == \"/api/avatar\":\n            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n        if parsed.path == \"/api/sessions\":\n            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n        if parsed.path == \"/api/history\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n        return self.serve_static(parsed.path)\n\n    def do_POST(self):\n        if self.path == \"/api/session\":\n            return self.json_response({\"session_id\": new_session_id()})\n        if self.path == \"/api/chat\":\n            return self.handle_chat()\n        return self.json_response({\"error\": \"Endpoint inconnu\"}, status=404)\n\n    def handle_chat(self):\n        try:\n            payload = self.read_json()\n            agent_id = str(payload.get(\"agent\", \"echo\"))\n            message = str(payload.get(\"message\", \"\")).strip()\n            session_id = str(payload.get(\"session_id\") or new_session_id())\n            if not message:\n                return self.json_response({\"error\": \"Message vide\"}, status=400)\n\n            agent = find_agent(self.server.agents, agent_id)\n            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n\n            result = adapter_for(agent).run(message, agent, session_id)\n            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n            return self.json_response(\n                {\n                    \"session_id\": session_id,\n                    \"agent\": agent_id,\n                    \"events\": stored_events,\n                    \"avatar\": get_avatar(result.avatar_state),\n                }\n            )\n        except KeyError as exc:\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n        except Exception as exc:  # defensive boundary for local UI\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)\n\n    def serve_static(self, request_path: str):\n        relative = \"index.html\" if request_path in (\"/\", \"\") else request_path.lstrip(\"/\")\n        path = (STATIC_ROOT / relative).resolve()\n        if STATIC_ROOT.resolve() not in path.parents and path != STATIC_ROOT.resolve():\n            return self.json_response({\"error\": \"Chemin interdit\"}, status=403)\n        if not path.exists() or not path.is_file():\n            return self.json_response({\"error\": \"Fichier introuvable\"}, status=404)\n        content_type = mimetypes.guess_type(str(path))[0] or \"application/octet-stream\"\n        body = path.read_bytes()\n        self.send_response(200)\n        self.send_header(\"Content-Type\", content_type)\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n    def read_json(self):\n        length = int(self.headers.get(\"Content-Length\", \"0\"))\n        body = self.rfile.read(length).decode(\"utf-8\") if length else \"{}\"\n        return json.loads(body)\n\n    def json_response(self, payload, status: int = 200):\n        body = json.dumps(payload, ensure_ascii=False, indent=2).encode(\"utf-8\")\n        self.send_response(status)\n        self.send_header(\"Content-Type\", \"application/json; charset=utf-8\")\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n\ndef main(argv: list[str] | None = None):\n    parser = argparse.ArgumentParser(description=\"Agent GUI local\")\n    parser.add_argument(\"--host\", default=\"127.0.0.1\")\n    parser.add_argument(\"--port\", type=int, default=8765)\n    parser.add_argument(\"--config\", type=Path, default=DEFAULT_CONFIG)\n    parser.add_argument(\"--sessions\", type=Path, default=DEFAULT_SESSIONS)\n    args = parser.parse_args(argv)\n\n    server = AgentGuiServer((args.host, args.port), Handler, args.config, args.sessions)\n    print(f\"Agent GUI lancé: http://{args.host}:{args.port}\")\n    print(f\"Config agents: {args.config}\")\n    print(\"Ctrl+C pour arrêter.\")\n    server.serve_forever()\n\n\nif __name__ == \"__main__\":\n    main()\n","structuredPatch":[{"oldStart":3,"oldLines":6,"newStart":3,"newLines":7,"lines":[" import argparse"," import json"," import mimetypes","+import threading"," from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer"," from pathlib import Path"," from urllib.parse import parse_qs, urlparse"]},{"oldStart":16,"oldLines":8,"newStart":17,"newLines":14,"lines":[" STATIC_ROOT = PROJECT_ROOT / \"static\""," DEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\""," DEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"","+TTS_DIR = PROJECT_ROOT / \"data\" / \"tts\"","+TTS_DIR.mkdir(parents=True, exist_ok=True)"," ","+# keyed by session_id: None=pending, dict=ready, str=error message","+_anim_store: dict[str, None | dict | str] = {}","+_anim_lock = threading.Lock()"," ","+"," class AgentGuiServer(ThreadingHTTPServer):","     def __init__(self, server_address, RequestHandlerClass, config_path: Path, sessions_root: Path):","         super().__init__(server_address, RequestHandlerClass)"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"a9a2dae8-3920-460b-8490-72abb5043e18","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"cool ca fonctionne, implemente ça sur mon gui d'avatar","leafUuid":"5ad69372-d37a-4a2e-a5b4-e85aca0810c3","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"permission-mode","permissionMode":"bypassPermissions","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"parentUuid":"5ad69372-d37a-4a2e-a5b4-e85aca0810c3","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_011rrErSE38LEKZtEkUaHZue","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LefJaQCaym7ztcYksqSwQt","name":"Edit","input":{"replace_all":false,"file_path":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py","old_string":"    def do_GET(self):\n        parsed = urlparse(self.path)\n        if parsed.path == \"/health\":\n            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n        if parsed.path == \"/api/agents\":\n            return self.json_response({\"agents\": self.server.agents})\n        if parsed.path == \"/api/avatar\":\n            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n        if parsed.path == \"/api/sessions\":\n            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n        if parsed.path == \"/api/history\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n        return self.serve_static(parsed.path)","new_string":"    def do_GET(self):\n        parsed = urlparse(self.path)\n        if parsed.path == \"/health\":\n            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n        if parsed.path == \"/api/agents\":\n            return self.json_response({\"agents\": self.server.agents})\n        if parsed.path == \"/api/avatar\":\n            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n        if parsed.path == \"/api/sessions\":\n            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n        if parsed.path == \"/api/history\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n        if parsed.path == \"/api/avatar/anim\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            with _anim_lock:\n                val = _anim_store.get(session_id)\n            if val is None:\n                return self.json_response({\"status\": \"pending\"})\n            if isinstance(val, str):\n                return self.json_response({\"status\": \"error\", \"message\": val})\n            return self.json_response({\"status\": \"ready\", \"bsData\": val})\n        if parsed.path == \"/api/avatar/audio\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            wav = TTS_DIR / f\"{session_id}.wav\"\n            if not wav.exists():\n                return self.json_response({\"error\": \"audio not found\"}, status=404)\n            body = wav.read_bytes()\n            self.send_response(200)\n            self.send_header(\"Content-Type\", \"audio/wav\")\n            self.send_header(\"Content-Length\", str(len(body)))\n            self.end_headers()\n            self.wfile.write(body)\n            return\n        return self.serve_static(parsed.path)"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":632,"cache_read_input_tokens":79559,"output_tokens":934,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":632,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":934,"cache_read_input_tokens":79559,"cache_creation_input_tokens":632,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":632},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n1Ya1jos4cJpFph8fX","type":"assistant","uuid":"01551f5b-3495-4087-8c5e-8107d21f539e","timestamp":"2026-06-15T21:44:51.879Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"01551f5b-3495-4087-8c5e-8107d21f539e","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LefJaQCaym7ztcYksqSwQt","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/agent_gui/app.py has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"0ccc495a-274b-4659-ac3d-edb892b40044","timestamp":"2026-06-15T21:44:51.896Z","toolUseResult":{"filePath":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py","oldString":"    def do_GET(self):\n        parsed = urlparse(self.path)\n        if parsed.path == \"/health\":\n            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n        if parsed.path == \"/api/agents\":\n            return self.json_response({\"agents\": self.server.agents})\n        if parsed.path == \"/api/avatar\":\n            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n        if parsed.path == \"/api/sessions\":\n            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n        if parsed.path == \"/api/history\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n        return self.serve_static(parsed.path)","newString":"    def do_GET(self):\n        parsed = urlparse(self.path)\n        if parsed.path == \"/health\":\n            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n        if parsed.path == \"/api/agents\":\n            return self.json_response({\"agents\": self.server.agents})\n        if parsed.path == \"/api/avatar\":\n            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n        if parsed.path == \"/api/sessions\":\n            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n        if parsed.path == \"/api/history\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n        if parsed.path == \"/api/avatar/anim\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            with _anim_lock:\n                val = _anim_store.get(session_id)\n            if val is None:\n                return self.json_response({\"status\": \"pending\"})\n            if isinstance(val, str):\n                return self.json_response({\"status\": \"error\", \"message\": val})\n            return self.json_response({\"status\": \"ready\", \"bsData\": val})\n        if parsed.path == \"/api/avatar/audio\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            wav = TTS_DIR / f\"{session_id}.wav\"\n            if not wav.exists():\n                return self.json_response({\"error\": \"audio not found\"}, status=404)\n            body = wav.read_bytes()\n            self.send_response(200)\n            self.send_header(\"Content-Type\", \"audio/wav\")\n            self.send_header(\"Content-Length\", str(len(body)))\n            self.end_headers()\n            self.wfile.write(body)\n            return\n        return self.serve_static(parsed.path)","originalFile":"from __future__ import annotations\n\nimport argparse\nimport json\nimport mimetypes\nimport threading\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .adapters import adapter_for\nfrom .avatar import available_states, get_avatar\nfrom .config import find_agent, load_agents\nfrom .sessions import SessionStore, new_session_id, utc_now\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nSTATIC_ROOT = PROJECT_ROOT / \"static\"\nDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\nDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"\nTTS_DIR = PROJECT_ROOT / \"data\" / \"tts\"\nTTS_DIR.mkdir(parents=True, exist_ok=True)\n\n# keyed by session_id: None=pending, dict=ready, str=error message\n_anim_store: dict[str, None | dict | str] = {}\n_anim_lock = threading.Lock()\n\n\nclass AgentGuiServer(ThreadingHTTPServer):\n    def __init__(self, server_address, RequestHandlerClass, config_path: Path, sessions_root: Path):\n        super().__init__(server_address, RequestHandlerClass)\n        self.config_path = config_path\n        self.sessions = SessionStore(sessions_root)\n\n    @property\n    def agents(self):\n        return load_agents(self.config_path)\n\n\nclass Handler(BaseHTTPRequestHandler):\n    server: AgentGuiServer\n\n    def log_message(self, format: str, *args):\n        print(\"[agent-gui] \" + format % args)\n\n    def do_GET(self):\n        parsed = urlparse(self.path)\n        if parsed.path == \"/health\":\n            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n        if parsed.path == \"/api/agents\":\n            return self.json_response({\"agents\": self.server.agents})\n        if parsed.path == \"/api/avatar\":\n            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n        if parsed.path == \"/api/sessions\":\n            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n        if parsed.path == \"/api/history\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n        return self.serve_static(parsed.path)\n\n    def do_POST(self):\n        if self.path == \"/api/session\":\n            return self.json_response({\"session_id\": new_session_id()})\n        if self.path == \"/api/chat\":\n            return self.handle_chat()\n        return self.json_response({\"error\": \"Endpoint inconnu\"}, status=404)\n\n    def handle_chat(self):\n        try:\n            payload = self.read_json()\n            agent_id = str(payload.get(\"agent\", \"echo\"))\n            message = str(payload.get(\"message\", \"\")).strip()\n            session_id = str(payload.get(\"session_id\") or new_session_id())\n            if not message:\n                return self.json_response({\"error\": \"Message vide\"}, status=400)\n\n            agent = find_agent(self.server.agents, agent_id)\n            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n\n            result = adapter_for(agent).run(message, agent, session_id)\n            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n            return self.json_response(\n                {\n                    \"session_id\": session_id,\n                    \"agent\": agent_id,\n                    \"events\": stored_events,\n                    \"avatar\": get_avatar(result.avatar_state),\n                }\n            )\n        except KeyError as exc:\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n        except Exception as exc:  # defensive boundary for local UI\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)\n\n    def serve_static(self, request_path: str):\n        relative = \"index.html\" if request_path in (\"/\", \"\") else request_path.lstrip(\"/\")\n        path = (STATIC_ROOT / relative).resolve()\n        if STATIC_ROOT.resolve() not in path.parents and path != STATIC_ROOT.resolve():\n            return self.json_response({\"error\": \"Chemin interdit\"}, status=403)\n        if not path.exists() or not path.is_file():\n            return self.json_response({\"error\": \"Fichier introuvable\"}, status=404)\n        content_type = mimetypes.guess_type(str(path))[0] or \"application/octet-stream\"\n        body = path.read_bytes()\n        self.send_response(200)\n        self.send_header(\"Content-Type\", content_type)\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n    def read_json(self):\n        length = int(self.headers.get(\"Content-Length\", \"0\"))\n        body = self.rfile.read(length).decode(\"utf-8\") if length else \"{}\"\n        return json.loads(body)\n\n    def json_response(self, payload, status: int = 200):\n        body = json.dumps(payload, ensure_ascii=False, indent=2).encode(\"utf-8\")\n        self.send_response(status)\n        self.send_header(\"Content-Type\", \"application/json; charset=utf-8\")\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n\ndef main(argv: list[str] | None = None):\n    parser = argparse.ArgumentParser(description=\"Agent GUI local\")\n    parser.add_argument(\"--host\", default=\"127.0.0.1\")\n    parser.add_argument(\"--port\", type=int, default=8765)\n    parser.add_argument(\"--config\", type=Path, default=DEFAULT_CONFIG)\n    parser.add_argument(\"--sessions\", type=Path, default=DEFAULT_SESSIONS)\n    args = parser.parse_args(argv)\n\n    server = AgentGuiServer((args.host, args.port), Handler, args.config, args.sessions)\n    print(f\"Agent GUI lancé: http://{args.host}:{args.port}\")\n    print(f\"Config agents: {args.config}\")\n    print(\"Ctrl+C pour arrêter.\")\n    server.serve_forever()\n\n\nif __name__ == \"__main__\":\n    main()\n","structuredPatch":[{"oldStart":56,"oldLines":6,"newStart":56,"newLines":27,"lines":["         if parsed.path == \"/api/history\":","             session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]","             return self.json_response({\"events\": self.server.sessions.read(session_id)})","+        if parsed.path == \"/api/avatar/anim\":","+            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]","+            with _anim_lock:","+                val = _anim_store.get(session_id)","+            if val is None:","+                return self.json_response({\"status\": \"pending\"})","+            if isinstance(val, str):","+                return self.json_response({\"status\": \"error\", \"message\": val})","+            return self.json_response({\"status\": \"ready\", \"bsData\": val})","+        if parsed.path == \"/api/avatar/audio\":","+            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]","+            wav = TTS_DIR / f\"{session_id}.wav\"","+            if not wav.exists():","+                return self.json_response({\"error\": \"audio not found\"}, status=404)","+            body = wav.read_bytes()","+            self.send_response(200)","+            self.send_header(\"Content-Type\", \"audio/wav\")","+            self.send_header(\"Content-Length\", str(len(body)))","+            self.end_headers()","+            self.wfile.write(body)","+            return","         return self.serve_static(parsed.path)"," ","     def do_POST(self):"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"01551f5b-3495-4087-8c5e-8107d21f539e","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"0ccc495a-274b-4659-ac3d-edb892b40044","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_0166SQeuirxmvks1iWoGaht1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0168z761dzdpLzK5LB57D2H3","name":"Edit","input":{"replace_all":false,"file_path":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py","old_string":"    def handle_chat(self):\n        try:\n            payload = self.read_json()\n            agent_id = str(payload.get(\"agent\", \"echo\"))\n            message = str(payload.get(\"message\", \"\")).strip()\n            session_id = str(payload.get(\"session_id\") or new_session_id())\n            if not message:\n                return self.json_response({\"error\": \"Message vide\"}, status=400)\n\n            agent = find_agent(self.server.agents, agent_id)\n            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n\n            result = adapter_for(agent).run(message, agent, session_id)\n            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n            return self.json_response(\n                {\n                    \"session_id\": session_id,\n                    \"agent\": agent_id,\n                    \"events\": stored_events,\n                    \"avatar\": get_avatar(result.avatar_state),\n                }\n            )\n        except KeyError as exc:\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n        except Exception as exc:  # defensive boundary for local UI\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)","new_string":"    def handle_chat(self):\n        try:\n            payload = self.read_json()\n            agent_id = str(payload.get(\"agent\", \"echo\"))\n            message = str(payload.get(\"message\", \"\")).strip()\n            session_id = str(payload.get(\"session_id\") or new_session_id())\n            if not message:\n                return self.json_response({\"error\": \"Message vide\"}, status=400)\n\n            agent = find_agent(self.server.agents, agent_id)\n            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n\n            result = adapter_for(agent).run(message, agent, session_id)\n            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n\n            assistant_text = next(\n                (e[\"content\"] for e in stored_events if e.get(\"type\") == \"assistant_message\"),\n                None,\n            )\n            if assistant_text:\n                _start_avatar_pipeline(session_id, assistant_text)\n\n            return self.json_response(\n                {\n                    \"session_id\": session_id,\n                    \"agent\": agent_id,\n                    \"events\": stored_events,\n                    \"avatar\": get_avatar(result.avatar_state),\n                    \"avatar_anim\": \"pending\" if assistant_text else None,\n                }\n            )\n        except KeyError as exc:\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n        except Exception as exc:  # defensive boundary for local UI\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1007,"cache_read_input_tokens":80191,"output_tokens":995,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1007,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":995,"cache_read_input_tokens":80191,"cache_creation_input_tokens":1007,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1007},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n2Ukbpm4mXieq1tSjE","type":"assistant","uuid":"a0a8627d-cbc5-4fae-ac04-44601bcbda6e","timestamp":"2026-06-15T21:45:03.272Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a0a8627d-cbc5-4fae-ac04-44601bcbda6e","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0168z761dzdpLzK5LB57D2H3","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/agent_gui/app.py has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"97b358cf-5043-4cd1-b892-e9199080f577","timestamp":"2026-06-15T21:45:03.305Z","toolUseResult":{"filePath":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py","oldString":"    def handle_chat(self):\n        try:\n            payload = self.read_json()\n            agent_id = str(payload.get(\"agent\", \"echo\"))\n            message = str(payload.get(\"message\", \"\")).strip()\n            session_id = str(payload.get(\"session_id\") or new_session_id())\n            if not message:\n                return self.json_response({\"error\": \"Message vide\"}, status=400)\n\n            agent = find_agent(self.server.agents, agent_id)\n            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n\n            result = adapter_for(agent).run(message, agent, session_id)\n            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n            return self.json_response(\n                {\n                    \"session_id\": session_id,\n                    \"agent\": agent_id,\n                    \"events\": stored_events,\n                    \"avatar\": get_avatar(result.avatar_state),\n                }\n            )\n        except KeyError as exc:\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n        except Exception as exc:  # defensive boundary for local UI\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)","newString":"    def handle_chat(self):\n        try:\n            payload = self.read_json()\n            agent_id = str(payload.get(\"agent\", \"echo\"))\n            message = str(payload.get(\"message\", \"\")).strip()\n            session_id = str(payload.get(\"session_id\") or new_session_id())\n            if not message:\n                return self.json_response({\"error\": \"Message vide\"}, status=400)\n\n            agent = find_agent(self.server.agents, agent_id)\n            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n\n            result = adapter_for(agent).run(message, agent, session_id)\n            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n\n            assistant_text = next(\n                (e[\"content\"] for e in stored_events if e.get(\"type\") == \"assistant_message\"),\n                None,\n            )\n            if assistant_text:\n                _start_avatar_pipeline(session_id, assistant_text)\n\n            return self.json_response(\n                {\n                    \"session_id\": session_id,\n                    \"agent\": agent_id,\n                    \"events\": stored_events,\n                    \"avatar\": get_avatar(result.avatar_state),\n                    \"avatar_anim\": \"pending\" if assistant_text else None,\n                }\n            )\n        except KeyError as exc:\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n        except Exception as exc:  # defensive boundary for local UI\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)","originalFile":"from __future__ import annotations\n\nimport argparse\nimport json\nimport mimetypes\nimport threading\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .adapters import adapter_for\nfrom .avatar import available_states, get_avatar\nfrom .config import find_agent, load_agents\nfrom .sessions import SessionStore, new_session_id, utc_now\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nSTATIC_ROOT = PROJECT_ROOT / \"static\"\nDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\nDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"\nTTS_DIR = PROJECT_ROOT / \"data\" / \"tts\"\nTTS_DIR.mkdir(parents=True, exist_ok=True)\n\n# keyed by session_id: None=pending, dict=ready, str=error message\n_anim_store: dict[str, None | dict | str] = {}\n_anim_lock = threading.Lock()\n\n\nclass AgentGuiServer(ThreadingHTTPServer):\n    def __init__(self, server_address, RequestHandlerClass, config_path: Path, sessions_root: Path):\n        super().__init__(server_address, RequestHandlerClass)\n        self.config_path = config_path\n        self.sessions = SessionStore(sessions_root)\n\n    @property\n    def agents(self):\n        return load_agents(self.config_path)\n\n\nclass Handler(BaseHTTPRequestHandler):\n    server: AgentGuiServer\n\n    def log_message(self, format: str, *args):\n        print(\"[agent-gui] \" + format % args)\n\n    def do_GET(self):\n        parsed = urlparse(self.path)\n        if parsed.path == \"/health\":\n            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n        if parsed.path == \"/api/agents\":\n            return self.json_response({\"agents\": self.server.agents})\n        if parsed.path == \"/api/avatar\":\n            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n        if parsed.path == \"/api/sessions\":\n            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n        if parsed.path == \"/api/history\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n        if parsed.path == \"/api/avatar/anim\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            with _anim_lock:\n                val = _anim_store.get(session_id)\n            if val is None:\n                return self.json_response({\"status\": \"pending\"})\n            if isinstance(val, str):\n                return self.json_response({\"status\": \"error\", \"message\": val})\n            return self.json_response({\"status\": \"ready\", \"bsData\": val})\n        if parsed.path == \"/api/avatar/audio\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            wav = TTS_DIR / f\"{session_id}.wav\"\n            if not wav.exists():\n                return self.json_response({\"error\": \"audio not found\"}, status=404)\n            body = wav.read_bytes()\n            self.send_response(200)\n            self.send_header(\"Content-Type\", \"audio/wav\")\n            self.send_header(\"Content-Length\", str(len(body)))\n            self.end_headers()\n            self.wfile.write(body)\n            return\n        return self.serve_static(parsed.path)\n\n    def do_POST(self):\n        if self.path == \"/api/session\":\n            return self.json_response({\"session_id\": new_session_id()})\n        if self.path == \"/api/chat\":\n            return self.handle_chat()\n        return self.json_response({\"error\": \"Endpoint inconnu\"}, status=404)\n\n    def handle_chat(self):\n        try:\n            payload = self.read_json()\n            agent_id = str(payload.get(\"agent\", \"echo\"))\n            message = str(payload.get(\"message\", \"\")).strip()\n            session_id = str(payload.get(\"session_id\") or new_session_id())\n            if not message:\n                return self.json_response({\"error\": \"Message vide\"}, status=400)\n\n            agent = find_agent(self.server.agents, agent_id)\n            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n\n            result = adapter_for(agent).run(message, agent, session_id)\n            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n            return self.json_response(\n                {\n                    \"session_id\": session_id,\n                    \"agent\": agent_id,\n                    \"events\": stored_events,\n                    \"avatar\": get_avatar(result.avatar_state),\n                }\n            )\n        except KeyError as exc:\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n        except Exception as exc:  # defensive boundary for local UI\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)\n\n    def serve_static(self, request_path: str):\n        relative = \"index.html\" if request_path in (\"/\", \"\") else request_path.lstrip(\"/\")\n        path = (STATIC_ROOT / relative).resolve()\n        if STATIC_ROOT.resolve() not in path.parents and path != STATIC_ROOT.resolve():\n            return self.json_response({\"error\": \"Chemin interdit\"}, status=403)\n        if not path.exists() or not path.is_file():\n            return self.json_response({\"error\": \"Fichier introuvable\"}, status=404)\n        content_type = mimetypes.guess_type(str(path))[0] or \"application/octet-stream\"\n        body = path.read_bytes()\n        self.send_response(200)\n        self.send_header(\"Content-Type\", content_type)\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n    def read_json(self):\n        length = int(self.headers.get(\"Content-Length\", \"0\"))\n        body = self.rfile.read(length).decode(\"utf-8\") if length else \"{}\"\n        return json.loads(body)\n\n    def json_response(self, payload, status: int = 200):\n        body = json.dumps(payload, ensure_ascii=False, indent=2).encode(\"utf-8\")\n        self.send_response(status)\n        self.send_header(\"Content-Type\", \"application/json; charset=utf-8\")\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n\ndef main(argv: list[str] | None = None):\n    parser = argparse.ArgumentParser(description=\"Agent GUI local\")\n    parser.add_argument(\"--host\", default=\"127.0.0.1\")\n    parser.add_argument(\"--port\", type=int, default=8765)\n    parser.add_argument(\"--config\", type=Path, default=DEFAULT_CONFIG)\n    parser.add_argument(\"--sessions\", type=Path, default=DEFAULT_SESSIONS)\n    args = parser.parse_args(argv)\n\n    server = AgentGuiServer((args.host, args.port), Handler, args.config, args.sessions)\n    print(f\"Agent GUI lancé: http://{args.host}:{args.port}\")\n    print(f\"Config agents: {args.config}\")\n    print(\"Ctrl+C pour arrêter.\")\n    server.serve_forever()\n\n\nif __name__ == \"__main__\":\n    main()\n","structuredPatch":[{"oldStart":101,"oldLines":12,"newStart":101,"newLines":21,"lines":[" ","             result = adapter_for(agent).run(message, agent, session_id)","             stored_events = [self.server.sessions.append(session_id, event) for event in result.events]","+","+            assistant_text = next(","+                (e[\"content\"] for e in stored_events if e.get(\"type\") == \"assistant_message\"),","+                None,","+            )","+            if assistant_text:","+                _start_avatar_pipeline(session_id, assistant_text)","+","             return self.json_response(","                 {","                     \"session_id\": session_id,","                     \"agent\": agent_id,","                     \"events\": stored_events,","                     \"avatar\": get_avatar(result.avatar_state),","+                    \"avatar_anim\": \"pending\" if assistant_text else None,","                 }","             )","         except KeyError as exc:"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"a0a8627d-cbc5-4fae-ac04-44601bcbda6e","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"cool ca fonctionne, implemente ça sur mon gui d'avatar","leafUuid":"97b358cf-5043-4cd1-b892-e9199080f577","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"permission-mode","permissionMode":"bypassPermissions","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"parentUuid":"97b358cf-5043-4cd1-b892-e9199080f577","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01NbDFad5gLVqUCMmQ7RynzL","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant j'ajoute la fonction `_start_avatar_pipeline` avant la classe `Handler`."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1049,"cache_read_input_tokens":81198,"output_tokens":389,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1049,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":389,"cache_read_input_tokens":81198,"cache_creation_input_tokens":1049,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1049},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n3KYVRJjr9NyKD661t","type":"assistant","uuid":"13383e7d-69c1-47c6-9f17-ab33558aa50b","timestamp":"2026-06-15T21:45:05.788Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"13383e7d-69c1-47c6-9f17-ab33558aa50b","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01NbDFad5gLVqUCMmQ7RynzL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Y8nxXA67rYMTZ7VKmZHtDW","name":"Edit","input":{"replace_all":false,"file_path":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py","old_string":"class AgentGuiServer(ThreadingHTTPServer):","new_string":"def _start_avatar_pipeline(session_id: str, text: str) -> None:\n    with _anim_lock:\n        _anim_store[session_id] = None  # pending\n\n    def _run() -> None:\n        try:\n            from .tts import synthesize\n            from .avatar_lam import audio_to_blendshapes\n\n            wav_path = TTS_DIR / f\"{session_id}.wav\"\n            synthesize(text, wav_path)\n            bs_data = audio_to_blendshapes(wav_path)\n            with _anim_lock:\n                _anim_store[session_id] = bs_data\n        except Exception as exc:\n            with _anim_lock:\n                _anim_store[session_id] = str(exc)\n\n    threading.Thread(target=_run, daemon=True).start()\n\n\nclass AgentGuiServer(ThreadingHTTPServer):"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1049,"cache_read_input_tokens":81198,"output_tokens":389,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1049,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":389,"cache_read_input_tokens":81198,"cache_creation_input_tokens":1049,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1049},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n3KYVRJjr9NyKD661t","type":"assistant","uuid":"b2fc0ae3-8d8f-4dac-83ab-4754abfc6331","timestamp":"2026-06-15T21:45:09.587Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"b2fc0ae3-8d8f-4dac-83ab-4754abfc6331","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Y8nxXA67rYMTZ7VKmZHtDW","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/agent_gui/app.py has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"1bb83bac-0a9c-41f0-aa51-ff8e327dd6a0","timestamp":"2026-06-15T21:45:09.606Z","toolUseResult":{"filePath":"/mnt/DATA/WORK/agent-gui/agent_gui/app.py","oldString":"class AgentGuiServer(ThreadingHTTPServer):","newString":"def _start_avatar_pipeline(session_id: str, text: str) -> None:\n    with _anim_lock:\n        _anim_store[session_id] = None  # pending\n\n    def _run() -> None:\n        try:\n            from .tts import synthesize\n            from .avatar_lam import audio_to_blendshapes\n\n            wav_path = TTS_DIR / f\"{session_id}.wav\"\n            synthesize(text, wav_path)\n            bs_data = audio_to_blendshapes(wav_path)\n            with _anim_lock:\n                _anim_store[session_id] = bs_data\n        except Exception as exc:\n            with _anim_lock:\n                _anim_store[session_id] = str(exc)\n\n    threading.Thread(target=_run, daemon=True).start()\n\n\nclass AgentGuiServer(ThreadingHTTPServer):","originalFile":"from __future__ import annotations\n\nimport argparse\nimport json\nimport mimetypes\nimport threading\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlparse\n\nfrom .adapters import adapter_for\nfrom .avatar import available_states, get_avatar\nfrom .config import find_agent, load_agents\nfrom .sessions import SessionStore, new_session_id, utc_now\n\nPROJECT_ROOT = Path(__file__).resolve().parent.parent\nSTATIC_ROOT = PROJECT_ROOT / \"static\"\nDEFAULT_CONFIG = PROJECT_ROOT / \"configs\" / \"agents.json\"\nDEFAULT_SESSIONS = PROJECT_ROOT / \"data\" / \"sessions\"\nTTS_DIR = PROJECT_ROOT / \"data\" / \"tts\"\nTTS_DIR.mkdir(parents=True, exist_ok=True)\n\n# keyed by session_id: None=pending, dict=ready, str=error message\n_anim_store: dict[str, None | dict | str] = {}\n_anim_lock = threading.Lock()\n\n\nclass AgentGuiServer(ThreadingHTTPServer):\n    def __init__(self, server_address, RequestHandlerClass, config_path: Path, sessions_root: Path):\n        super().__init__(server_address, RequestHandlerClass)\n        self.config_path = config_path\n        self.sessions = SessionStore(sessions_root)\n\n    @property\n    def agents(self):\n        return load_agents(self.config_path)\n\n\nclass Handler(BaseHTTPRequestHandler):\n    server: AgentGuiServer\n\n    def log_message(self, format: str, *args):\n        print(\"[agent-gui] \" + format % args)\n\n    def do_GET(self):\n        parsed = urlparse(self.path)\n        if parsed.path == \"/health\":\n            return self.json_response({\"ok\": True, \"service\": \"agent-gui\", \"time\": utc_now()})\n        if parsed.path == \"/api/agents\":\n            return self.json_response({\"agents\": self.server.agents})\n        if parsed.path == \"/api/avatar\":\n            state = parse_qs(parsed.query).get(\"state\", [\"idle\"])[0]\n            return self.json_response({\"avatar\": get_avatar(state), \"states\": available_states()})\n        if parsed.path == \"/api/sessions\":\n            return self.json_response({\"sessions\": self.server.sessions.list_sessions()})\n        if parsed.path == \"/api/history\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            return self.json_response({\"events\": self.server.sessions.read(session_id)})\n        if parsed.path == \"/api/avatar/anim\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            with _anim_lock:\n                val = _anim_store.get(session_id)\n            if val is None:\n                return self.json_response({\"status\": \"pending\"})\n            if isinstance(val, str):\n                return self.json_response({\"status\": \"error\", \"message\": val})\n            return self.json_response({\"status\": \"ready\", \"bsData\": val})\n        if parsed.path == \"/api/avatar/audio\":\n            session_id = parse_qs(parsed.query).get(\"session_id\", [\"\"])[0]\n            wav = TTS_DIR / f\"{session_id}.wav\"\n            if not wav.exists():\n                return self.json_response({\"error\": \"audio not found\"}, status=404)\n            body = wav.read_bytes()\n            self.send_response(200)\n            self.send_header(\"Content-Type\", \"audio/wav\")\n            self.send_header(\"Content-Length\", str(len(body)))\n            self.end_headers()\n            self.wfile.write(body)\n            return\n        return self.serve_static(parsed.path)\n\n    def do_POST(self):\n        if self.path == \"/api/session\":\n            return self.json_response({\"session_id\": new_session_id()})\n        if self.path == \"/api/chat\":\n            return self.handle_chat()\n        return self.json_response({\"error\": \"Endpoint inconnu\"}, status=404)\n\n    def handle_chat(self):\n        try:\n            payload = self.read_json()\n            agent_id = str(payload.get(\"agent\", \"echo\"))\n            message = str(payload.get(\"message\", \"\")).strip()\n            session_id = str(payload.get(\"session_id\") or new_session_id())\n            if not message:\n                return self.json_response({\"error\": \"Message vide\"}, status=400)\n\n            agent = find_agent(self.server.agents, agent_id)\n            self.server.sessions.append(session_id, {\"type\": \"user_message\", \"agent\": agent_id, \"content\": message})\n            self.server.sessions.append(session_id, {\"type\": \"status\", \"agent\": agent_id, \"content\": \"Agent en réflexion...\"})\n\n            result = adapter_for(agent).run(message, agent, session_id)\n            stored_events = [self.server.sessions.append(session_id, event) for event in result.events]\n\n            assistant_text = next(\n                (e[\"content\"] for e in stored_events if e.get(\"type\") == \"assistant_message\"),\n                None,\n            )\n            if assistant_text:\n                _start_avatar_pipeline(session_id, assistant_text)\n\n            return self.json_response(\n                {\n                    \"session_id\": session_id,\n                    \"agent\": agent_id,\n                    \"events\": stored_events,\n                    \"avatar\": get_avatar(result.avatar_state),\n                    \"avatar_anim\": \"pending\" if assistant_text else None,\n                }\n            )\n        except KeyError as exc:\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=404)\n        except Exception as exc:  # defensive boundary for local UI\n            return self.json_response({\"error\": str(exc), \"avatar\": get_avatar(\"error\")}, status=500)\n\n    def serve_static(self, request_path: str):\n        relative = \"index.html\" if request_path in (\"/\", \"\") else request_path.lstrip(\"/\")\n        path = (STATIC_ROOT / relative).resolve()\n        if STATIC_ROOT.resolve() not in path.parents and path != STATIC_ROOT.resolve():\n            return self.json_response({\"error\": \"Chemin interdit\"}, status=403)\n        if not path.exists() or not path.is_file():\n            return self.json_response({\"error\": \"Fichier introuvable\"}, status=404)\n        content_type = mimetypes.guess_type(str(path))[0] or \"application/octet-stream\"\n        body = path.read_bytes()\n        self.send_response(200)\n        self.send_header(\"Content-Type\", content_type)\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n    def read_json(self):\n        length = int(self.headers.get(\"Content-Length\", \"0\"))\n        body = self.rfile.read(length).decode(\"utf-8\") if length else \"{}\"\n        return json.loads(body)\n\n    def json_response(self, payload, status: int = 200):\n        body = json.dumps(payload, ensure_ascii=False, indent=2).encode(\"utf-8\")\n        self.send_response(status)\n        self.send_header(\"Content-Type\", \"application/json; charset=utf-8\")\n        self.send_header(\"Content-Length\", str(len(body)))\n        self.end_headers()\n        self.wfile.write(body)\n\n\ndef main(argv: list[str] | None = None):\n    parser = argparse.ArgumentParser(description=\"Agent GUI local\")\n    parser.add_argument(\"--host\", default=\"127.0.0.1\")\n    parser.add_argument(\"--port\", type=int, default=8765)\n    parser.add_argument(\"--config\", type=Path, default=DEFAULT_CONFIG)\n    parser.add_argument(\"--sessions\", type=Path, default=DEFAULT_SESSIONS)\n    args = parser.parse_args(argv)\n\n    server = AgentGuiServer((args.host, args.port), Handler, args.config, args.sessions)\n    print(f\"Agent GUI lancé: http://{args.host}:{args.port}\")\n    print(f\"Config agents: {args.config}\")\n    print(\"Ctrl+C pour arrêter.\")\n    server.serve_forever()\n\n\nif __name__ == \"__main__\":\n    main()\n","structuredPatch":[{"oldStart":25,"oldLines":6,"newStart":25,"newLines":27,"lines":[" _anim_lock = threading.Lock()"," "," ","+def _start_avatar_pipeline(session_id: str, text: str) -> None:","+    with _anim_lock:","+        _anim_store[session_id] = None  # pending","+","+    def _run() -> None:","+        try:","+            from .tts import synthesize","+            from .avatar_lam import audio_to_blendshapes","+","+            wav_path = TTS_DIR / f\"{session_id}.wav\"","+            synthesize(text, wav_path)","+            bs_data = audio_to_blendshapes(wav_path)","+            with _anim_lock:","+                _anim_store[session_id] = bs_data","+        except Exception as exc:","+            with _anim_lock:","+                _anim_store[session_id] = str(exc)","+","+    threading.Thread(target=_run, daemon=True).start()","+","+"," class AgentGuiServer(ThreadingHTTPServer):","     def __init__(self, server_address, RequestHandlerClass, config_path: Path, sessions_root: Path):","         super().__init__(server_address, RequestHandlerClass)"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"b2fc0ae3-8d8f-4dac-83ab-4754abfc6331","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"1bb83bac-0a9c-41f0-aa51-ff8e327dd6a0","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01SKz7tDQpUxkirec3ByVVnJ","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant le frontend. D'abord `avatar.js` avec Three.js."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":443,"cache_read_input_tokens":82247,"output_tokens":1529,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":443,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":1529,"cache_read_input_tokens":82247,"cache_creation_input_tokens":443,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":443},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n3nkMVoXkhjnCeEwce","type":"assistant","uuid":"ce004f04-ee4b-4eaf-9a2f-ba231924e5e2","timestamp":"2026-06-15T21:45:13.127Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"ce004f04-ee4b-4eaf-9a2f-ba231924e5e2","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01SKz7tDQpUxkirec3ByVVnJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01T9A2dB1WrKEmDamDmT5xib","name":"Write","input":{"file_path":"/mnt/DATA/WORK/agent-gui/static/avatar.js","content":"import * as THREE from 'three';\nimport { GLTFLoader } from 'three/addons/loaders/GLTFLoader.js';\n\nconst AVATAR_GLB = '/avatar/barbara/skin.glb';\nconst FPS = 30;\n\nlet renderer, scene, camera, avatarMesh, morphDict, morphNames;\nlet frames = [], frameIndex = 0, animTimer = null;\nlet audioCtx = null, audioBuffer = null, audioSource = null;\n\nfunction init() {\n  const canvas = document.getElementById('avatarCanvas');\n  if (!canvas) return;\n\n  renderer = new THREE.WebGLRenderer({ canvas, antialias: true, alpha: true });\n  renderer.setPixelRatio(window.devicePixelRatio);\n  renderer.setSize(canvas.clientWidth, canvas.clientHeight, false);\n  renderer.outputColorSpace = THREE.SRGBColorSpace;\n\n  scene = new THREE.Scene();\n\n  camera = new THREE.PerspectiveCamera(35, canvas.clientWidth / canvas.clientHeight, 0.01, 100);\n  camera.position.set(0, 0, 3);\n\n  const ambient = new THREE.AmbientLight(0xffffff, 1.2);\n  scene.add(ambient);\n  const dir = new THREE.DirectionalLight(0xffffff, 1.5);\n  dir.position.set(1, 2, 3);\n  scene.add(dir);\n\n  loadAvatar();\n  renderLoop();\n}\n\nfunction loadAvatar() {\n  const loader = new GLTFLoader();\n  loader.load(AVATAR_GLB, (gltf) => {\n    const root = gltf.scene;\n    scene.add(root);\n\n    root.traverse((node) => {\n      if (node.isMesh && node.morphTargetDictionary && Object.keys(node.morphTargetDictionary).length > 0) {\n        avatarMesh = node;\n        morphDict = node.morphTargetDictionary;\n      }\n    });\n\n    fitCamera(root);\n    setStatus('Prêt');\n  }, undefined, (err) => {\n    setStatus('Erreur chargement avatar');\n    console.error(err);\n  });\n}\n\nfunction fitCamera(object) {\n  const box = new THREE.Box3().setFromObject(object);\n  const center = box.getCenter(new THREE.Vector3());\n  const size = box.getSize(new THREE.Vector3());\n  const maxDim = Math.max(size.x, size.y);\n  const fovRad = camera.fov * Math.PI / 180;\n  const dist = (maxDim / 2) / Math.tan(fovRad / 2);\n\n  // Focus on upper half (face/head)\n  camera.position.set(center.x, center.y + size.y * 0.15, center.z + dist * 1.1);\n  camera.lookAt(center.x, center.y + size.y * 0.15, center.z);\n  camera.near = dist / 100;\n  camera.far = dist * 100;\n  camera.updateProjectionMatrix();\n}\n\nfunction applyFrame(frame) {\n  if (!avatarMesh || !morphDict || !frame) return;\n  const weights = frame.weights;\n  for (let i = 0; i < morphNames.length; i++) {\n    const name = morphNames[i];\n    if (name in morphDict) {\n      avatarMesh.morphTargetInfluences[morphDict[name]] = weights[i] ?? 0;\n    }\n  }\n}\n\nfunction resetMorphs() {\n  if (!avatarMesh) return;\n  avatarMesh.morphTargetInfluences.fill(0);\n}\n\nfunction renderLoop() {\n  requestAnimationFrame(renderLoop);\n  renderer.render(scene, camera);\n}\n\nfunction setStatus(text) {\n  const el = document.getElementById('avatarStatus');\n  if (el) el.textContent = text;\n}\n\nasync function loadAudio(url) {\n  if (!audioCtx) audioCtx = new AudioContext();\n  const res = await fetch(url);\n  const buf = await res.arrayBuffer();\n  audioBuffer = await audioCtx.decodeAudioData(buf);\n}\n\nfunction playAudio() {\n  if (!audioCtx || !audioBuffer) return;\n  if (audioSource) { try { audioSource.stop(); } catch (_) {} }\n  audioSource = audioCtx.createBufferSource();\n  audioSource.buffer = audioBuffer;\n  audioSource.connect(audioCtx.destination);\n  audioSource.start(0);\n}\n\nfunction startAnimation(bsData) {\n  if (animTimer) clearInterval(animTimer);\n  morphNames = bsData.metadata.blendshape_names;\n  frames = bsData.frames;\n  frameIndex = 0;\n  setStatus('Animation...');\n\n  animTimer = setInterval(() => {\n    if (frameIndex >= frames.length) {\n      clearInterval(animTimer);\n      animTimer = null;\n      resetMorphs();\n      setStatus('Prêt');\n      return;\n    }\n    applyFrame(frames[frameIndex++]);\n  }, 1000 / FPS);\n}\n\nasync function speakWithAnimation(sessionId) {\n  setStatus('Génération audio...');\n  try {\n    await loadAudio(`/api/avatar/audio?session_id=${encodeURIComponent(sessionId)}`);\n    playAudio();\n  } catch (e) {\n    console.warn('Audio load failed:', e);\n  }\n}\n\n// Exported controller\nwindow.avatarController = { init, startAnimation, speakWithAnimation, setStatus };\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":443,"cache_read_input_tokens":82247,"output_tokens":1529,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":443,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":1529,"cache_read_input_tokens":82247,"cache_creation_input_tokens":443,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":443},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n3nkMVoXkhjnCeEwce","type":"assistant","uuid":"25fd137e-f024-46ca-a580-437c993268eb","timestamp":"2026-06-15T21:45:30.069Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"25fd137e-f024-46ca-a580-437c993268eb","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01T9A2dB1WrKEmDamDmT5xib","type":"tool_result","content":"File created successfully at: /mnt/DATA/WORK/agent-gui/static/avatar.js (file state is current in your context — no need to Read it back)"}]},"uuid":"4a777034-4a3e-49ca-879c-4aa68afdd4af","timestamp":"2026-06-15T21:45:30.104Z","toolUseResult":{"type":"create","filePath":"/mnt/DATA/WORK/agent-gui/static/avatar.js","content":"import * as THREE from 'three';\nimport { GLTFLoader } from 'three/addons/loaders/GLTFLoader.js';\n\nconst AVATAR_GLB = '/avatar/barbara/skin.glb';\nconst FPS = 30;\n\nlet renderer, scene, camera, avatarMesh, morphDict, morphNames;\nlet frames = [], frameIndex = 0, animTimer = null;\nlet audioCtx = null, audioBuffer = null, audioSource = null;\n\nfunction init() {\n  const canvas = document.getElementById('avatarCanvas');\n  if (!canvas) return;\n\n  renderer = new THREE.WebGLRenderer({ canvas, antialias: true, alpha: true });\n  renderer.setPixelRatio(window.devicePixelRatio);\n  renderer.setSize(canvas.clientWidth, canvas.clientHeight, false);\n  renderer.outputColorSpace = THREE.SRGBColorSpace;\n\n  scene = new THREE.Scene();\n\n  camera = new THREE.PerspectiveCamera(35, canvas.clientWidth / canvas.clientHeight, 0.01, 100);\n  camera.position.set(0, 0, 3);\n\n  const ambient = new THREE.AmbientLight(0xffffff, 1.2);\n  scene.add(ambient);\n  const dir = new THREE.DirectionalLight(0xffffff, 1.5);\n  dir.position.set(1, 2, 3);\n  scene.add(dir);\n\n  loadAvatar();\n  renderLoop();\n}\n\nfunction loadAvatar() {\n  const loader = new GLTFLoader();\n  loader.load(AVATAR_GLB, (gltf) => {\n    const root = gltf.scene;\n    scene.add(root);\n\n    root.traverse((node) => {\n      if (node.isMesh && node.morphTargetDictionary && Object.keys(node.morphTargetDictionary).length > 0) {\n        avatarMesh = node;\n        morphDict = node.morphTargetDictionary;\n      }\n    });\n\n    fitCamera(root);\n    setStatus('Prêt');\n  }, undefined, (err) => {\n    setStatus('Erreur chargement avatar');\n    console.error(err);\n  });\n}\n\nfunction fitCamera(object) {\n  const box = new THREE.Box3().setFromObject(object);\n  const center = box.getCenter(new THREE.Vector3());\n  const size = box.getSize(new THREE.Vector3());\n  const maxDim = Math.max(size.x, size.y);\n  const fovRad = camera.fov * Math.PI / 180;\n  const dist = (maxDim / 2) / Math.tan(fovRad / 2);\n\n  // Focus on upper half (face/head)\n  camera.position.set(center.x, center.y + size.y * 0.15, center.z + dist * 1.1);\n  camera.lookAt(center.x, center.y + size.y * 0.15, center.z);\n  camera.near = dist / 100;\n  camera.far = dist * 100;\n  camera.updateProjectionMatrix();\n}\n\nfunction applyFrame(frame) {\n  if (!avatarMesh || !morphDict || !frame) return;\n  const weights = frame.weights;\n  for (let i = 0; i < morphNames.length; i++) {\n    const name = morphNames[i];\n    if (name in morphDict) {\n      avatarMesh.morphTargetInfluences[morphDict[name]] = weights[i] ?? 0;\n    }\n  }\n}\n\nfunction resetMorphs() {\n  if (!avatarMesh) return;\n  avatarMesh.morphTargetInfluences.fill(0);\n}\n\nfunction renderLoop() {\n  requestAnimationFrame(renderLoop);\n  renderer.render(scene, camera);\n}\n\nfunction setStatus(text) {\n  const el = document.getElementById('avatarStatus');\n  if (el) el.textContent = text;\n}\n\nasync function loadAudio(url) {\n  if (!audioCtx) audioCtx = new AudioContext();\n  const res = await fetch(url);\n  const buf = await res.arrayBuffer();\n  audioBuffer = await audioCtx.decodeAudioData(buf);\n}\n\nfunction playAudio() {\n  if (!audioCtx || !audioBuffer) return;\n  if (audioSource) { try { audioSource.stop(); } catch (_) {} }\n  audioSource = audioCtx.createBufferSource();\n  audioSource.buffer = audioBuffer;\n  audioSource.connect(audioCtx.destination);\n  audioSource.start(0);\n}\n\nfunction startAnimation(bsData) {\n  if (animTimer) clearInterval(animTimer);\n  morphNames = bsData.metadata.blendshape_names;\n  frames = bsData.frames;\n  frameIndex = 0;\n  setStatus('Animation...');\n\n  animTimer = setInterval(() => {\n    if (frameIndex >= frames.length) {\n      clearInterval(animTimer);\n      animTimer = null;\n      resetMorphs();\n      setStatus('Prêt');\n      return;\n    }\n    applyFrame(frames[frameIndex++]);\n  }, 1000 / FPS);\n}\n\nasync function speakWithAnimation(sessionId) {\n  setStatus('Génération audio...');\n  try {\n    await loadAudio(`/api/avatar/audio?session_id=${encodeURIComponent(sessionId)}`);\n    playAudio();\n  } catch (e) {\n    console.warn('Audio load failed:', e);\n  }\n}\n\n// Exported controller\nwindow.avatarController = { init, startAnimation, speakWithAnimation, setStatus };\n","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"25fd137e-f024-46ca-a580-437c993268eb","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"d60a44e0-f76c-4ac1-8708-839cab93f587","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_019AmwVmZF4LnenD3FR2frpA","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant je modifie `index.html` pour ajouter l'importmap Three.js et la section avatar."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1678,"cache_read_input_tokens":82690,"output_tokens":380,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1678,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":380,"cache_read_input_tokens":82690,"cache_creation_input_tokens":1678,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1678},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n5JBgoKBnM8f4aUpER","type":"assistant","uuid":"ab24c07d-0a17-41e8-8f0f-a99b611cbbb2","timestamp":"2026-06-15T21:45:33.444Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"c92c4d29-4d1b-4417-b9f1-3f8103336b97","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PdhKZn4kQHL2bD1bbv4v3m","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/static/index.html has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"5381d72c-5dc1-4e57-a8f5-81f3f779e1cc","timestamp":"2026-06-15T21:45:36.257Z","toolUseResult":{"filePath":"/mnt/DATA/WORK/agent-gui/static/index.html","oldString":"<head>\n  <meta charset=\"utf-8\" />\n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n  <title>Agent GUI Local</title>\n  <link rel=\"stylesheet\" href=\"/styles.css\" />\n</head>","newString":"<head>\n  <meta charset=\"utf-8\" />\n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n  <title>Agent GUI Local</title>\n  <link rel=\"stylesheet\" href=\"/styles.css\" />\n  <script type=\"importmap\">\n  {\n    \"imports\": {\n      \"three\": \"https://cdn.jsdelivr.net/npm/three@0.160.0/build/three.module.js\",\n      \"three/addons/\": \"https://cdn.jsdelivr.net/npm/three@0.160.0/examples/jsm/\"\n    }\n  }\n  </script>\n</head>","originalFile":"<!doctype html>\n<html lang=\"fr\">\n<head>\n  <meta charset=\"utf-8\" />\n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n  <title>Agent GUI Local</title>\n  <link rel=\"stylesheet\" href=\"/styles.css\" />\n</head>\n<body>\n  <div class=\"scanlines\"></div>\n  <div class=\"app-shell\">\n    <aside class=\"sidebar\">\n      <div class=\"brand\">\n        <div class=\"logo\">AG</div>\n        <div>\n          <div class=\"eyebrow\">Pixel command center</div>\n          <h1>Agent GUI</h1>\n          <p>Surcouche locale universelle</p>\n        </div>\n      </div>\n\n      <label for=\"agentSelect\">Agent actif</label>\n      <select id=\"agentSelect\"></select>\n      <p id=\"agentDescription\" class=\"muted\"></p>\n\n      <div class=\"button-row\">\n        <button id=\"newSessionBtn\">Nouvelle session</button>\n        <button id=\"reloadBtn\" class=\"secondary\">Reload crew</button>\n      </div>\n\n      <section class=\"sessions\">\n        <h2>Sessions</h2>\n        <div id=\"sessionsList\"></div>\n      </section>\n    </aside>\n\n    <main class=\"center-column\">\n      <section class=\"office-card\">\n        <div class=\"card-head\">\n          <div>\n            <div class=\"eyebrow\">Virtual office</div>\n            <h2>Bureau animé</h2>\n          </div>\n          <div class=\"office-meta\">\n            <span id=\"statusPill\" class=\"pill idle\">idle</span>\n            <span id=\"sessionLabel\" class=\"session-tag\">Session</span>\n          </div>\n        </div>\n        <canvas id=\"officeCanvas\" width=\"960\" height=\"540\"></canvas>\n        <div id=\"officeCaption\" class=\"office-caption\">Les agents arrivent au bureau.</div>\n      </section>\n\n      <section class=\"chat-card\">\n        <div class=\"card-head compact\">\n          <div>\n            <div class=\"eyebrow\">Conversation</div>\n            <h2>Chat</h2>\n          </div>\n          <div class=\"muted\">Ctrl+Enter pour envoyer</div>\n        </div>\n\n        <section id=\"chatLog\" class=\"chat-log\"></section>\n\n        <form id=\"chatForm\" class=\"composer\">\n          <textarea id=\"messageInput\" rows=\"3\" placeholder=\"Écris à l'agent...\"></textarea>\n          <button type=\"submit\">Envoyer</button>\n        </form>\n      </section>\n    </main>\n\n    <aside class=\"details-column\">\n      <section class=\"panel\">\n        <div class=\"eyebrow\">Crew</div>\n        <h2>Agents présents</h2>\n        <div id=\"crewList\" class=\"crew-list\"></div>\n      </section>\n\n      <section class=\"panel\">\n        <div class=\"eyebrow\">Selected</div>\n        <h2 id=\"selectedAgentName\">Agent</h2>\n        <p id=\"selectedAgentState\" class=\"muted\">idle</p>\n        <div id=\"selectedAgentMeta\" class=\"meta-box\"></div>\n      </section>\n\n      <section class=\"panel\">\n        <div class=\"eyebrow\">Timeline</div>\n        <h2>Activity</h2>\n        <div id=\"timeline\" class=\"timeline\"></div>\n      </section>\n    </aside>\n  </div>\n\n  <script src=\"/app.js\"></script>\n</body>\n</html>\n","structuredPatch":[{"oldStart":5,"oldLines":6,"newStart":5,"newLines":14,"lines":["   <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />","   <title>Agent GUI Local</title>","   <link rel=\"stylesheet\" href=\"/styles.css\" />","+  <script type=\"importmap\">","+  {","+    \"imports\": {","+      \"three\": \"https://cdn.jsdelivr.net/npm/three@0.160.0/build/three.module.js\",","+      \"three/addons/\": \"https://cdn.jsdelivr.net/npm/three@0.160.0/examples/jsm/\"","+    }","+  }","+  </script>"," </head>"," <body>","   <div class=\"scanlines\"></div>"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"c92c4d29-4d1b-4417-b9f1-3f8103336b97","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"cool ca fonctionne, implemente ça sur mon gui d'avatar","leafUuid":"5381d72c-5dc1-4e57-a8f5-81f3f779e1cc","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"type":"last-prompt","lastPrompt":"cool ca fonctionne, implemente ça sur mon gui d'avatar","leafUuid":"2f60ce24-8372-4d99-9ba0-742f02266a1d","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"parentUuid":"2f60ce24-8372-4d99-9ba0-742f02266a1d","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01SV86UksqWPLPMC6ZYQcy64","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant j'ajoute le polling dans `app.js` — juste la partie qui se déclenche après un chat response."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":374,"cache_read_input_tokens":86618,"output_tokens":1169,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":374,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":1169,"cache_read_input_tokens":86618,"cache_creation_input_tokens":374,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":374},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n7QgfiNJo3GrbHCgXi","type":"assistant","uuid":"9145551b-9f33-48e4-9b2b-599814e69ba9","timestamp":"2026-06-15T21:46:03.036Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"9145551b-9f33-48e4-9b2b-599814e69ba9","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01SV86UksqWPLPMC6ZYQcy64","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014moL1989LLvWQEBpnjmjEs","name":"Edit","input":{"replace_all":false,"file_path":"/mnt/DATA/WORK/agent-gui/static/app.js","old_string":"async function sendMessage(event) {\n  event.preventDefault();\n  const input = $('messageInput');\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $('agentSelect').value;\n  const sessionId = await ensureSession();\n\n  input.value = '';\n  renderEvent({ type: 'user_message', content: message, agent });\n  setStatus('thinking');\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, 'thinking', '', 0);\n\n  try {\n    const data = await api('/api/chat', {\n      method: 'POST',\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $('officeCaption').textContent = data.avatar?.state === 'speaking'\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || 'speaking');\n    await loadSessions();\n  } catch (error) {\n    addMessage('error', error.message, 'erreur');\n    setStatus('error');\n    setOfficeCaption('Erreur lors de l'exécution de l'agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}","new_string":"let _animPollTimer = null;\n\nfunction pollAvatarAnim(sessionId) {\n  if (_animPollTimer) clearInterval(_animPollTimer);\n  _animPollTimer = setInterval(async () => {\n    try {\n      const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);\n      if (data.status === 'pending') return;\n      clearInterval(_animPollTimer);\n      _animPollTimer = null;\n      if (data.status === 'ready' && window.avatarController) {\n        await window.avatarController.speakWithAnimation(sessionId);\n        window.avatarController.startAnimation(data.bsData);\n      }\n      if (data.status === 'error') {\n        console.warn('Avatar anim error:', data.message);\n        if (window.avatarController) window.avatarController.setStatus('Erreur TTS/A2E');\n      }\n    } catch (_) {}\n  }, 800);\n}\n\nasync function sendMessage(event) {\n  event.preventDefault();\n  const input = $('messageInput');\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $('agentSelect').value;\n  const sessionId = await ensureSession();\n\n  input.value = '';\n  renderEvent({ type: 'user_message', content: message, agent });\n  setStatus('thinking');\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, 'thinking', '', 0);\n\n  try {\n    const data = await api('/api/chat', {\n      method: 'POST',\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $('officeCaption').textContent = data.avatar?.state === 'speaking'\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || 'speaking');\n    if (data.avatar_anim === 'pending') {\n      if (window.avatarController) window.avatarController.setStatus('Génération avatar...');\n      pollAvatarAnim(sessionId);\n    }\n    await loadSessions();\n  } catch (error) {\n    addMessage('error', error.message, 'erreur');\n    setStatus('error');\n    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":374,"cache_read_input_tokens":86618,"output_tokens":1169,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":374,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":1169,"cache_read_input_tokens":86618,"cache_creation_input_tokens":374,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":374},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n7QgfiNJo3GrbHCgXi","type":"assistant","uuid":"1630b3cc-1375-472a-89ea-0008946bb3b5","timestamp":"2026-06-15T21:46:15.712Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"1630b3cc-1375-472a-89ea-0008946bb3b5","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014moL1989LLvWQEBpnjmjEs","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/static/app.js has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"c6259f38-a80f-41d9-b415-0192e90e9e34","timestamp":"2026-06-15T21:46:15.746Z","toolUseResult":{"filePath":"/mnt/DATA/WORK/agent-gui/static/app.js","oldString":"async function sendMessage(event) {\n  event.preventDefault();\n  const input = $('messageInput');\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $('agentSelect').value;\n  const sessionId = await ensureSession();\n\n  input.value = '';\n  renderEvent({ type: 'user_message', content: message, agent });\n  setStatus('thinking');\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, 'thinking', '', 0);\n\n  try {\n    const data = await api('/api/chat', {\n      method: 'POST',\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $('officeCaption').textContent = data.avatar?.state === 'speaking'\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || 'speaking');\n    await loadSessions();\n  } catch (error) {\n    addMessage('error', error.message, 'erreur');\n    setStatus('error');\n    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}","newString":"let _animPollTimer = null;\n\nfunction pollAvatarAnim(sessionId) {\n  if (_animPollTimer) clearInterval(_animPollTimer);\n  _animPollTimer = setInterval(async () => {\n    try {\n      const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);\n      if (data.status === 'pending') return;\n      clearInterval(_animPollTimer);\n      _animPollTimer = null;\n      if (data.status === 'ready' && window.avatarController) {\n        await window.avatarController.speakWithAnimation(sessionId);\n        window.avatarController.startAnimation(data.bsData);\n      }\n      if (data.status === 'error') {\n        console.warn('Avatar anim error:', data.message);\n        if (window.avatarController) window.avatarController.setStatus('Erreur TTS/A2E');\n      }\n    } catch (_) {}\n  }, 800);\n}\n\nasync function sendMessage(event) {\n  event.preventDefault();\n  const input = $('messageInput');\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $('agentSelect').value;\n  const sessionId = await ensureSession();\n\n  input.value = '';\n  renderEvent({ type: 'user_message', content: message, agent });\n  setStatus('thinking');\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, 'thinking', '', 0);\n\n  try {\n    const data = await api('/api/chat', {\n      method: 'POST',\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $('officeCaption').textContent = data.avatar?.state === 'speaking'\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || 'speaking');\n    if (data.avatar_anim === 'pending') {\n      if (window.avatarController) window.avatarController.setStatus('Génération avatar...');\n      pollAvatarAnim(sessionId);\n    }\n    await loadSessions();\n  } catch (error) {\n    addMessage('error', error.message, 'erreur');\n    setStatus('error');\n    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}","originalFile":null,"structuredPatch":[{"oldStart":349,"oldLines":36,"newStart":349,"newLines":62,"lines":["   data.events.forEach(renderEvent);"," }"," ","+let _animPollTimer = null;","+","+function pollAvatarAnim(sessionId) {","+  if (_animPollTimer) clearInterval(_animPollTimer);","+  _animPollTimer = setInterval(async () => {","+    try {","+      const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);","+      if (data.status === ‘pending’) return;","+      clearInterval(_animPollTimer);","+      _animPollTimer = null;","+      if (data.status === ‘ready’ && window.avatarController) {","+        await window.avatarController.speakWithAnimation(sessionId);","+        window.avatarController.startAnimation(data.bsData);","+      }","+      if (data.status === ‘error’) {","+        console.warn(‘Avatar anim error:’, data.message);","+        if (window.avatarController) window.avatarController.setStatus(‘Erreur TTS/A2E’);","+      }","+    } catch (_) {}","+  }, 800);","+}","+"," async function sendMessage(event) {","   event.preventDefault();","-  const input = $('messageInput');","+  const input = $(‘messageInput’);","   const message = input.value.trim();","   if (!message) return;","-  const agent = $('agentSelect').value;","+  const agent = $(‘agentSelect’).value;","   const sessionId = await ensureSession();"," ","-  input.value = '';","-  renderEvent({ type: 'user_message', content: message, agent });","-  setStatus('thinking');","+  input.value = ‘’;","+  renderEvent({ type: ‘user_message’, content: message, agent });","+  setStatus(‘thinking’);","   setOfficeCaption(`${agent} est en train de réfléchir...`);","-  setCrewMood(agent, 'thinking', '', 0);","+  setCrewMood(agent, ‘thinking’, ‘’, 0);"," ","   try {","-    const data = await api('/api/chat', {","-      method: 'POST',","+    const data = await api(‘/api/chat’, {","+      method: ‘POST’,","       body: JSON.stringify({ agent, message, session_id: sessionId }),","     });","     data.events.forEach(renderEvent);","-    $('officeCaption').textContent = data.avatar?.state === 'speaking'","+    $(‘officeCaption’).textContent = data.avatar?.state === ‘speaking’","       ? `${agent} est retourné à son bureau.`","       : state.sceneMessage;","-    setStatus(data.avatar?.state || 'speaking');","+    setStatus(data.avatar?.state || ‘speaking’);","+    if (data.avatar_anim === ‘pending’) {","+      if (window.avatarController) window.avatarController.setStatus(‘Génération avatar...’);","+      pollAvatarAnim(sessionId);","+    }","     await loadSessions();","   } catch (error) {","-    addMessage('error', error.message, 'erreur');","-    setStatus('error');","-    setOfficeCaption('Erreur lors de l’exécution de l’agent.');","-    setCrewMood(agent, 'error', '!', 1900);","+    addMessage(‘error’, error.message, ‘erreur’);","+    setStatus(‘error’);","+    setOfficeCaption(‘Erreur lors de l’exécution de l’agent.’);","+    setCrewMood(agent, ‘error’, ‘!’, 1900);","   }"," }"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"1630b3cc-1375-472a-89ea-0008946bb3b5","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"69fcd6a6-073a-4656-b6b8-de8c8fe82d01","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01Rjkz1BDfrwyM5syAnKAWuM","type":"message","role":"assistant","content":[{"type":"text","text":"Enfin, j'initialise le controller avatar au démarrage de l'app."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1314,"cache_read_input_tokens":86992,"output_tokens":244,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1314,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":244,"cache_read_input_tokens":86992,"cache_creation_input_tokens":1314,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1314},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n8fKGBv3TPHmfgheCc","type":"assistant","uuid":"0c7ca8cb-45f3-47ca-8424-b79a7c7103b3","timestamp":"2026-06-15T21:46:18.301Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"bde62676-6479-41e3-bc7f-78dd83b3a424","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NSX7QjosbH5KHnAbnG7f93","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/static/app.js has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"4ad19328-184a-467d-8e54-50f78ba67b3d","timestamp":"2026-06-15T21:46:20.433Z","toolUseResult":{"filePath":"/mnt/DATA/WORK/agent-gui/static/app.js","oldString":"async function init() {\n  initEvents();\n  resizeOfficeCanvas();\n  await loadAgents();","newString":"async function init() {\n  initEvents();\n  resizeOfficeCanvas();\n  // Wait a tick for the avatar module to register its controller\n  await new Promise((r) => setTimeout(r, 100));\n  if (window.avatarController) window.avatarController.init();\n  await loadAgents();","originalFile":null,"structuredPatch":[{"oldStart":849,"oldLines":6,"newStart":849,"newLines":9,"lines":[" async function init() {","   initEvents();","   resizeOfficeCanvas();","+  // Wait a tick for the avatar module to register its controller","+  await new Promise((r) => setTimeout(r, 100));","+  if (window.avatarController) window.avatarController.init();","   await loadAgents();","   if (!state.selectedAgentId && state.crew[0]) {","     setSelectedAgent(state.crew[0].id);"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"bde62676-6479-41e3-bc7f-78dd83b3a424","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"54fe7712-3cd7-494d-80fd-8cddd0467569","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_014vnBsanXWJeY45grCpK11c","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KZV524dqwSUPdkSxaSDqqW","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui && find . -newer agent_gui/__init__.py -name \"*.py\" -o -newer agent_gui/__init__.py -name \"*.js\" 2>/dev/null | grep -v __pycache__ | sort","description":"List newly created/modified files"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":296,"cache_read_input_tokens":88306,"output_tokens":271,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":296,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":271,"cache_read_input_tokens":88306,"cache_creation_input_tokens":296,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":296},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n91pYjPZ4RT7ELWDo9","type":"assistant","uuid":"10662dd9-1d8a-4203-9dd1-bde6865b5ecb","timestamp":"2026-06-15T21:46:25.108Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"10662dd9-1d8a-4203-9dd1-bde6865b5ecb","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KZV524dqwSUPdkSxaSDqqW","type":"tool_result","content":"./agent_gui/adapters.py\n./agent_gui/app.py\n./agent_gui/avatar_lam.py\n./agent_gui/avatar.py\n./agent_gui/config.py\n./agent_gui/sessions.py\n./agent_gui/tts.py\n./static/app.js\n./static/avatar.js\n./tests/test_adapters.py\n./tests/test_avatar.py\n./tests/test_config.py\n./tests/test_sessions.py\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"f1d43e17-5423-4746-8b96-f388276f341a","timestamp":"2026-06-15T21:46:25.176Z","toolUseResult":{"stdout":"./agent_gui/adapters.py\n./agent_gui/app.py\n./agent_gui/avatar_lam.py\n./agent_gui/avatar.py\n./agent_gui/config.py\n./agent_gui/sessions.py\n./agent_gui/tts.py\n./static/app.js\n./static/avatar.js\n./tests/test_adapters.py\n./tests/test_avatar.py\n./tests/test_config.py\n./tests/test_sessions.py","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"10662dd9-1d8a-4203-9dd1-bde6865b5ecb","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"f1d43e17-5423-4746-8b96-f388276f341a","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_014vnBsanXWJeY45grCpK11c","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QiP1dnV7XebK42qcn9TMWo","name":"Bash","input":{"command":"grep -n \"from .tts\\|from .avatar_lam\\|_anim_store\\|_start_avatar\" /mnt/DATA/WORK/agent-gui/agent_gui/app.py | head -20","description":"Verify app.py imports and functions"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":296,"cache_read_input_tokens":88306,"output_tokens":271,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":296,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":271,"cache_read_input_tokens":88306,"cache_creation_input_tokens":296,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":296},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n91pYjPZ4RT7ELWDo9","type":"assistant","uuid":"daf88db8-85bf-49ef-bd10-8890883d889f","timestamp":"2026-06-15T21:46:26.163Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"daf88db8-85bf-49ef-bd10-8890883d889f","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QiP1dnV7XebK42qcn9TMWo","type":"tool_result","content":"24:_anim_store: dict[str, None | dict | str] = {}\n28:def _start_avatar_pipeline(session_id: str, text: str) -> None:\n30:        _anim_store[session_id] = None  # pending\n34:            from .tts import synthesize\n35:            from .avatar_lam import audio_to_blendshapes\n41:                _anim_store[session_id] = bs_data\n44:                _anim_store[session_id] = str(exc)\n83:                val = _anim_store.get(session_id)\n131:                _start_avatar_pipeline(session_id, assistant_text)","is_error":false}]},"uuid":"7e50dd44-59ef-48ea-a9c0-801a3e60acc2","timestamp":"2026-06-15T21:46:26.251Z","toolUseResult":{"stdout":"24:_anim_store: dict[str, None | dict | str] = {}\n28:def _start_avatar_pipeline(session_id: str, text: str) -> None:\n30:        _anim_store[session_id] = None  # pending\n34:            from .tts import synthesize\n35:            from .avatar_lam import audio_to_blendshapes\n41:                _anim_store[session_id] = bs_data\n44:                _anim_store[session_id] = str(exc)\n83:                val = _anim_store.get(session_id)\n131:                _start_avatar_pipeline(session_id, assistant_text)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"daf88db8-85bf-49ef-bd10-8890883d889f","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"7e50dd44-59ef-48ea-a9c0-801a3e60acc2","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01FBCAbHoh9RQ95J9Mudj763","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Ec5aJGGfM38gRaadRn6Bbc","name":"Bash","input":{"command":"# Test quick syntax check on new Python files\n$HOME/miniconda3/bin/python -c \"\nimport ast, pathlib\nfor f in ['agent_gui/tts.py', 'agent_gui/avatar_lam.py', 'agent_gui/app.py']:\n    src = (pathlib.Path('/mnt/DATA/WORK/agent-gui') / f).read_text()\n    ast.parse(src)\n    print(f'OK: {f}')\n\"","description":"Syntax check Python files"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":629,"cache_read_input_tokens":88602,"output_tokens":190,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":629,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":190,"cache_read_input_tokens":88602,"cache_creation_input_tokens":629,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":629},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n9SgH18K3QESQVMYa4","type":"assistant","uuid":"b0b17805-dc4f-4f2f-8671-5a5ec50039b0","timestamp":"2026-06-15T21:46:32.745Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"b0b17805-dc4f-4f2f-8671-5a5ec50039b0","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ec5aJGGfM38gRaadRn6Bbc","type":"tool_result","content":"OK: agent_gui/tts.py\nOK: agent_gui/avatar_lam.py\nOK: agent_gui/app.py","is_error":false}]},"uuid":"c9293d6f-a9f2-47d0-8c94-23e057dfd131","timestamp":"2026-06-15T21:46:32.826Z","toolUseResult":{"stdout":"OK: agent_gui/tts.py\nOK: agent_gui/avatar_lam.py\nOK: agent_gui/app.py","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"b0b17805-dc4f-4f2f-8671-5a5ec50039b0","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"c9293d6f-a9f2-47d0-8c94-23e057dfd131","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01PSBtJVT5im5Q7DSQUpPZnv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PKhRQwTrMX6jgg99sDSTVi","name":"Bash","input":{"command":"# Test TTS quickly\n$HOME/miniconda3/bin/python -c \"\nimport sys\nsys.path.insert(0, '/mnt/DATA/WORK/agent-gui')\nfrom pathlib import Path\nimport tempfile, os\nwav = Path(tempfile.mktemp(suffix='.wav'))\nfrom agent_gui.tts import synthesize\nsynthesize('Hello, I am Barbara, your AI assistant.', wav)\nprint(f'WAV: {wav}, size={wav.stat().st_size} bytes')\nwav.unlink()\n\"","description":"Test TTS synthesize function"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":235,"cache_read_input_tokens":89231,"output_tokens":206,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":235,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":206,"cache_read_input_tokens":89231,"cache_creation_input_tokens":235,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":235},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5n9v9GVoTzJayuxsJAg","type":"assistant","uuid":"3a03e8db-927e-4d79-a436-e0fed998e204","timestamp":"2026-06-15T21:46:37.307Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"3a03e8db-927e-4d79-a436-e0fed998e204","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PKhRQwTrMX6jgg99sDSTVi","type":"tool_result","content":"WAV: /tmp/tmpvflb4wlj.wav, size=123726 bytes","is_error":false}]},"uuid":"57322b2e-e6a3-4079-937e-0d7bb142b7fd","timestamp":"2026-06-15T21:46:37.970Z","toolUseResult":{"stdout":"WAV: /tmp/tmpvflb4wlj.wav, size=123726 bytes","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"3a03e8db-927e-4d79-a436-e0fed998e204","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"57322b2e-e6a3-4079-937e-0d7bb142b7fd","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01VACAK35tSSB3Dszj9rbJPc","type":"message","role":"assistant","content":[{"type":"text","text":"TTS fonctionne. Maintenant testons le pipeline A2E complet sur ce WAV."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":241,"cache_read_input_tokens":89466,"output_tokens":335,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":241,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":335,"cache_read_input_tokens":89466,"cache_creation_input_tokens":241,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":241},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5nAJ9MFUmn8uafqjyZy","type":"assistant","uuid":"a7864104-2df2-4cd1-a9e2-5ecde702f86a","timestamp":"2026-06-15T21:46:43.613Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a7864104-2df2-4cd1-a9e2-5ecde702f86a","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01VACAK35tSSB3Dszj9rbJPc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QxyqugopVYSXstmutnpMGi","name":"Bash","input":{"command":"$HOME/miniconda3/bin/python -c \"\nimport sys, tempfile\nfrom pathlib import Path\nsys.path.insert(0, '/mnt/DATA/WORK/agent-gui')\nfrom agent_gui.tts import synthesize\n\n# Generate a test WAV\nwav = Path(tempfile.mktemp(suffix='.wav'))\nsynthesize('Hello, I am Barbara.', wav)\nprint(f'TTS OK: {wav.stat().st_size} bytes')\n\n# Test A2E\nfrom agent_gui.avatar_lam import audio_to_blendshapes\nprint('Running Audio2Expression...')\nbs = audio_to_blendshapes(wav)\nprint(f'A2E OK: {len(bs[\\\"frames\\\"])} frames at {bs[\\\"metadata\\\"][\\\"fps\\\"]} fps')\nwav.unlink()\n\" 2>&1 | tail -20","description":"Test full TTS + A2E pipeline","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":241,"cache_read_input_tokens":89466,"output_tokens":335,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":241,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":335,"cache_read_input_tokens":89466,"cache_creation_input_tokens":241,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":241},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5nAJ9MFUmn8uafqjyZy","type":"assistant","uuid":"15ea0338-b6c0-41ff-9e84-d5f27091ebc8","timestamp":"2026-06-15T21:46:45.462Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"cool ca fonctionne, implemente ça sur mon gui d'avatar","leafUuid":"15ea0338-b6c0-41ff-9e84-d5f27091ebc8","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"permission-mode","permissionMode":"bypassPermissions","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"parentUuid":"15ea0338-b6c0-41ff-9e84-d5f27091ebc8","isSidechain":false,"promptId":"c3136b5c-6e33-41a2-9387-c0b0d14221b5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QxyqugopVYSXstmutnpMGi","type":"tool_result","content":"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/transformers/utils/generic.py:441: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n  _torch_pytree._register_pytree_node(\n/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/transformers/utils/generic.py:309: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n  _torch_pytree._register_pytree_node(\n[2026-06-15 23:46:47,872 INFO infer.py line 51 45277] => Loading config ...\n[2026-06-15 23:46:47,872 INFO infer.py line 58 45277] => Building model ...\n[2026-06-15 23:46:48,297 INFO infer.py line 66 45277] Num params: 97912596\n[2026-06-15 23:46:48,474 INFO infer.py line 73 45277] Loading weight at: pretrained_models/lam_audio2exp_streaming.tar\n[2026-06-15 23:46:48,634 INFO infer.py line 85 45277] => Loaded weight 'pretrained_models/lam_audio2exp_streaming.tar'\nOK 73 frames\nTTS OK: 77646 bytes\nRunning Audio2Expression...\nA2E OK: 73 frames at 30.0 fps","is_error":false}]},"uuid":"7f5144d5-55c6-414e-9fa3-86e01b1b60ed","timestamp":"2026-06-15T21:46:49.557Z","toolUseResult":{"stdout":"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/transformers/utils/generic.py:441: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n  _torch_pytree._register_pytree_node(\n/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/transformers/utils/generic.py:309: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n  _torch_pytree._register_pytree_node(\n[2026-06-15 23:46:47,872 INFO infer.py line 51 45277] => Loading config ...\n[2026-06-15 23:46:47,872 INFO infer.py line 58 45277] => Building model ...\n[2026-06-15 23:46:48,297 INFO infer.py line 66 45277] Num params: 97912596\n[2026-06-15 23:46:48,474 INFO infer.py line 73 45277] Loading weight at: pretrained_models/lam_audio2exp_streaming.tar\n[2026-06-15 23:46:48,634 INFO infer.py line 85 45277] => Loaded weight 'pretrained_models/lam_audio2exp_streaming.tar'\nOK 73 frames\nTTS OK: 77646 bytes\nRunning Audio2Expression...\nA2E OK: 73 frames at 30.0 fps","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"15ea0338-b6c0-41ff-9e84-d5f27091ebc8","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"009b1cdc-5cf1-46f4-8ea7-cbf0506b0c60","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01DDrw2K9N52d1vp7f8xowyq","type":"message","role":"assistant","content":[{"type":"text","text":"Trouvé. Mon edit a introduit des guillemets courbes (`'` `'`) au lieu de droits, ce qui casse tout le parsing de `app.js` → l'init ne tourne jamais → pas de liste d'agents. Je corrige."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":849,"cache_read_input_tokens":84763,"output_tokens":657,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":849,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":657,"cache_read_input_tokens":84763,"cache_creation_input_tokens":849,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":849},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RoQc8RgM9q5ZdWoHbN","type":"assistant","uuid":"31c9eeba-13e0-4c94-bb5b-7048e80425e1","timestamp":"2026-06-16T06:00:22.907Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"31c9eeba-13e0-4c94-bb5b-7048e80425e1","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01DDrw2K9N52d1vp7f8xowyq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KSSDG3stzBSpYyr5cwGUjY","name":"Bash","input":{"command":"grep -n \"Erreur lors de l\\|setOfficeCaption('L\\|setOfficeCaption('Erreur\" /mnt/DATA/WORK/agent-gui/static/app.js","description":"Find all curly-quote lines in app.js"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":849,"cache_read_input_tokens":84763,"output_tokens":657,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":849,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":657,"cache_read_input_tokens":84763,"cache_creation_input_tokens":849,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":849},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RoQc8RgM9q5ZdWoHbN","type":"assistant","uuid":"be971ee8-5c6a-45e5-b417-4f30f5afbe8e","timestamp":"2026-06-16T06:00:24.046Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"type":"last-prompt","lastPrompt":"je ne vois plus la liste des agents à gauche ( claude, hermes) etil n'y a pas d'avatar qui s'affichent","leafUuid":"83e8dbf6-a18a-4c4d-8692-2232d151a527","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"parentUuid":"0b4a0c23-6dc9-401c-a41e-0934db760a10","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_017Lt9NfHYUkRW4dR4txeuRF","type":"message","role":"assistant","content":[{"type":"text","text":"Ligne 406 a des guillemets courbes comme délimiteurs de string (`'...'`) — c'est l'erreur de syntaxe. Les autres lignes ont des apostrophes courbes *à l'intérieur* de strings droites, ce qui est valide. Je corrige la 406."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":801,"cache_read_input_tokens":85612,"output_tokens":461,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":801,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":461,"cache_read_input_tokens":85612,"cache_creation_input_tokens":801,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":801},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RpAdXE7rM3KHsdMXHB","type":"assistant","uuid":"e5062e58-2259-4e1a-96b8-e69ae40ad187","timestamp":"2026-06-16T06:00:29.029Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e5062e58-2259-4e1a-96b8-e69ae40ad187","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_017Lt9NfHYUkRW4dR4txeuRF","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QzgNrXu4CWa76VNuLhSkgQ","name":"Edit","input":{"replace_all":false,"file_path":"/mnt/DATA/WORK/agent-gui/static/app.js","old_string":"    setOfficeCaption(‘Erreur lors de l’exécution de l’agent.’);\n    setCrewMood(agent, ‘error’, ‘!’, 1900);","new_string":"    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":801,"cache_read_input_tokens":85612,"output_tokens":461,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":801,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":461,"cache_read_input_tokens":85612,"cache_creation_input_tokens":801,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":801},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RpAdXE7rM3KHsdMXHB","type":"assistant","uuid":"22bf3256-0d40-422a-909d-0678ea3091c8","timestamp":"2026-06-16T06:00:30.991Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"22bf3256-0d40-422a-909d-0678ea3091c8","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QzgNrXu4CWa76VNuLhSkgQ","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/static/app.js has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"67822419-bee3-46db-908e-071103f72b31","timestamp":"2026-06-16T06:00:31.006Z","toolUseResult":{"filePath":"/mnt/DATA/WORK/agent-gui/static/app.js","oldString":"    setOfficeCaption(‘Erreur lors de l’exécution de l’agent.’);\n    setCrewMood(agent, ‘error’, ‘!’, 1900);","newString":"    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);","originalFile":null,"structuredPatch":[{"oldStart":403,"oldLines":8,"newStart":403,"newLines":8,"lines":["   } catch (error) {","     addMessage(‘error’, error.message, ‘erreur’);","     setStatus(‘error’);","-    setOfficeCaption(‘Erreur lors de l’exécution de l’agent.’);","-    setCrewMood(agent, ‘error’, ‘!’, 1900);","+    setOfficeCaption('Erreur lors de l’exécution de l’agent.');","+    setCrewMood(agent, 'error', '!', 1900);","   }"," }"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"22bf3256-0d40-422a-909d-0678ea3091c8","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"8ded3484-5614-4a0d-bd48-b3143122374d","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01APMXdrGPVn3MAZzXaL9iFC","type":"tool_result","content":"359:      if (data.status === ‘pending’) return;\n366:      if (data.status === ‘error’) {\n367:        console.warn(‘Avatar anim error:’, data.message);\n368:        if (window.avatarController) window.avatarController.setStatus(‘Erreur TTS/A2E’);\n376:  const input = $(‘messageInput’);\n379:  const agent = $(‘agentSelect’).value;\n382:  input.value = ‘’;\n383:  renderEvent({ type: ‘user_message’, content: message, agent });\n384:  setStatus(‘thinking’);\n386:  setCrewMood(agent, ‘thinking’, ‘’, 0);\n389:    const data = await api(‘/api/chat’, {\n390:      method: ‘POST’,\n394:    $(‘officeCaption’).textContent = data.avatar?.state === ‘speaking’\n397:    setStatus(data.avatar?.state || ‘speaking’);\n398:    if (data.avatar_anim === ‘pending’) {\n399:      if (window.avatarController) window.avatarController.setStatus(‘Génération avatar...’);\n404:    addMessage(‘error’, error.message, ‘erreur’);\n405:    setStatus(‘error’);","is_error":false}]},"uuid":"a9658336-a037-46b1-b05a-35ba46bf3f2e","timestamp":"2026-06-16T06:00:35.427Z","toolUseResult":{"stdout":"359:      if (data.status === ‘pending’) return;\n366:      if (data.status === ‘error’) {\n367:        console.warn(‘Avatar anim error:’, data.message);\n368:        if (window.avatarController) window.avatarController.setStatus(‘Erreur TTS/A2E’);\n376:  const input = $(‘messageInput’);\n379:  const agent = $(‘agentSelect’).value;\n382:  input.value = ‘’;\n383:  renderEvent({ type: ‘user_message’, content: message, agent });\n384:  setStatus(‘thinking’);\n386:  setCrewMood(agent, ‘thinking’, ‘’, 0);\n389:    const data = await api(‘/api/chat’, {\n390:      method: ‘POST’,\n394:    $(‘officeCaption’).textContent = data.avatar?.state === ‘speaking’\n397:    setStatus(data.avatar?.state || ‘speaking’);\n398:    if (data.avatar_anim === ‘pending’) {\n399:      if (window.avatarController) window.avatarController.setStatus(‘Génération avatar...’);\n404:    addMessage(‘error’, error.message, ‘erreur’);\n405:    setStatus(‘error’);","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"8ded3484-5614-4a0d-bd48-b3143122374d","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"cd68eac1-78c8-49a1-aeb6-99062d1e649f","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01Mc8i28SNzV7q1FXshca3j2","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":620,"cache_read_input_tokens":86930,"output_tokens":300,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":620,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":300,"cache_read_input_tokens":86930,"cache_creation_input_tokens":620,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":620},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RpztfK1doiLwhuH99W","type":"assistant","uuid":"7eb241a1-d902-4808-aa77-cc5c501ba19d","timestamp":"2026-06-16T06:00:39.268Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"7eb241a1-d902-4808-aa77-cc5c501ba19d","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01Mc8i28SNzV7q1FXshca3j2","type":"message","role":"assistant","content":[{"type":"text","text":"Tout mon bloc collé (pollAvatarAnim + sendMessage) a des guillemets courbes. Je dois réécrire la section entière avec des guillemets droits. Je regarde l'étendue exacte."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":620,"cache_read_input_tokens":86930,"output_tokens":300,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":620,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":300,"cache_read_input_tokens":86930,"cache_creation_input_tokens":620,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":620},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RpztfK1doiLwhuH99W","type":"assistant","uuid":"5fad7cb1-c548-473d-84fc-d51cb9e15dae","timestamp":"2026-06-16T06:00:40.742Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"5fad7cb1-c548-473d-84fc-d51cb9e15dae","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01Mc8i28SNzV7q1FXshca3j2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011k9vihGB2ShKYWe3Kq5Wvu","name":"Read","input":{"file_path":"/mnt/DATA/WORK/agent-gui/static/app.js","offset":352,"limit":60},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":620,"cache_read_input_tokens":86930,"output_tokens":300,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":620,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":300,"cache_read_input_tokens":86930,"cache_creation_input_tokens":620,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":620},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RpztfK1doiLwhuH99W","type":"assistant","uuid":"3f44d603-f498-4ccb-8c97-f6741270bb1d","timestamp":"2026-06-16T06:00:41.050Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"3f44d603-f498-4ccb-8c97-f6741270bb1d","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011k9vihGB2ShKYWe3Kq5Wvu","type":"tool_result","content":"352\tlet _animPollTimer = null;\n353\t\n354\tfunction pollAvatarAnim(sessionId) {\n355\t  if (_animPollTimer) clearInterval(_animPollTimer);\n356\t  _animPollTimer = setInterval(async () => {\n357\t    try {\n358\t      const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);\n359\t      if (data.status === ‘pending’) return;\n360\t      clearInterval(_animPollTimer);\n361\t      _animPollTimer = null;\n362\t      if (data.status === ‘ready’ && window.avatarController) {\n363\t        await window.avatarController.speakWithAnimation(sessionId);\n364\t        window.avatarController.startAnimation(data.bsData);\n365\t      }\n366\t      if (data.status === ‘error’) {\n367\t        console.warn(‘Avatar anim error:’, data.message);\n368\t        if (window.avatarController) window.avatarController.setStatus(‘Erreur TTS/A2E’);\n369\t      }\n370\t    } catch (_) {}\n371\t  }, 800);\n372\t}\n373\t\n374\tasync function sendMessage(event) {\n375\t  event.preventDefault();\n376\t  const input = $(‘messageInput’);\n377\t  const message = input.value.trim();\n378\t  if (!message) return;\n379\t  const agent = $(‘agentSelect’).value;\n380\t  const sessionId = await ensureSession();\n381\t\n382\t  input.value = ‘’;\n383\t  renderEvent({ type: ‘user_message’, content: message, agent });\n384\t  setStatus(‘thinking’);\n385\t  setOfficeCaption(`${agent} est en train de réfléchir...`);\n386\t  setCrewMood(agent, ‘thinking’, ‘’, 0);\n387\t\n388\t  try {\n389\t    const data = await api(‘/api/chat’, {\n390\t      method: ‘POST’,\n391\t      body: JSON.stringify({ agent, message, session_id: sessionId }),\n392\t    });\n393\t    data.events.forEach(renderEvent);\n394\t    $(‘officeCaption’).textContent = data.avatar?.state === ‘speaking’\n395\t      ? `${agent} est retourné à son bureau.`\n396\t      : state.sceneMessage;\n397\t    setStatus(data.avatar?.state || ‘speaking’);\n398\t    if (data.avatar_anim === ‘pending’) {\n399\t      if (window.avatarController) window.avatarController.setStatus(‘Génération avatar...’);\n400\t      pollAvatarAnim(sessionId);\n401\t    }\n402\t    await loadSessions();\n403\t  } catch (error) {\n404\t    addMessage(‘error’, error.message, ‘erreur’);\n405\t    setStatus(‘error’);\n406\t    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n407\t    setCrewMood(agent, 'error', '!', 1900);\n408\t  }\n409\t}\n410\t\n411\tasync function newSession() {"}]},"uuid":"a57b319a-f862-459d-9b49-50ad002280d3","timestamp":"2026-06-16T06:00:41.071Z","toolUseResult":{"type":"text","file":{"filePath":"/mnt/DATA/WORK/agent-gui/static/app.js","content":"let _animPollTimer = null;\n\nfunction pollAvatarAnim(sessionId) {\n  if (_animPollTimer) clearInterval(_animPollTimer);\n  _animPollTimer = setInterval(async () => {\n    try {\n      const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);\n      if (data.status === ‘pending’) return;\n      clearInterval(_animPollTimer);\n      _animPollTimer = null;\n      if (data.status === ‘ready’ && window.avatarController) {\n        await window.avatarController.speakWithAnimation(sessionId);\n        window.avatarController.startAnimation(data.bsData);\n      }\n      if (data.status === ‘error’) {\n        console.warn(‘Avatar anim error:’, data.message);\n        if (window.avatarController) window.avatarController.setStatus(‘Erreur TTS/A2E’);\n      }\n    } catch (_) {}\n  }, 800);\n}\n\nasync function sendMessage(event) {\n  event.preventDefault();\n  const input = $(‘messageInput’);\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $(‘agentSelect’).value;\n  const sessionId = await ensureSession();\n\n  input.value = ‘’;\n  renderEvent({ type: ‘user_message’, content: message, agent });\n  setStatus(‘thinking’);\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, ‘thinking’, ‘’, 0);\n\n  try {\n    const data = await api(‘/api/chat’, {\n      method: ‘POST’,\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $(‘officeCaption’).textContent = data.avatar?.state === ‘speaking’\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || ‘speaking’);\n    if (data.avatar_anim === ‘pending’) {\n      if (window.avatarController) window.avatarController.setStatus(‘Génération avatar...’);\n      pollAvatarAnim(sessionId);\n    }\n    await loadSessions();\n  } catch (error) {\n    addMessage(‘error’, error.message, ‘erreur’);\n    setStatus(‘error’);\n    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}\n\nasync function newSession() {","numLines":60,"startLine":352,"totalLines":870}},"sourceToolAssistantUUID":"3f44d603-f498-4ccb-8c97-f6741270bb1d","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a57b319a-f862-459d-9b49-50ad002280d3","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_016hbfMR3vs1wS2hvAQnBrzT","type":"message","role":"assistant","content":[{"type":"text","text":"I'll rewrite the whole block with straight quotes."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1456,"cache_read_input_tokens":87550,"output_tokens":1882,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1456,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1882,"cache_read_input_tokens":87550,"cache_creation_input_tokens":1456,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1456},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RqRKqvRDuKcPsoZrpJ","type":"assistant","uuid":"cc6efc90-51e8-4170-99f8-e3083defcdda","timestamp":"2026-06-16T06:00:44.550Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"cc6efc90-51e8-4170-99f8-e3083defcdda","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_016hbfMR3vs1wS2hvAQnBrzT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EepJyaaBLykQ1oDpU3inyz","name":"Edit","input":{"replace_all":false,"file_path":"/mnt/DATA/WORK/agent-gui/static/app.js","old_string":"let _animPollTimer = null;\n\nfunction pollAvatarAnim(sessionId) {\n  if (_animPollTimer) clearInterval(_animPollTimer);\n  _animPollTimer = setInterval(async () => {\n    try {\n      const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);\n      if (data.status === ‘pending’) return;\n      clearInterval(_animPollTimer);\n      _animPollTimer = null;\n      if (data.status === ‘ready’ && window.avatarController) {\n        await window.avatarController.speakWithAnimation(sessionId);\n        window.avatarController.startAnimation(data.bsData);\n      }\n      if (data.status === ‘error’) {\n        console.warn(‘Avatar anim error:’, data.message);\n        if (window.avatarController) window.avatarController.setStatus(‘Erreur TTS/A2E’);\n      }\n    } catch (_) {}\n  }, 800);\n}\n\nasync function sendMessage(event) {\n  event.preventDefault();\n  const input = $(‘messageInput’);\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $(‘agentSelect’).value;\n  const sessionId = await ensureSession();\n\n  input.value = ‘’;\n  renderEvent({ type: ‘user_message’, content: message, agent });\n  setStatus(‘thinking’);\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, ‘thinking’, ‘’, 0);\n\n  try {\n    const data = await api(‘/api/chat’, {\n      method: ‘POST’,\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $(‘officeCaption’).textContent = data.avatar?.state === ‘speaking’\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || ‘speaking’);\n    if (data.avatar_anim === ‘pending’) {\n      if (window.avatarController) window.avatarController.setStatus(‘Génération avatar...’);\n      pollAvatarAnim(sessionId);\n    }\n    await loadSessions();\n  } catch (error) {\n    addMessage(‘error’, error.message, ‘erreur’);\n    setStatus(‘error’);\n    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}","new_string":"let _animPollTimer = null;\n\nfunction pollAvatarAnim(sessionId) {\n  if (_animPollTimer) clearInterval(_animPollTimer);\n  _animPollTimer = setInterval(async () => {\n    try {\n      const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);\n      if (data.status === 'pending') return;\n      clearInterval(_animPollTimer);\n      _animPollTimer = null;\n      if (data.status === 'ready' && window.avatarController) {\n        await window.avatarController.speakWithAnimation(sessionId);\n        window.avatarController.startAnimation(data.bsData);\n      }\n      if (data.status === 'error') {\n        console.warn('Avatar anim error:', data.message);\n        if (window.avatarController) window.avatarController.setStatus('Erreur TTS/A2E');\n      }\n    } catch (_) {}\n  }, 800);\n}\n\nasync function sendMessage(event) {\n  event.preventDefault();\n  const input = $('messageInput');\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $('agentSelect').value;\n  const sessionId = await ensureSession();\n\n  input.value = '';\n  renderEvent({ type: 'user_message', content: message, agent });\n  setStatus('thinking');\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, 'thinking', '', 0);\n\n  try {\n    const data = await api('/api/chat', {\n      method: 'POST',\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $('officeCaption').textContent = data.avatar?.state === 'speaking'\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || 'speaking');\n    if (data.avatar_anim === 'pending') {\n      if (window.avatarController) window.avatarController.setStatus('Génération avatar...');\n      pollAvatarAnim(sessionId);\n    }\n    await loadSessions();\n  } catch (error) {\n    addMessage('error', error.message, 'erreur');\n    setStatus('error');\n    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1456,"cache_read_input_tokens":87550,"output_tokens":1882,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1456,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1882,"cache_read_input_tokens":87550,"cache_creation_input_tokens":1456,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1456},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RqRKqvRDuKcPsoZrpJ","type":"assistant","uuid":"bc397c5d-835f-4126-8a2e-cb487c43cf54","timestamp":"2026-06-16T06:00:58.214Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"bc397c5d-835f-4126-8a2e-cb487c43cf54","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EepJyaaBLykQ1oDpU3inyz","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/static/app.js has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"f8cb3a0c-2439-4571-93e8-7d585299bc5a","timestamp":"2026-06-16T06:00:58.250Z","toolUseResult":{"filePath":"/mnt/DATA/WORK/agent-gui/static/app.js","oldString":"let _animPollTimer = null;\n\nfunction pollAvatarAnim(sessionId) {\n  if (_animPollTimer) clearInterval(_animPollTimer);\n  _animPollTimer = setInterval(async () => {\n    try {\n      const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);\n      if (data.status === ‘pending’) return;\n      clearInterval(_animPollTimer);\n      _animPollTimer = null;\n      if (data.status === ‘ready’ && window.avatarController) {\n        await window.avatarController.speakWithAnimation(sessionId);\n        window.avatarController.startAnimation(data.bsData);\n      }\n      if (data.status === ‘error’) {\n        console.warn(‘Avatar anim error:’, data.message);\n        if (window.avatarController) window.avatarController.setStatus(‘Erreur TTS/A2E’);\n      }\n    } catch (_) {}\n  }, 800);\n}\n\nasync function sendMessage(event) {\n  event.preventDefault();\n  const input = $(‘messageInput’);\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $(‘agentSelect’).value;\n  const sessionId = await ensureSession();\n\n  input.value = ‘’;\n  renderEvent({ type: ‘user_message’, content: message, agent });\n  setStatus(‘thinking’);\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, ‘thinking’, ‘’, 0);\n\n  try {\n    const data = await api(‘/api/chat’, {\n      method: ‘POST’,\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $(‘officeCaption’).textContent = data.avatar?.state === ‘speaking’\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || ‘speaking’);\n    if (data.avatar_anim === ‘pending’) {\n      if (window.avatarController) window.avatarController.setStatus(‘Génération avatar...’);\n      pollAvatarAnim(sessionId);\n    }\n    await loadSessions();\n  } catch (error) {\n    addMessage(‘error’, error.message, ‘erreur’);\n    setStatus(‘error’);\n    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}","newString":"let _animPollTimer = null;\n\nfunction pollAvatarAnim(sessionId) {\n  if (_animPollTimer) clearInterval(_animPollTimer);\n  _animPollTimer = setInterval(async () => {\n    try {\n      const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);\n      if (data.status === 'pending') return;\n      clearInterval(_animPollTimer);\n      _animPollTimer = null;\n      if (data.status === 'ready' && window.avatarController) {\n        await window.avatarController.speakWithAnimation(sessionId);\n        window.avatarController.startAnimation(data.bsData);\n      }\n      if (data.status === 'error') {\n        console.warn('Avatar anim error:', data.message);\n        if (window.avatarController) window.avatarController.setStatus('Erreur TTS/A2E');\n      }\n    } catch (_) {}\n  }, 800);\n}\n\nasync function sendMessage(event) {\n  event.preventDefault();\n  const input = $('messageInput');\n  const message = input.value.trim();\n  if (!message) return;\n  const agent = $('agentSelect').value;\n  const sessionId = await ensureSession();\n\n  input.value = '';\n  renderEvent({ type: 'user_message', content: message, agent });\n  setStatus('thinking');\n  setOfficeCaption(`${agent} est en train de réfléchir...`);\n  setCrewMood(agent, 'thinking', '', 0);\n\n  try {\n    const data = await api('/api/chat', {\n      method: 'POST',\n      body: JSON.stringify({ agent, message, session_id: sessionId }),\n    });\n    data.events.forEach(renderEvent);\n    $('officeCaption').textContent = data.avatar?.state === 'speaking'\n      ? `${agent} est retourné à son bureau.`\n      : state.sceneMessage;\n    setStatus(data.avatar?.state || 'speaking');\n    if (data.avatar_anim === 'pending') {\n      if (window.avatarController) window.avatarController.setStatus('Génération avatar...');\n      pollAvatarAnim(sessionId);\n    }\n    await loadSessions();\n  } catch (error) {\n    addMessage('error', error.message, 'erreur');\n    setStatus('error');\n    setOfficeCaption('Erreur lors de l’exécution de l’agent.');\n    setCrewMood(agent, 'error', '!', 1900);\n  }\n}","originalFile":null,"structuredPatch":[{"oldStart":356,"oldLines":16,"newStart":356,"newLines":16,"lines":["   _animPollTimer = setInterval(async () => {","     try {","       const data = await api(`/api/avatar/anim?session_id=${encodeURIComponent(sessionId)}`);","-      if (data.status === ‘pending’) return;","+      if (data.status === 'pending') return;","       clearInterval(_animPollTimer);","       _animPollTimer = null;","-      if (data.status === ‘ready’ && window.avatarController) {","+      if (data.status === 'ready' && window.avatarController) {","         await window.avatarController.speakWithAnimation(sessionId);","         window.avatarController.startAnimation(data.bsData);","       }","-      if (data.status === ‘error’) {","-        console.warn(‘Avatar anim error:’, data.message);","-        if (window.avatarController) window.avatarController.setStatus(‘Erreur TTS/A2E’);","+      if (data.status === 'error') {","+        console.warn('Avatar anim error:', data.message);","+        if (window.avatarController) window.avatarController.setStatus('Erreur TTS/A2E');","       }","     } catch (_) {}","   }, 800);"]},{"oldStart":373,"oldLines":36,"newStart":373,"newLines":36,"lines":[" "," async function sendMessage(event) {","   event.preventDefault();","-  const input = $(‘messageInput’);","+  const input = $('messageInput');","   const message = input.value.trim();","   if (!message) return;","-  const agent = $(‘agentSelect’).value;","+  const agent = $('agentSelect').value;","   const sessionId = await ensureSession();"," ","-  input.value = ‘’;","-  renderEvent({ type: ‘user_message’, content: message, agent });","-  setStatus(‘thinking’);","+  input.value = '';","+  renderEvent({ type: 'user_message', content: message, agent });","+  setStatus('thinking');","   setOfficeCaption(`${agent} est en train de réfléchir...`);","-  setCrewMood(agent, ‘thinking’, ‘’, 0);","+  setCrewMood(agent, 'thinking', '', 0);"," ","   try {","-    const data = await api(‘/api/chat’, {","-      method: ‘POST’,","+    const data = await api('/api/chat', {","+      method: 'POST',","       body: JSON.stringify({ agent, message, session_id: sessionId }),","     });","     data.events.forEach(renderEvent);","-    $(‘officeCaption’).textContent = data.avatar?.state === ‘speaking’","+    $('officeCaption').textContent = data.avatar?.state === 'speaking'","       ? `${agent} est retourné à son bureau.`","       : state.sceneMessage;","-    setStatus(data.avatar?.state || ‘speaking’);","-    if (data.avatar_anim === ‘pending’) {","-      if (window.avatarController) window.avatarController.setStatus(‘Génération avatar...’);","+    setStatus(data.avatar?.state || 'speaking');","+    if (data.avatar_anim === 'pending') {","+      if (window.avatarController) window.avatarController.setStatus('Génération avatar...');","       pollAvatarAnim(sessionId);","     }","     await loadSessions();","   } catch (error) {","-    addMessage(‘error’, error.message, ‘erreur’);","-    setStatus(‘error’);","+    addMessage('error', error.message, 'erreur');","+    setStatus('error');","     setOfficeCaption('Erreur lors de l’exécution de l’agent.');","     setCrewMood(agent, 'error', '!', 1900);","   }"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"bc397c5d-835f-4126-8a2e-cb487c43cf54","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"je ne vois plus la liste des agents à gauche ( claude, hermes) etil n'y a pas d'avatar qui s'affichent","leafUuid":"f8cb3a0c-2439-4571-93e8-7d585299bc5a","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"parentUuid":"f8cb3a0c-2439-4571-93e8-7d585299bc5a","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01DFoqibY8PDMUucQNRX7hS7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SXLtUxiBxB6cfU8yAExnMV","name":"Bash","input":{"command":"# Verify no curly quotes used as string delimiters remain anywhere in app.js\ngrep -nP \"[\\(\\,=]\\s*[‘’]|[‘’]\\s*[\\),;]|=== ?[‘’]|\\|\\| ?[‘’]\" /mnt/DATA/WORK/agent-gui/static/app.js && echo \"STILL HAS PROBLEMS\" || echo \"Clean — only curly apostrophes inside strings remain\"","description":"Verify app.js has no curly quote delimiters"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1938,"cache_read_input_tokens":89006,"output_tokens":233,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1938,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":233,"cache_read_input_tokens":89006,"cache_creation_input_tokens":1938,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1938},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RrghqDkvBQALMGbUi4","type":"assistant","uuid":"5afccaae-47b6-4398-a638-d97385ea77bf","timestamp":"2026-06-16T06:01:02.976Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"5afccaae-47b6-4398-a638-d97385ea77bf","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SXLtUxiBxB6cfU8yAExnMV","type":"tool_result","content":"Clean — only curly apostrophes inside strings remain","is_error":false}]},"uuid":"94a1d959-c666-4587-a4bf-1e44d5ea7e53","timestamp":"2026-06-16T06:01:03.036Z","toolUseResult":{"stdout":"Clean — only curly apostrophes inside strings remain","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"5afccaae-47b6-4398-a638-d97385ea77bf","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"94a1d959-c666-4587-a4bf-1e44d5ea7e53","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01DqHkhU5g2jzg9vMWmMCATZ","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"ErwCCmMIDhgCKkB2PRzt2SyNSMIYdXifH9j3COTxnSY6WexVLSYMGgfbu/nqCwc4xb9xMyB7CVG9JuLGAB//829HO9K86QFs2GMMMg9jbGF1ZGUtb3B1cy00LTg4AEIIdGhpbmtpbmcSDHU3mWWayx5RWKMxfhoM7nxBxyo1NNWeX8u1IjANAcewEz4SluptE101jrNiQIQdrEkDdXDyePm+fmeetNWM1p0J4H0Zq1GOG1YNKz0qhgHu2+xQpYF/45aC3r1mZ+COzRzXEpSalUvUNsgagx+fS6U/lw16k4JNOke4PzveJBUm9/V9olkC2pzTE2wxq8XcCsswNUVhMDP5XMyO8W3ohX1rCBt+xqJeoobmnDNzt+PuvTpeeaX895opZ8QgGC+YHCieLjWOzoeKq4KBv//cMKgycd7puhgB"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":256,"cache_read_input_tokens":90944,"output_tokens":312,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":256,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":312,"cache_read_input_tokens":90944,"cache_creation_input_tokens":256,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":256},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Rs3CdA9WfchL6g43bu","type":"assistant","uuid":"4c69cb2c-641a-46e7-86ef-9cd19989143c","timestamp":"2026-06-16T06:01:06.137Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"4c69cb2c-641a-46e7-86ef-9cd19989143c","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01DqHkhU5g2jzg9vMWmMCATZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017haS2uQx2JGZ5ue6ic2eBY","name":"Bash","input":{"command":"# Validate JS parses correctly with node if available\nwhich node && node --check /mnt/DATA/WORK/agent-gui/static/app.js 2>&1 && echo \"app.js OK\" || echo \"node not available, skipping\"\n# Also check avatar.js for curly quotes\ngrep -nP \"[\\(\\,=]\\s*[‘’]|[‘’]\\s*[\\),;]|=== ?[‘’]\" /mnt/DATA/WORK/agent-gui/static/avatar.js && echo \"avatar.js HAS PROBLEMS\" || echo \"avatar.js clean\"","description":"Validate app.js and check avatar.js"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":256,"cache_read_input_tokens":90944,"output_tokens":312,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":256,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":312,"cache_read_input_tokens":90944,"cache_creation_input_tokens":256,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":256},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Rs3CdA9WfchL6g43bu","type":"assistant","uuid":"c5fc8969-6fc9-41d9-a5f5-afcdff6660e2","timestamp":"2026-06-16T06:01:08.287Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"c5fc8969-6fc9-41d9-a5f5-afcdff6660e2","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017haS2uQx2JGZ5ue6ic2eBY","type":"tool_result","content":"/usr/bin/node\napp.js OK\navatar.js clean","is_error":false}]},"uuid":"299f3a28-54a1-4d44-b4c1-8680d93bdbe2","timestamp":"2026-06-16T06:01:08.389Z","toolUseResult":{"stdout":"/usr/bin/node\napp.js OK\navatar.js clean","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"c5fc8969-6fc9-41d9-a5f5-afcdff6660e2","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"299f3a28-54a1-4d44-b4c1-8680d93bdbe2","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_018YwEBSCQMxNdJU8sDMnYwM","type":"message","role":"assistant","content":[{"type":"text","text":"`app.js` parse maintenant correctement. La liste d'agents reviendra. Pour l'avatar qui ne s'affiche pas — c'est probablement lié à l'erreur JS qui bloquait tout, ou au chargement du GLB. Rafraîchis la page (Ctrl+Shift+R pour vider le cache) et regarde la console du navigateur.\n\nMais vérifions d'abord que le module avatar.js se charge bien — `node --check` ne valide pas les imports ES. Regardons s'il y a un souci avec `node:` ou la syntaxe module."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":339,"cache_read_input_tokens":91200,"output_tokens":347,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":339,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":347,"cache_read_input_tokens":91200,"cache_creation_input_tokens":339,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":339},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RsRqQdtnos13GfGj6C","type":"assistant","uuid":"57029226-1d99-472d-943a-dea1900859ca","timestamp":"2026-06-16T06:01:13.506Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"57029226-1d99-472d-943a-dea1900859ca","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_018YwEBSCQMxNdJU8sDMnYwM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01D6nbHEtFNYFWYbnStXHgTg","name":"Bash","input":{"command":"node --input-type=module --check < /mnt/DATA/WORK/agent-gui/static/avatar.js 2>&1 && echo \"avatar.js module syntax OK\" || echo \"avatar.js has syntax error\"","description":"Validate avatar.js as ES module"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":339,"cache_read_input_tokens":91200,"output_tokens":347,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":339,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":347,"cache_read_input_tokens":91200,"cache_creation_input_tokens":339,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":339},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RsRqQdtnos13GfGj6C","type":"assistant","uuid":"c6e2f351-24ae-4f53-9a66-8bd870b73507","timestamp":"2026-06-16T06:01:14.693Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"861612c5-6dec-4c51-9d80-5afb0ab2ec66","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01VSXQP3BLg2QAMzNJVHVV5z","type":"message","role":"assistant","content":[{"type":"text","text":"Les deux fichiers parsent bien. Le souci d'affichage de l'avatar mérite investigation — ce GLB est peut-être destiné au gaussian splatting, pas à un rendu mesh standard. Vérifions sa structure (matériaux, textures)."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":363,"cache_read_input_tokens":91539,"output_tokens":1035,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":363,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1035,"cache_read_input_tokens":91539,"cache_creation_input_tokens":363,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":363},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RsuM8DH1dCu4BX71Ux","type":"assistant","uuid":"5c9a3802-9723-4e28-b923-b584efc62de0","timestamp":"2026-06-16T06:01:25.627Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"5c9a3802-9723-4e28-b923-b584efc62de0","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01VSXQP3BLg2QAMzNJVHVV5z","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0158bJSsxRsJ3tKJAo7c7GGj","name":"Bash","input":{"command":"$HOME/miniconda3/bin/python -c \"\nimport struct, json\nwith open('/mnt/DATA/WORK/agent-gui/static/avatar/barbara/skin.glb', 'rb') as f:\n    struct.unpack('<4sII', f.read(12))\n    chunk_len, chunk_type = struct.unpack('<II', f.read(8))\n    gltf = json.loads(f.read(chunk_len))\n\nprint('materials:', len(gltf.get('materials', [])))\nfor m in gltf.get('materials', [])[:3]:\n    print('  ', m.get('name'), list(m.keys()))\nprint('textures:', len(gltf.get('textures', [])))\nprint('images:', len(gltf.get('images', [])))\nprim = gltf['meshes'][0]['primitives'][0]\nprint('primitive attributes:', list(prim.get('attributes', {}).keys()))\nprint('primitive mode:', prim.get('mode', 4), '(4=TRIANGLES, 0=POINTS)')\nprint('has material:', 'material' in prim)\nprint('extensions used:', gltf.get('extensionsUsed', []))\n\"","description":"Inspect GLB materials, textures, primitive mode"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":363,"cache_read_input_tokens":91539,"output_tokens":1035,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":363,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1035,"cache_read_input_tokens":91539,"cache_creation_input_tokens":363,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":363},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RsuM8DH1dCu4BX71Ux","type":"assistant","uuid":"b3d21c48-2b41-499f-ba94-0bcd9324dc3e","timestamp":"2026-06-16T06:01:29.722Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"61d58ccb-d240-4c31-80ca-acf40fb9658b","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01QKyxUhuzrRYEeig5be5aze","type":"message","role":"assistant","content":[{"type":"text","text":"Voilà le vrai problème. Le `skin.glb` est juste une **géométrie nue** : positions + skinning + 51 morph targets, mais **aucune normale, aucune texture, aucune couleur**. C'est un simple proxy mesh. Le rendu photoréaliste de LAM vient du **Gaussian Splatting** (données dans `offset.ply` + `animation.glb`), pas du mesh.\n\nDonc :\n1. **Pas d'avatar visible** = le mesh sans normales rend en noir sur fond noir → invisible.\n2. Charger juste `skin.glb` en Three.js ne donnera jamais le rendu photoréaliste.\n\nD'après ta mémoire projet, le bon chemin c'est le package npm `gaussian-splat-renderer-for-lam` (repo `aigc3d/LAM_WebRender`). Vérifions sa disponibilité via CDN."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":1167,"cache_read_input_tokens":91902,"output_tokens":1619,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1167,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":1619,"cache_read_input_tokens":91902,"cache_creation_input_tokens":1167,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1167},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Ru1R2WZN758BygukVL","type":"assistant","uuid":"a8f82b95-0882-4c6a-907e-957edfb63ad3","timestamp":"2026-06-16T06:01:52.178Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a8f82b95-0882-4c6a-907e-957edfb63ad3","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01QKyxUhuzrRYEeig5be5aze","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NxJ5Db5XtjEwdjsKkDKaZG","name":"Bash","input":{"command":"for url in \\\n  \"https://cdn.jsdelivr.net/npm/gaussian-splat-renderer-for-lam/package.json\" \\\n  \"https://registry.npmjs.org/gaussian-splat-renderer-for-lam\" \\\n  ; do\n  echo \"=== $url ===\"\n  curl -s --max-time 15 \"$url\" 2>&1 | head -c 600\n  echo \"\"\ndone","description":"Check npm package availability for LAM gaussian renderer"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":1167,"cache_read_input_tokens":91902,"output_tokens":1619,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1167,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":1619,"cache_read_input_tokens":91902,"cache_creation_input_tokens":1167,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1167},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Ru1R2WZN758BygukVL","type":"assistant","uuid":"19048225-1456-487f-964f-2891eeb3bdc9","timestamp":"2026-06-16T06:01:52.634Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"19048225-1456-487f-964f-2891eeb3bdc9","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NxJ5Db5XtjEwdjsKkDKaZG","type":"tool_result","content":"=== https://cdn.jsdelivr.net/npm/gaussian-splat-renderer-for-lam/package.json ===\n{\n    \"name\": \"gaussian-splat-renderer-for-lam\",\n    \"version\": \"0.0.9-alpha.2\",\n    \"description\": \"Three.js-based 3D Gaussian splat viewer for LAM\",\n    \"module\": \"build/gaussian-splat-renderer-for-lam.module.js\",\n    \"main\": \"build/gaussian-splat-renderer-for-lam.umd.cjs\",\n    \"author\": \"Xiaodan Ye\",\n    \"license\": \"MIT\",\n    \"type\": \"module\",\n    \"scripts\": {\n        \"build-demo\": \"mkdir -p ./build/demo && cp -r ./demo ./build/\",\n        \"build-library\": \"npx rollup -c && mkdir -p ./build/demo/lib && cp ./build/gaussian-splat-renderer-for-lam.module.* ./build/demo/lib/\",\n        \"build\": \"\n=== https://registry.npmjs.org/gaussian-splat-renderer-for-lam ===\n{\"_id\":\"gaussian-splat-renderer-for-lam\",\"_rev\":\"11-afddcad67ec2068faf754641cf42d126\",\"name\":\"gaussian-splat-renderer-for-lam\",\"dist-tags\":{\"latest\":\"0.0.9-alpha.2\"},\"versions\":{\"0.0.1\":{\"name\":\"gaussian-splat-renderer-for-lam\",\"version\":\"0.0.1\",\"keywords\":[\"three\",\"threejs\",\"three.js\",\"splatting\",\"3D\",\"gaussian\",\"webgl\",\"javascript\",\"lam\"],\"author\":{\"name\":\"Xiaodan Ye\"},\"license\":\"MIT\",\"_id\":\"gaussian-splat-renderer-for-lam@0.0.1\",\"maintainers\":[{\"name\":\"yexiaodan0222\",\"email\":\"hey_yxd@qq.com\"}],\"dist\":{\"shasum\":\"98b9d4e3c9979cf1adb6c8ca46db096096d8eb3e\",\"tarball\":\"https://registry.npmjs.org/","is_error":false}]},"uuid":"57ddfc5f-b3fd-4967-8f2b-8910791dc259","timestamp":"2026-06-16T06:01:54.048Z","toolUseResult":{"stdout":"=== https://cdn.jsdelivr.net/npm/gaussian-splat-renderer-for-lam/package.json ===\n{\n    \"name\": \"gaussian-splat-renderer-for-lam\",\n    \"version\": \"0.0.9-alpha.2\",\n    \"description\": \"Three.js-based 3D Gaussian splat viewer for LAM\",\n    \"module\": \"build/gaussian-splat-renderer-for-lam.module.js\",\n    \"main\": \"build/gaussian-splat-renderer-for-lam.umd.cjs\",\n    \"author\": \"Xiaodan Ye\",\n    \"license\": \"MIT\",\n    \"type\": \"module\",\n    \"scripts\": {\n        \"build-demo\": \"mkdir -p ./build/demo && cp -r ./demo ./build/\",\n        \"build-library\": \"npx rollup -c && mkdir -p ./build/demo/lib && cp ./build/gaussian-splat-renderer-for-lam.module.* ./build/demo/lib/\",\n        \"build\": \"\n=== https://registry.npmjs.org/gaussian-splat-renderer-for-lam ===\n{\"_id\":\"gaussian-splat-renderer-for-lam\",\"_rev\":\"11-afddcad67ec2068faf754641cf42d126\",\"name\":\"gaussian-splat-renderer-for-lam\",\"dist-tags\":{\"latest\":\"0.0.9-alpha.2\"},\"versions\":{\"0.0.1\":{\"name\":\"gaussian-splat-renderer-for-lam\",\"version\":\"0.0.1\",\"keywords\":[\"three\",\"threejs\",\"three.js\",\"splatting\",\"3D\",\"gaussian\",\"webgl\",\"javascript\",\"lam\"],\"author\":{\"name\":\"Xiaodan Ye\"},\"license\":\"MIT\",\"_id\":\"gaussian-splat-renderer-for-lam@0.0.1\",\"maintainers\":[{\"name\":\"yexiaodan0222\",\"email\":\"hey_yxd@qq.com\"}],\"dist\":{\"shasum\":\"98b9d4e3c9979cf1adb6c8ca46db096096d8eb3e\",\"tarball\":\"https://registry.npmjs.org/","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"19048225-1456-487f-964f-2891eeb3bdc9","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"57ddfc5f-b3fd-4967-8f2b-8910791dc259","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01MuEhrs5Vd9tCGoWLnzvMU5","type":"message","role":"assistant","content":[{"type":"text","text":"Le package existe et est dispo via CDN. Je récupère son API."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2441,"cache_read_input_tokens":93069,"output_tokens":386,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2441,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":386,"cache_read_input_tokens":93069,"cache_creation_input_tokens":2441,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2441},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Rvo6v4v4iqAoM67w7Q","type":"assistant","uuid":"0935530d-c87a-4437-afd5-4e1d8f20fccd","timestamp":"2026-06-16T06:01:56.643Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"0935530d-c87a-4437-afd5-4e1d8f20fccd","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01MuEhrs5Vd9tCGoWLnzvMU5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016s2Pj9pM7jzuVCTyP1QbQh","name":"Bash","input":{"command":"# Get the module entry and check exported API\ncurl -s --max-time 20 \"https://cdn.jsdelivr.net/npm/gaussian-splat-renderer-for-lam@0.0.9-alpha.2/build/gaussian-splat-renderer-for-lam.module.js\" -o /tmp/lam_renderer.js\nwc -c /tmp/lam_renderer.js\necho \"=== exports ===\"\ngrep -oE \"export\\{[^}]*\\}|export \\{[^}]*\\}\" /tmp/lam_renderer.js | head -5\necho \"=== class names / API ===\"\ngrep -oE \"GaussianSplatRenderer|getInstance|loadAvatar|updateBS|getExpressionData|startRender|class [A-Za-z]+\" /tmp/lam_renderer.js | sort -u | head -20","description":"Download and inspect LAM gaussian renderer module API"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2441,"cache_read_input_tokens":93069,"output_tokens":386,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2441,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":386,"cache_read_input_tokens":93069,"cache_creation_input_tokens":2441,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2441},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Rvo6v4v4iqAoM67w7Q","type":"assistant","uuid":"2674dabe-32d8-4d32-bde4-0713ffc457dd","timestamp":"2026-06-16T06:01:59.895Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"619bc90f-fbf0-45bc-8539-fbe059f6d860","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01JGt185aBgwm2NCsBCBwHZN","type":"message","role":"assistant","content":[{"type":"text","text":"Le fetch marche vers stdout (souci d'écriture /tmp en sandbox). Je récupère le contenu et l'API via pipe, et je regarde la démo officielle pour l'usage."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":304,"cache_read_input_tokens":96303,"output_tokens":452,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":304,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":452,"cache_read_input_tokens":96303,"cache_creation_input_tokens":304,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":304},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Rww6fAe9mNxPtDqQFb","type":"assistant","uuid":"c72ba453-c20b-407e-abb0-3cc7c803d74f","timestamp":"2026-06-16T06:02:13.980Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"c72ba453-c20b-407e-abb0-3cc7c803d74f","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01JGt185aBgwm2NCsBCBwHZN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011xfNcwjDc4rkxdKcjo6WVV","name":"Bash","input":{"command":"curl -sL --max-time 30 \"https://cdn.jsdelivr.net/npm/gaussian-splat-renderer-for-lam@0.0.9-alpha.2/build/gaussian-splat-renderer-for-lam.module.js\" > /mnt/DATA/WORK/agent-gui/static/lib_lam_renderer.js 2>&1\nwc -c /mnt/DATA/WORK/agent-gui/static/lib_lam_renderer.js\necho \"=== exports ===\"\ngrep -oaE \"export\\{[^}]*\\}\" /mnt/DATA/WORK/agent-gui/static/lib_lam_renderer.js | tail -3","description":"Save renderer to project static dir and inspect exports"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":304,"cache_read_input_tokens":96303,"output_tokens":452,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":304,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":452,"cache_read_input_tokens":96303,"cache_creation_input_tokens":304,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":304},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Rww6fAe9mNxPtDqQFb","type":"assistant","uuid":"c33691fe-85e6-4e8d-9b6c-c4e360c44952","timestamp":"2026-06-16T06:02:16.702Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"8fd2d102-e2b1-4d87-adc1-3463b03b44df","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01TXmWJNdogi5WnNFJ8CpZPk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RCMjCjuN4v2vgq2z8xgTKY","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n# Find the export statement (could be export{ at end)\ngrep -oaE \"export ?\\{[A-Za-z0-9_, $]*\\}\" lib_lam_renderer.js | tail -5\necho \"=== GaussianSplatRenderer usages ===\"\ngrep -oa \"GaussianSplatRenderer\" lib_lam_renderer.js | sort -u\necho \"=== key methods ===\"\ngrep -oaE \"getInstance|loadAvatar|updateBS|getExpressionData|startRenderEvent|setExpressionData|class GaussianSplatRenderer\" lib_lam_renderer.js | sort | uniq -c","description":"Find exports and key methods in renderer"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":500,"cache_read_input_tokens":96607,"output_tokens":313,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":500,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":313,"cache_read_input_tokens":96607,"cache_creation_input_tokens":500,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":500},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RxWAk1pFNmkPsbEDkn","type":"assistant","uuid":"1adaf86e-5e0d-48c4-b85b-ecfba32531ed","timestamp":"2026-06-16T06:02:23.438Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"1adaf86e-5e0d-48c4-b85b-ecfba32531ed","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RCMjCjuN4v2vgq2z8xgTKY","type":"tool_result","content":"export { AbortablePromise, DropInViewer, GaussianSplatRenderer, KSplatLoader, Utils as LoaderUtils, LogLevel, OrbitControls, PlayCanvasCompressedPlyParser, PlyLoader, PlyParser, RenderMode, SceneFormat, SceneRevealMode, SplatBuffer, SplatBufferGenerator, SplatLoader, SplatParser, SplatPartitioner, SplatRenderMode, Viewer, WebXRMode }\n=== GaussianSplatRenderer usages ===\nGaussianSplatRenderer\n=== key methods ===\n      1 class GaussianSplatRenderer\n      4 getExpressionData\n      5 getInstance\n      3 updateBS\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"ec9b8bec-4be1-4c7b-a717-9aa1bce97d75","timestamp":"2026-06-16T06:02:23.527Z","toolUseResult":{"stdout":"export { AbortablePromise, DropInViewer, GaussianSplatRenderer, KSplatLoader, Utils as LoaderUtils, LogLevel, OrbitControls, PlayCanvasCompressedPlyParser, PlyLoader, PlyParser, RenderMode, SceneFormat, SceneRevealMode, SplatBuffer, SplatBufferGenerator, SplatLoader, SplatParser, SplatPartitioner, SplatRenderMode, Viewer, WebXRMode }\n=== GaussianSplatRenderer usages ===\nGaussianSplatRenderer\n=== key methods ===\n      1 class GaussianSplatRenderer\n      4 getExpressionData\n      5 getInstance\n      3 updateBS","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"1adaf86e-5e0d-48c4-b85b-ecfba32531ed","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"1a64610a-e50c-4ef2-884b-94cf32579094","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Vu1cTV4tycJKr6pKW5qZsx","type":"tool_result","content":"# 3D Gaussian splatting for Three.js\n\n3D Gaussian Splatting Renderer for [LAM: Large Avatar Model for One-shot Animatable Gaussian Head](https://github.com/aigc3d/LAM), developed based on [@mkkellogg/gaussian-splats-3d](https://github.com/mkkellogg/GaussianSplats3D).]","is_error":false}]},"uuid":"9e9690ef-879f-4f36-bde7-e56f4c89a8d8","timestamp":"2026-06-16T06:02:37.798Z","toolUseResult":{"stdout":"# 3D Gaussian splatting for Three.js\n\n3D Gaussian Splatting Renderer for [LAM: Large Avatar Model for One-shot Animatable Gaussian Head](https://github.com/aigc3d/LAM), developed based on [@mkkellogg/gaussian-splats-3d](https://github.com/mkkellogg/GaussianSplats3D).]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"1a64610a-e50c-4ef2-884b-94cf32579094","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"9fa882a5-d3df-46a7-aac1-f8687e45f490","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_018HEBcmwCJRD6S9nLQ8aXp6","type":"message","role":"assistant","content":[{"type":"text","text":"Le README est minimal. Je reverse-engineer l'API `getInstance` depuis le bundle."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":276,"cache_read_input_tokens":98401,"output_tokens":583,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":276,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":583,"cache_read_input_tokens":98401,"cache_creation_input_tokens":276,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":276},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Rz27LQfX7okjLaqxLm","type":"assistant","uuid":"687ccbc9-1f1a-4888-9310-a840278bedca","timestamp":"2026-06-16T06:02:45.527Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"687ccbc9-1f1a-4888-9310-a840278bedca","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_018HEBcmwCJRD6S9nLQ8aXp6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01N1Z6Xr3JzH3GFoSCbtd3WE","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n$HOME/miniconda3/bin/python -c \"\ndata = open('lib_lam_renderer.js').read()\n# Find getInstance definition\nimport re\nfor m in re.finditer(r'getInstance', data):\n    i = m.start()\n    snippet = data[i:i+400]\n    if 'static' in data[max(0,i-20):i] or '(' in data[i:i+30]:\n        print('---', i, '---')\n        print(snippet[:400])\n        print()\n\" 2>&1 | head -60","description":"Extract getInstance signature from bundle"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":276,"cache_read_input_tokens":98401,"output_tokens":583,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":276,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":583,"cache_read_input_tokens":98401,"cache_creation_input_tokens":276,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":276},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Rz27LQfX7okjLaqxLm","type":"assistant","uuid":"f28abc38-3924-4cf5-9408-e5cfb663428a","timestamp":"2026-06-16T06:02:47.629Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"f28abc38-3924-4cf5-9408-e5cfb663428a","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01N1Z6Xr3JzH3GFoSCbtd3WE","type":"tool_result","content":"--- 2123150 ---\ngetInstanceIndex();\n\n\t\t} else if ( scope === IndexNode.DRAW ) {\n\n\t\t\tpropertyName = builder.getDrawIndex();\n\n\t\t} else if ( scope === IndexNode.INVOCATION_LOCAL ) {\n\n\t\t\tpropertyName = builder.getInvocationLocalIndex();\n\n\t\t} else if ( scope === IndexNode.INVOCATION_SUBGROUP ) {\n\n\t\t\tpropertyName = builder.getInvocationSubgroupIndex();\n\n\t\t} else if ( scope === IndexNode.SUBGROUP ) {\n\n\t\t\tpropertyName = \n\n--- 2845207 ---\ngetInstanceIndex() {\n\n\t\tconsole.warn( 'Abstract function.' );\n\n\t}\n\n\t/**\n\t * Returns the drawIndex input variable as a native shader string.\n\t * Only relevant for WebGL and its `WEBGL_multi_draw` extension.\n\t *\n\t * @abstract\n\t * @return {String} The drawIndex shader string.\n\t */\n\tgetDrawIndex() {\n\n\t\tconsole.warn( 'Abstract function.' );\n\n\t}\n\n\t/**\n\t * Returns the frontFacing input variable as a nati\n\n--- 3083526 ---\ngetInstanceIndex() {\n\n\t\treturn 'uint( gl_InstanceID )';\n\n\t}\n\n\t/**\n\t * Returns the invocation local index builtin.\n\t *\n\t * @return {String} The invocation local index.\n\t */\n\tgetInvocationLocalIndex() {\n\n\t\tconst workgroupSize = this.object.workgroupSize;\n\n\t\tconst size = workgroupSize.reduce( ( acc, curr ) => acc * curr, 1 );\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"e3e4a4c4-86bc-4d5f-a0f7-bc312e3eab4b","timestamp":"2026-06-16T06:02:47.775Z","toolUseResult":{"stdout":"--- 2123150 ---\ngetInstanceIndex();\n\n\t\t} else if ( scope === IndexNode.DRAW ) {\n\n\t\t\tpropertyName = builder.getDrawIndex();\n\n\t\t} else if ( scope === IndexNode.INVOCATION_LOCAL ) {\n\n\t\t\tpropertyName = builder.getInvocationLocalIndex();\n\n\t\t} else if ( scope === IndexNode.INVOCATION_SUBGROUP ) {\n\n\t\t\tpropertyName = builder.getInvocationSubgroupIndex();\n\n\t\t} else if ( scope === IndexNode.SUBGROUP ) {\n\n\t\t\tpropertyName = \n\n--- 2845207 ---\ngetInstanceIndex() {\n\n\t\tconsole.warn( 'Abstract function.' );\n\n\t}\n\n\t/**\n\t * Returns the drawIndex input variable as a native shader string.\n\t * Only relevant for WebGL and its `WEBGL_multi_draw` extension.\n\t *\n\t * @abstract\n\t * @return {String} The drawIndex shader string.\n\t */\n\tgetDrawIndex() {\n\n\t\tconsole.warn( 'Abstract function.' );\n\n\t}\n\n\t/**\n\t * Returns the frontFacing input variable as a nati\n\n--- 3083526 ---\ngetInstanceIndex() {\n\n\t\treturn 'uint( gl_InstanceID )';\n\n\t}\n\n\t/**\n\t * Returns the invocation local index builtin.\n\t *\n\t * @return {String} The invocation local index.\n\t */\n\tgetInvocationLocalIndex() {\n\n\t\tconst workgroupSize = this.object.workgroupSize;\n\n\t\tconst size = workgroupSize.reduce( ( acc, curr ) => acc * curr, 1 );","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"f28abc38-3924-4cf5-9408-e5cfb663428a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e3e4a4c4-86bc-4d5f-a0f7-bc312e3eab4b","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01Radj8K9asCJSdT7p2qesva","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VFmatQNokbF2Z13tZeSD2M","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n$HOME/miniconda3/bin/python -c \"\ndata = open('lib_lam_renderer.js').read()\nimport re\n# Find 'class GaussianSplatRenderer'\ni = data.find('class GaussianSplatRenderer')\nprint('=== class def at', i, '===')\nprint(data[i:i+1500])\n\" 2>&1","description":"Extract GaussianSplatRenderer class definition"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1159,"cache_read_input_tokens":98677,"output_tokens":218,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1159,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":218,"cache_read_input_tokens":98677,"cache_creation_input_tokens":1159,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1159},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6RzkpribxNUBYGTyRdK","type":"assistant","uuid":"a7a806ca-b08e-455c-a40f-656cf070deb8","timestamp":"2026-06-16T06:02:53.411Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a7a806ca-b08e-455c-a40f-656cf070deb8","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VFmatQNokbF2Z13tZeSD2M","type":"tool_result","content":"=== class def at 4046784 ===\nclass GaussianSplatRenderer {\n    static getInstance(container, assetPath, options) {\n        var _a, _b, _c, _d, _e, _f;\n        return __awaiter(this, void 0, void 0, function* () {\n            if (this.instance != undefined) {\n                return this.instance;\n            }\n            try {\n                const characterPath = assetPath;\n                const { pathname } = urlParse(characterPath);\n                const matches = pathname.match(/\\/([^/]+?)\\.zip/);\n                const characterName = matches && matches[1];\n                if (!characterName) {\n                    throw new Error('character model is not found');\n                }\n                NProgress.start();\n                const characterZipResponse = yield axios.get(characterPath, {\n                    responseType: 'arraybuffer',\n                    timeout: 100000,\n                    onDownloadProgress: (progressEvent) => {\n                        if (progressEvent.lengthComputable && progressEvent && progressEvent.total) {\n                            const percent = (progressEvent.loaded / progressEvent.total);\n                            // console.log(`Downloaded ${percent * 100.0}%`);\n                            NProgress.set(percent);\n                            (options === null || options === void 0 ? void 0 : options.downloadProgress) && (options === null || options === void 0 ? void 0 : options.downloadProgress(percent));\n                        }\n                    }\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"e162e256-761e-483b-ab85-01a6a202f04a","timestamp":"2026-06-16T06:02:53.510Z","toolUseResult":{"stdout":"=== class def at 4046784 ===\nclass GaussianSplatRenderer {\n    static getInstance(container, assetPath, options) {\n        var _a, _b, _c, _d, _e, _f;\n        return __awaiter(this, void 0, void 0, function* () {\n            if (this.instance != undefined) {\n                return this.instance;\n            }\n            try {\n                const characterPath = assetPath;\n                const { pathname } = urlParse(characterPath);\n                const matches = pathname.match(/\\/([^/]+?)\\.zip/);\n                const characterName = matches && matches[1];\n                if (!characterName) {\n                    throw new Error('character model is not found');\n                }\n                NProgress.start();\n                const characterZipResponse = yield axios.get(characterPath, {\n                    responseType: 'arraybuffer',\n                    timeout: 100000,\n                    onDownloadProgress: (progressEvent) => {\n                        if (progressEvent.lengthComputable && progressEvent && progressEvent.total) {\n                            const percent = (progressEvent.loaded / progressEvent.total);\n                            // console.log(`Downloaded ${percent * 100.0}%`);\n                            NProgress.set(percent);\n                            (options === null || options === void 0 ? void 0 : options.downloadProgress) && (options === null || options === void 0 ? void 0 : options.downloadProgress(percent));\n                        }\n                    }","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"a7a806ca-b08e-455c-a40f-656cf070deb8","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"je ne vois plus la liste des agents à gauche ( claude, hermes) etil n'y a pas d'avatar qui s'affichent","leafUuid":"e162e256-761e-483b-ab85-01a6a202f04a","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"permission-mode","permissionMode":"bypassPermissions","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"parentUuid":"e162e256-761e-483b-ab85-01a6a202f04a","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NXbPJue2FUs5eEkGk4KmHv","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":710,"cache_read_input_tokens":99836,"output_tokens":580,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":710,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":580,"cache_read_input_tokens":99836,"cache_creation_input_tokens":710,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":710},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S1BGXabAbBAsrZA7nw","type":"assistant","uuid":"b1fffad4-2fc1-4d44-8ecb-8730adc9ded4","timestamp":"2026-06-16T06:02:59.346Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"b1fffad4-2fc1-4d44-8ecb-8730adc9ded4","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NXbPJue2FUs5eEkGk4KmHv","type":"message","role":"assistant","content":[{"type":"text","text":"`getInstance(container, assetPath, options)` attend une **URL vers un .zip** contenant l'avatar complet, et `options.getExpressionData` est un callback appelé à chaque frame pour fournir les blendshapes. Je récupère le reste pour comprendre le format exact."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":710,"cache_read_input_tokens":99836,"output_tokens":580,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":710,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":580,"cache_read_input_tokens":99836,"cache_creation_input_tokens":710,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":710},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S1BGXabAbBAsrZA7nw","type":"assistant","uuid":"476184d8-3203-4ad4-a11c-c2c690f1ce4e","timestamp":"2026-06-16T06:03:01.227Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"476184d8-3203-4ad4-a11c-c2c690f1ce4e","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NXbPJue2FUs5eEkGk4KmHv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01STUCBxSmEJaT7c889wzcC4","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n$HOME/miniconda3/bin/python -c \"\ndata = open('lib_lam_renderer.js').read()\ni = data.find('class GaussianSplatRenderer')\nprint(data[i+1500:i+3500])\n\" 2>&1","description":"Extract more of GaussianSplatRenderer class"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":710,"cache_read_input_tokens":99836,"output_tokens":580,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":710,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":580,"cache_read_input_tokens":99836,"cache_creation_input_tokens":710,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":710},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S1BGXabAbBAsrZA7nw","type":"assistant","uuid":"dd342c1a-9bd1-404e-a635-d5136b61df53","timestamp":"2026-06-16T06:03:02.125Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"dd342c1a-9bd1-404e-a635-d5136b61df53","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01STUCBxSmEJaT7c889wzcC4","type":"tool_result","content":"     });\n                (options === null || options === void 0 ? void 0 : options.loadProgress) && (options === null || options === void 0 ? void 0 : options.loadProgress(0.1));\n                NProgress.done();\n                console.log('download completed:', characterZipResponse.data);\n                const arrayBuffer = characterZipResponse.data;\n                const zipData = yield jszip_minExports.loadAsync(arrayBuffer);\n                let fileName = '';\n                Object.values(zipData.files).forEach(file => {\n                    var _a, _b;\n                    if (file.dir) {\n                        fileName = (_a = file.name) === null || _a === void 0 ? void 0 : _a.slice(0, ((_b = file.name) === null || _b === void 0 ? void 0 : _b.length) - 1); // 去掉末尾的‘/’\n                    }\n                });\n                if (!fileName) {\n                    throw new Error('file fold is not found');\n                }\n                const renderer = new GaussianSplatRenderer(container, zipData);\n                const cameraPos = new Vector3();\n                cameraPos.x = ((_a = charactorConfig.camPos) === null || _a === void 0 ? void 0 : _a.x) || 0;\n                cameraPos.y = ((_b = charactorConfig.camPos) === null || _b === void 0 ? void 0 : _b.y) || 0;\n                cameraPos.z = ((_c = charactorConfig.camPos) === null || _c === void 0 ? void 0 : _c.z) || 1;\n                const cameraRotation = new Vector3();\n                cameraRotation.x = ((_d = charactorConfig.camRot) === null || _d === void 0 ? void 0 : _d.x) || 0;\n                cameraRotation.y = ((_e = charactorConfig.camRot) === null || _e === void 0 ? void 0 : _e.y) || 0;\n                cameraRotation.z = ((_f = charactorConfig.camRot) === null || _f === void 0 ? void 0 : _f.z) || 0;\n                let backgroundColor = 0xffffff;\n                if (charactorConfig.backgroundColor) {\n                    backgroundColor = parseInt(charactorConfig.backgroundColor, 16);\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"3ed6474d-32fc-4143-8dde-86df555c39cc","timestamp":"2026-06-16T06:03:02.177Z","toolUseResult":{"stdout":"     });\n                (options === null || options === void 0 ? void 0 : options.loadProgress) && (options === null || options === void 0 ? void 0 : options.loadProgress(0.1));\n                NProgress.done();\n                console.log('download completed:', characterZipResponse.data);\n                const arrayBuffer = characterZipResponse.data;\n                const zipData = yield jszip_minExports.loadAsync(arrayBuffer);\n                let fileName = '';\n                Object.values(zipData.files).forEach(file => {\n                    var _a, _b;\n                    if (file.dir) {\n                        fileName = (_a = file.name) === null || _a === void 0 ? void 0 : _a.slice(0, ((_b = file.name) === null || _b === void 0 ? void 0 : _b.length) - 1); // 去掉末尾的‘/’\n                    }\n                });\n                if (!fileName) {\n                    throw new Error('file fold is not found');\n                }\n                const renderer = new GaussianSplatRenderer(container, zipData);\n                const cameraPos = new Vector3();\n                cameraPos.x = ((_a = charactorConfig.camPos) === null || _a === void 0 ? void 0 : _a.x) || 0;\n                cameraPos.y = ((_b = charactorConfig.camPos) === null || _b === void 0 ? void 0 : _b.y) || 0;\n                cameraPos.z = ((_c = charactorConfig.camPos) === null || _c === void 0 ? void 0 : _c.z) || 1;\n                const cameraRotation = new Vector3();\n                cameraRotation.x = ((_d = charactorConfig.camRot) === null || _d === void 0 ? void 0 : _d.x) || 0;\n                cameraRotation.y = ((_e = charactorConfig.camRot) === null || _e === void 0 ? void 0 : _e.y) || 0;\n                cameraRotation.z = ((_f = charactorConfig.camRot) === null || _f === void 0 ? void 0 : _f.z) || 0;\n                let backgroundColor = 0xffffff;\n                if (charactorConfig.backgroundColor) {\n                    backgroundColor = parseInt(charactorConfig.backgroundColor, 16);","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"dd342c1a-9bd1-404e-a635-d5136b61df53","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"2657bb3f-08d3-443b-86d2-6117aca041e3","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01VhBksxGswPdevVGYyZgphb","type":"message","role":"assistant","content":[{"type":"text","text":"Le zip doit contenir un **dossier** (ex: `barbara/`) avec les fichiers dedans. Il lit aussi un `charactorConfig`. Je vérifie d'où vient cette config et comment `getExpressionData` est consommé."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1360,"cache_read_input_tokens":100546,"output_tokens":426,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1360,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":426,"cache_read_input_tokens":100546,"cache_creation_input_tokens":1360,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1360},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S1pMDM8cg5W7UeQYCd","type":"assistant","uuid":"e38fc84c-cf50-4a61-b23b-b416bf4bc456","timestamp":"2026-06-16T06:03:07.613Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e38fc84c-cf50-4a61-b23b-b416bf4bc456","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01VhBksxGswPdevVGYyZgphb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01E2zBZ1K6iitRhUPHu4TW5z","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n$HOME/miniconda3/bin/python -c \"\ndata = open('lib_lam_renderer.js').read()\n# Find charactorConfig source\nimport re\ni = data.find('charactorConfig')\nprint('=== first charactorConfig at', i, '===')\nprint(data[max(0,i-600):i+200])\n\" 2>&1","description":"Find charactorConfig source"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1360,"cache_read_input_tokens":100546,"output_tokens":426,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1360,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":426,"cache_read_input_tokens":100546,"cache_creation_input_tokens":1360,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1360},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S1pMDM8cg5W7UeQYCd","type":"assistant","uuid":"a8491910-f083-4aa6-a619-ffaaeaf9ade7","timestamp":"2026-06-16T06:03:09.143Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a8491910-f083-4aa6-a619-ffaaeaf9ade7","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01E2zBZ1K6iitRhUPHu4TW5z","type":"tool_result","content":"=== first charactorConfig at 4046622 ===\ncale: scale$1\n};\n\nvar hello = {\n\tsize: 2,\n\tisGroup: false\n};\nvar idle = {\n\tsize: 1,\n\tisGroup: false\n};\nvar listen = {\n\tsize: 0,\n\tisGroup: false\n};\nvar speak = {\n\tsize: 6,\n\tisGroup: false\n};\nvar think = {\n\tsize: 3,\n\tisGroup: true\n};\nvar other = [\n];\nvar animationConfig = {\n\thello: hello,\n\tidle: idle,\n\tlisten: listen,\n\tspeak: speak,\n\tthink: think,\n\tother: other\n};\n\nvar name = \"\";\nvar position = {\n\tx: 0,\n\ty: 0,\n\tz: 0\n};\nvar scale = {\n\tx: 1,\n\ty: 1,\n\tz: 1\n};\nvar camPos = {\n\tx: 0,\n\ty: 1.8,\n\tz: 1\n};\nvar camRot = {\n\tx: -10,\n\ty: 0,\n\tz: 0\n};\nvar backgroundColor = \"0xffffff\";\nvar useFlame = \"false\";\nvar charactorConfig = {\n\tname: name,\n\tposition: position,\n\tscale: scale,\n\tcamPos: camPos,\n\tcamRot: camRot,\n\tbackgroundColor: backgroundColor,\n\tuseFlame: useFlame\n};\n\nclass GaussianSplatRenderer {\n    stat\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"e4a8c385-b6ed-409c-9803-02872736b7f0","timestamp":"2026-06-16T06:03:09.235Z","toolUseResult":{"stdout":"=== first charactorConfig at 4046622 ===\ncale: scale$1\n};\n\nvar hello = {\n\tsize: 2,\n\tisGroup: false\n};\nvar idle = {\n\tsize: 1,\n\tisGroup: false\n};\nvar listen = {\n\tsize: 0,\n\tisGroup: false\n};\nvar speak = {\n\tsize: 6,\n\tisGroup: false\n};\nvar think = {\n\tsize: 3,\n\tisGroup: true\n};\nvar other = [\n];\nvar animationConfig = {\n\thello: hello,\n\tidle: idle,\n\tlisten: listen,\n\tspeak: speak,\n\tthink: think,\n\tother: other\n};\n\nvar name = \"\";\nvar position = {\n\tx: 0,\n\ty: 0,\n\tz: 0\n};\nvar scale = {\n\tx: 1,\n\ty: 1,\n\tz: 1\n};\nvar camPos = {\n\tx: 0,\n\ty: 1.8,\n\tz: 1\n};\nvar camRot = {\n\tx: -10,\n\ty: 0,\n\tz: 0\n};\nvar backgroundColor = \"0xffffff\";\nvar useFlame = \"false\";\nvar charactorConfig = {\n\tname: name,\n\tposition: position,\n\tscale: scale,\n\tcamPos: camPos,\n\tcamRot: camRot,\n\tbackgroundColor: backgroundColor,\n\tuseFlame: useFlame\n};\n\nclass GaussianSplatRenderer {\n    stat","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"a8491910-f083-4aa6-a619-ffaaeaf9ade7","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e4a8c385-b6ed-409c-9803-02872736b7f0","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NdKxtu8yUvYDGb2BwGu5ya","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"EuIDCmMIDhgCKkCTbto/3U7b4KoNjcve0ab/+OYyLsQxhNrAoJOMvdLtAy2mEWXvCKlnokxXqdO5Gwsm/k5R5zaQfqE5mv3C+gOeMg9jbGF1ZGUtb3B1cy00LTg4AEIIdGhpbmtpbmcSDFnvbiavVvwCHErrKhoM4iSRxm413w/BBIObIjCVu6LXoDwBLnmwDmBvuUWBmN1LzpVDAtmNmbqlRXoW+WOhLYSYAE5Q24G6206AZMcqrAIzT1SKwqozWGMvVOIvECK5hfDIyJI6lTac2oxBR8JhqfsXNPbiL+jglAdwg59TaBQR6DkSYWwx3ToshXTPsVTXotpa1mfgm0q23zGu0lAU036IWLofei1uHP3RMCP8iVyQW/4e1W8qDAv6DqlNYKGS9i61HCzK+WfMoeOVfNTl7u0AvPlvmc0kgKmvAVZj/EuoriFto0vSer3h7YO9i2PVZVUIG/w8OL7/70c5O+DluvG9lerMe0QQDaNUShkK1xVvSpd95QSM5Vyv4AAT5yXWEAmKU1v5RJkK2dsDY3LXi5swqUY++yIKYuZob9bKd1hfcdqmRYh6d0fQMMYuIFkJhc10Uqww5ZJ2ZHXrprQZgX3kNXgBEg2ddOT+RBN2s6MPezLK25Ddx/nFJFgYAQ=="}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":895,"cache_read_input_tokens":101906,"output_tokens":458,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":895,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":458,"cache_read_input_tokens":101906,"cache_creation_input_tokens":895,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":895},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S2LXffnt4d5B5R65so","type":"assistant","uuid":"4e1bc763-4a2c-445b-8ead-bd9170751b11","timestamp":"2026-06-16T06:03:12.796Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"4e1bc763-4a2c-445b-8ead-bd9170751b11","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NdKxtu8yUvYDGb2BwGu5ya","type":"message","role":"assistant","content":[{"type":"text","text":"`charactorConfig` est un défaut intégré. Maintenant comment `getExpressionData` (depuis options) est branché et consommé par frame."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":895,"cache_read_input_tokens":101906,"output_tokens":458,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":895,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":458,"cache_read_input_tokens":101906,"cache_creation_input_tokens":895,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":895},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S2LXffnt4d5B5R65so","type":"assistant","uuid":"e0ec782c-8911-40f0-a791-98edc7980183","timestamp":"2026-06-16T06:03:13.742Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e0ec782c-8911-40f0-a791-98edc7980183","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NdKxtu8yUvYDGb2BwGu5ya","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01H6WbAnxid7MY7m42A1Am1b","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n$HOME/miniconda3/bin/python -c \"\ndata = open('lib_lam_renderer.js').read()\nimport re\nfor kw in ['getExpressionData', 'updateBS', 'options.getExpressionData', 'this.getExpressionData']:\n    i = data.find(kw)\n    while i >= 0 and i < len(data):\n        seg = data[max(0,i-80):i+160]\n        print(f'--- {kw} @ {i} ---')\n        print(seg.replace(chr(10),' '))\n        print()\n        i = data.find(kw, i+1)\n        if i > 4040000: break\n\" 2>&1 | head -50","description":"Find getExpressionData and updateBS usage"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":895,"cache_read_input_tokens":101906,"output_tokens":458,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":895,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":458,"cache_read_input_tokens":101906,"cache_creation_input_tokens":895,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":895},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S2LXffnt4d5B5R65so","type":"assistant","uuid":"23734089-1722-4101-9609-5c81a1e451c0","timestamp":"2026-06-16T06:03:16.234Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"23734089-1722-4101-9609-5c81a1e451c0","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01H6WbAnxid7MY7m42A1Am1b","type":"tool_result","content":"--- getExpressionData @ 4050800 ---\n|| options === void 0 ? void 0 : options.getChatState;                 renderer.getExpressionData = options === null || options === void 0 ? void 0 : options.getExpressionData;                 if (charactorConfig.useFlame) {                \n\n--- updateBS @ 3888369 ---\ndle.update(state);                     break;             }         }     }     updateBS(actionData) {         let influence = {             browDownLeft: 0.0,             browDownRight: 0.0,             browInnerUp: 0.0,             browOu\n\n--- options.getExpressionData @ 4050870 ---\n renderer.getExpressionData = options === null || options === void 0 ? void 0 : options.getExpressionData;                 if (charactorConfig.useFlame) {                     renderer.useFlame = (charactorConfig.useFlame == \"false\") ? false\n\n--- this.getExpressionData @ 4057590 ---\nd 0 ? void 0 : _a.update(this.chatState);                 }                 if (this.getExpressionData) {                     this.expressionData = this.updateBS(this.getExpressionData());                 }                 if (this.viewer.u\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"1b381fd3-8ffd-4aab-a4ac-f151c23d0ae5","timestamp":"2026-06-16T06:03:16.323Z","toolUseResult":{"stdout":"--- getExpressionData @ 4050800 ---\n|| options === void 0 ? void 0 : options.getChatState;                 renderer.getExpressionData = options === null || options === void 0 ? void 0 : options.getExpressionData;                 if (charactorConfig.useFlame) {                \n\n--- updateBS @ 3888369 ---\ndle.update(state);                     break;             }         }     }     updateBS(actionData) {         let influence = {             browDownLeft: 0.0,             browDownRight: 0.0,             browInnerUp: 0.0,             browOu\n\n--- options.getExpressionData @ 4050870 ---\n renderer.getExpressionData = options === null || options === void 0 ? void 0 : options.getExpressionData;                 if (charactorConfig.useFlame) {                     renderer.useFlame = (charactorConfig.useFlame == \"false\") ? false\n\n--- this.getExpressionData @ 4057590 ---\nd 0 ? void 0 : _a.update(this.chatState);                 }                 if (this.getExpressionData) {                     this.expressionData = this.updateBS(this.getExpressionData());                 }                 if (this.viewer.u","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"23734089-1722-4101-9609-5c81a1e451c0","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"1b381fd3-8ffd-4aab-a4ac-f151c23d0ae5","isSidechain":false,"attachment":{"type":"task_reminder","content":[],"itemCount":0},"type":"attachment","uuid":"4c4ddb7f-b6a7-48f1-aa10-f49da32ba622","timestamp":"2026-06-16T06:03:16.325Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"4c4ddb7f-b6a7-48f1-aa10-f49da32ba622","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01XB9wgdRRvY4g3NdN82hrQc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01H4JPDx3t3n1Qa9GXrbjVNK","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n$HOME/miniconda3/bin/python -c \"\ndata = open('lib_lam_renderer.js').read()\ni = data.find('updateBS(actionData)')\nprint(data[i:i+900])\n\" 2>&1","description":"Read updateBS to understand expected input format"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":876,"cache_read_input_tokens":102801,"output_tokens":174,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":876,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":174,"cache_read_input_tokens":102801,"cache_creation_input_tokens":876,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":876},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S2rp5Ef1t42sH6PgxW","type":"assistant","uuid":"691a4174-b647-4390-9477-846e09bd752f","timestamp":"2026-06-16T06:03:20.111Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"691a4174-b647-4390-9477-846e09bd752f","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01H4JPDx3t3n1Qa9GXrbjVNK","type":"tool_result","content":"updateBS(actionData) {\n        let influence = {\n            browDownLeft: 0.0,\n            browDownRight: 0.0,\n            browInnerUp: 0.0,\n            browOuterUpLeft: 0.0,\n            browOuterUpRight: 0.0,\n            mouthCheekPuff: 0.0,\n            cheekSquintLeft: 0.0,\n            cheekSquintRight: 0.0,\n            eyeBlinkLeft: 0.0,\n            eyeBlinkRight: 0.0,\n            eyeLookDownLeft: 0.0,\n            eyeLookDownRight: 0.0,\n            eyeLookInLeft: 0.0,\n            eyeLookInRight: 0.0,\n            eyeLookOutLeft: 0.0,\n            eyeLookOutRight: 0.0,\n            eyeLookUpLeft: 0.0,\n            eyeLookUpRight: 0.0,\n            eyeSquintLeft: 0.0,\n            eyeSquintRight: 0.0,\n            eyeWideLeft: 0.0,\n            eyeWideRight: 0.0,\n            jawForward: 0.0,\n            jawLeft: 0.0,\n            jawOpen: 0.0,\n            jawRight: 0.0,\n            mouthClose:\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"91d5b48d-aaa9-4b68-b5b4-44de2c727bd2","timestamp":"2026-06-16T06:03:20.206Z","toolUseResult":{"stdout":"updateBS(actionData) {\n        let influence = {\n            browDownLeft: 0.0,\n            browDownRight: 0.0,\n            browInnerUp: 0.0,\n            browOuterUpLeft: 0.0,\n            browOuterUpRight: 0.0,\n            mouthCheekPuff: 0.0,\n            cheekSquintLeft: 0.0,\n            cheekSquintRight: 0.0,\n            eyeBlinkLeft: 0.0,\n            eyeBlinkRight: 0.0,\n            eyeLookDownLeft: 0.0,\n            eyeLookDownRight: 0.0,\n            eyeLookInLeft: 0.0,\n            eyeLookInRight: 0.0,\n            eyeLookOutLeft: 0.0,\n            eyeLookOutRight: 0.0,\n            eyeLookUpLeft: 0.0,\n            eyeLookUpRight: 0.0,\n            eyeSquintLeft: 0.0,\n            eyeSquintRight: 0.0,\n            eyeWideLeft: 0.0,\n            eyeWideRight: 0.0,\n            jawForward: 0.0,\n            jawLeft: 0.0,\n            jawOpen: 0.0,\n            jawRight: 0.0,\n            mouthClose:","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"691a4174-b647-4390-9477-846e09bd752f","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"91d5b48d-aaa9-4b68-b5b4-44de2c727bd2","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01UiM3XAHuBAZGCMvC775nv1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Xs2UDVhKGPQHhKXXuoCyfv","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n$HOME/miniconda3/bin/python -c \"\ndata = open('lib_lam_renderer.js').read()\ni = data.find('updateBS(actionData)')\n# Skip past the influence dict default (find end of it after tongueOut)\nj = data.find('tongueOut', i)\nprint(data[j:j+1100])\n\" 2>&1","description":"Read updateBS body after influence defaults"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":762,"cache_read_input_tokens":103677,"output_tokens":214,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":762,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":214,"cache_read_input_tokens":103677,"cache_creation_input_tokens":762,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":762},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S39RTSzKBaFoRHXErR","type":"assistant","uuid":"ee96cf4e-70ce-4cba-aec7-63ba43ef5591","timestamp":"2026-06-16T06:03:26.749Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"ee96cf4e-70ce-4cba-aec7-63ba43ef5591","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Xs2UDVhKGPQHhKXXuoCyfv","type":"tool_result","content":"tongueOut: 0.0\n        };\n        if (actionData != null) {\n            influence = actionData;\n        }\n        return influence;\n    }\n}\nAnimationManager.IsBlending = false;\nAnimationManager.actions = [];\nAnimationManager.NeedReset = false;\nAnimationManager.NeedFullReset = false;\nclass State {\n    constructor(_actions, _isGroup) {\n        this.isPlaying = false;\n        this.stage = 0;\n        this.actions = [];\n        this.blendingTime = 0.5;\n        this.isGroup = false;\n        this.actions = _actions;\n        this.isGroup = _isGroup;\n    }\n    dispose() {\n        this.actions = [];\n    }\n    // eslint-disable-next-line @typescript-eslint/no-unused-vars\n    update(state) {\n    }\n}\nclass Hello extends State {\n    constructor(_actions, _isGroup) {\n        super(_actions, _isGroup);\n    }\n    update(state) {\n        if (AnimationManager.CurPlaying == undefined &&\n            state == TYVoiceChatState.Idle &&\n            this.isPlaying == false) {\n            this.stage = 0;\n            this.actions[this.stage].time = 0;\n            AnimationManager.SetWeight(this.actions[this.sta\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"938eed12-7d0b-493c-aadf-95cc8edc345c","timestamp":"2026-06-16T06:03:26.826Z","toolUseResult":{"stdout":"tongueOut: 0.0\n        };\n        if (actionData != null) {\n            influence = actionData;\n        }\n        return influence;\n    }\n}\nAnimationManager.IsBlending = false;\nAnimationManager.actions = [];\nAnimationManager.NeedReset = false;\nAnimationManager.NeedFullReset = false;\nclass State {\n    constructor(_actions, _isGroup) {\n        this.isPlaying = false;\n        this.stage = 0;\n        this.actions = [];\n        this.blendingTime = 0.5;\n        this.isGroup = false;\n        this.actions = _actions;\n        this.isGroup = _isGroup;\n    }\n    dispose() {\n        this.actions = [];\n    }\n    // eslint-disable-next-line @typescript-eslint/no-unused-vars\n    update(state) {\n    }\n}\nclass Hello extends State {\n    constructor(_actions, _isGroup) {\n        super(_actions, _isGroup);\n    }\n    update(state) {\n        if (AnimationManager.CurPlaying == undefined &&\n            state == TYVoiceChatState.Idle &&\n            this.isPlaying == false) {\n            this.stage = 0;\n            this.actions[this.stage].time = 0;\n            AnimationManager.SetWeight(this.actions[this.sta","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"ee96cf4e-70ce-4cba-aec7-63ba43ef5591","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"je ne vois plus la liste des agents à gauche ( claude, hermes) etil n'y a pas d'avatar qui s'affichent","leafUuid":"938eed12-7d0b-493c-aadf-95cc8edc345c","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"parentUuid":"938eed12-7d0b-493c-aadf-95cc8edc345c","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NRppf6enmMgcMdmhowYCVk","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":670,"cache_read_input_tokens":104439,"output_tokens":905,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":670,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":905,"cache_read_input_tokens":104439,"cache_creation_input_tokens":670,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":670},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S3eC5UuPfm9fDGUMVm","type":"assistant","uuid":"baa67f4a-a6cb-41e7-8d38-5f298817ca51","timestamp":"2026-06-16T06:03:37.013Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"baa67f4a-a6cb-41e7-8d38-5f298817ca51","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NRppf6enmMgcMdmhowYCVk","type":"message","role":"assistant","content":[{"type":"text","text":"`getExpressionData()` doit retourner un objet `{blendshapeName: weight, ...}`. Voyons comment le composant Gradio (qui marche déjà) construit ce frame, pour copier exactement le mapping de noms."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":670,"cache_read_input_tokens":104439,"output_tokens":905,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":670,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":905,"cache_read_input_tokens":104439,"cache_creation_input_tokens":670,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":670},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S3eC5UuPfm9fDGUMVm","type":"assistant","uuid":"f953205b-6a8d-4913-ad09-4e74069c7fec","timestamp":"2026-06-16T06:03:37.950Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"f953205b-6a8d-4913-ad09-4e74069c7fec","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NRppf6enmMgcMdmhowYCVk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HaHRGw75FxodpEGScSqeqN","name":"Bash","input":{"command":"$HOME/miniconda3/bin/python -c \"\ndata = open('/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/gradio_gaussian_render/templates/component/index.js','rb').read().decode('utf-8','ignore')\ni = data.find('getArkitFaceFrame')\nwhile i >= 0:\n    print('---', i, '---')\n    print(data[i:i+700].replace(chr(10),' '))\n    print()\n    i = data.find('getArkitFaceFrame', i+1)\n\" 2>&1 | head -40","description":"Find getArkitFaceFrame in gradio bundle"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":670,"cache_read_input_tokens":104439,"output_tokens":905,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":670,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":905,"cache_read_input_tokens":104439,"cache_creation_input_tokens":670,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":670},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S3eC5UuPfm9fDGUMVm","type":"assistant","uuid":"fb651129-4966-4c7c-af0e-9de95fa8ae33","timestamp":"2026-06-16T06:03:40.140Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"fb651129-4966-4c7c-af0e-9de95fa8ae33","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HaHRGw75FxodpEGScSqeqN","type":"tool_result","content":"--- 2255840 ---\ngetArkitFaceFrame(e) {     const t = e.frames.length, n = 1 / 30, s = (performance.now() / 1e3 - this.startTime) % (t * n), a = Math.floor(s / n);     let o = {};     return e.names.forEach((l, c) => {       o[l] = e.frames[a].weights[c];     }), o;   }   render() {     var e;     if (this.viewer && this.viewer.selfDrivenMode) {       if (this.viewer.webXRMode)         this.viewer.renderer.setAnimationLoop(this.viewer.selfDrivenUpdateFunc);       else {         this.viewer.requestFrameId = requestAnimationFrame(() => this.render());         const t = 1 / 30, i = (performance.now() / 1e3 - this.startTime) % (this.viewer.totalFrames * t), s = Math.floor(i / t);         if (this.viewer.frame = \n\n--- 2259934 ---\ngetArkitFaceFrame(o)), !this.viewer)         throw new Error(\"render viewer is not initialized\");       let l, c;       i.traverse((u) => {         u.isSkinnedMesh && (l = u), u instanceof Pu && u.name == \"hip\" && (c = u);       }), this.viewer.sortedIndexes = a, l != null && (this.viewer.gaussianSplatCount = l.geometry.attributes.position.count), this.viewer.avatarMesh = i, this.viewer.skinModel = l, this.viewer.boneRoot = c, this.mixer = new rR(i), this.animManager = new Ye(this.mixer, s, t), this.motioncfg = n, l != null && this.viewer.updateMorphTarget(l), this.viewer.threeScene.add(i), i.visible = !1, l != null && l.skeleton.computeBoneTexture();     });   }   unpackFileAsBlob(e) {","is_error":false}]},"uuid":"05bef60f-27fe-4ac0-8c12-b65a86a9c5f1","timestamp":"2026-06-16T06:03:40.231Z","toolUseResult":{"stdout":"--- 2255840 ---\ngetArkitFaceFrame(e) {     const t = e.frames.length, n = 1 / 30, s = (performance.now() / 1e3 - this.startTime) % (t * n), a = Math.floor(s / n);     let o = {};     return e.names.forEach((l, c) => {       o[l] = e.frames[a].weights[c];     }), o;   }   render() {     var e;     if (this.viewer && this.viewer.selfDrivenMode) {       if (this.viewer.webXRMode)         this.viewer.renderer.setAnimationLoop(this.viewer.selfDrivenUpdateFunc);       else {         this.viewer.requestFrameId = requestAnimationFrame(() => this.render());         const t = 1 / 30, i = (performance.now() / 1e3 - this.startTime) % (this.viewer.totalFrames * t), s = Math.floor(i / t);         if (this.viewer.frame = \n\n--- 2259934 ---\ngetArkitFaceFrame(o)), !this.viewer)         throw new Error(\"render viewer is not initialized\");       let l, c;       i.traverse((u) => {         u.isSkinnedMesh && (l = u), u instanceof Pu && u.name == \"hip\" && (c = u);       }), this.viewer.sortedIndexes = a, l != null && (this.viewer.gaussianSplatCount = l.geometry.attributes.position.count), this.viewer.avatarMesh = i, this.viewer.skinModel = l, this.viewer.boneRoot = c, this.mixer = new rR(i), this.animManager = new Ye(this.mixer, s, t), this.motioncfg = n, l != null && this.viewer.updateMorphTarget(l), this.viewer.threeScene.add(i), i.visible = !1, l != null && l.skeleton.computeBoneTexture();     });   }   unpackFileAsBlob(e) {","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"fb651129-4966-4c7c-af0e-9de95fa8ae33","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"5c93683c-1454-4781-ada8-307a329362f4","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01RouFXf3fUUPEDy1ecNZ1jJ","type":"message","role":"assistant","content":[{"type":"text","text":"Tout est clair maintenant. Format `getExpressionData()` :\n```js\n// bsData a `names` + `frames[i].weights`\no[names[c]] = frames[currentFrame].weights[c]  // → {name: weight}\n```\n\nLe renderer est auto-suffisant (three.js + axios + jszip bundlés, 4MB). Je n'ai plus besoin de mon propre Three.js. Étapes :\n1. Créer le zip avatar avec un dossier `barbara/` dedans\n2. Réécrire `avatar.js` pour utiliser `GaussianSplatRenderer.getInstance`\n\nD'abord le zip."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1563,"cache_read_input_tokens":105109,"output_tokens":1543,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1563,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1543,"cache_read_input_tokens":105109,"cache_creation_input_tokens":1563,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1563},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S4d3euz6V6FTDnq1no","type":"assistant","uuid":"2d1d5343-db3b-49ff-b6bd-7e04989a2ff3","timestamp":"2026-06-16T06:03:56.283Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"2d1d5343-db3b-49ff-b6bd-7e04989a2ff3","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01RouFXf3fUUPEDy1ecNZ1jJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DP98B7DnteDjVmbv1Xbrin","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static/avatar\n# Build zip with a folder named 'barbara' containing the 5 files\nrm -f barbara.zip\nSRC=/mnt/DATA/WORK/LAM_Audio2Expression/assets/sample_lam/barbara/arkitWithBSData\nrm -rf /tmp/lam_barbara && mkdir -p /tmp/lam_barbara/barbara\ncp \"$SRC\"/animation.glb \"$SRC\"/skin.glb \"$SRC\"/offset.ply \"$SRC\"/vertex_order.json \"$SRC\"/bsData.json /tmp/lam_barbara/barbara/\ncd /tmp/lam_barbara && zip -r -q /mnt/DATA/WORK/agent-gui/static/avatar/barbara.zip barbara/\nls -lh /mnt/DATA/WORK/agent-gui/static/avatar/barbara.zip\nunzip -l /mnt/DATA/WORK/agent-gui/static/avatar/barbara.zip","description":"Build barbara avatar zip with folder structure"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1563,"cache_read_input_tokens":105109,"output_tokens":1543,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1563,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1543,"cache_read_input_tokens":105109,"cache_creation_input_tokens":1563,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1563},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S4d3euz6V6FTDnq1no","type":"assistant","uuid":"45e651d2-f1c8-4fa5-b488-67dc74222513","timestamp":"2026-06-16T06:04:00.181Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"45e651d2-f1c8-4fa5-b488-67dc74222513","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DP98B7DnteDjVmbv1Xbrin","type":"tool_result","content":"-rw-rw-r--+ 1 wildlama wildlama 4.1M Jun 16 08:04 /mnt/DATA/WORK/agent-gui/static/avatar/barbara.zip\nArchive:  /mnt/DATA/WORK/agent-gui/static/avatar/barbara.zip\n  Length      Date    Time    Name\n---------  ---------- -----   ----\n        0  2026-06-16 08:04   barbara/\n   395148  2026-06-16 08:04   barbara/bsData.json\n   209090  2026-06-16 08:04   barbara/vertex_order.json\n  1361640  2026-06-16 08:04   barbara/offset.ply\n  3612156  2026-06-16 08:04   barbara/skin.glb\n  2221524  2026-06-16 08:04   barbara/animation.glb\n---------                     -------\n  7799558                     6 files\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"dbadbb9b-fe3e-4cf2-8a9b-956634252cd1","timestamp":"2026-06-16T06:04:00.467Z","toolUseResult":{"stdout":"-rw-rw-r--+ 1 wildlama wildlama 4.1M Jun 16 08:04 /mnt/DATA/WORK/agent-gui/static/avatar/barbara.zip\nArchive:  /mnt/DATA/WORK/agent-gui/static/avatar/barbara.zip\n  Length      Date    Time    Name\n---------  ---------- -----   ----\n        0  2026-06-16 08:04   barbara/\n   395148  2026-06-16 08:04   barbara/bsData.json\n   209090  2026-06-16 08:04   barbara/vertex_order.json\n  1361640  2026-06-16 08:04   barbara/offset.ply\n  3612156  2026-06-16 08:04   barbara/skin.glb\n  2221524  2026-06-16 08:04   barbara/animation.glb\n---------                     -------\n  7799558                     6 files","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"45e651d2-f1c8-4fa5-b488-67dc74222513","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"dbadbb9b-fe3e-4cf2-8a9b-956634252cd1","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01VJWqhELk4NheAiNB3Tg8jY","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1863,"cache_read_input_tokens":106672,"output_tokens":387,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1863,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":387,"cache_read_input_tokens":106672,"cache_creation_input_tokens":1863,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1863},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S67auom2JkYDtrKVsa","type":"assistant","uuid":"393d6e78-0042-4085-979d-ec6bf1d5f203","timestamp":"2026-06-16T06:04:03.818Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"393d6e78-0042-4085-979d-ec6bf1d5f203","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01VJWqhELk4NheAiNB3Tg8jY","type":"message","role":"assistant","content":[{"type":"text","text":"Le zip est prêt. Vérifions les noms de fichiers exacts que le renderer cherche dans le zip."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1863,"cache_read_input_tokens":106672,"output_tokens":387,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1863,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":387,"cache_read_input_tokens":106672,"cache_creation_input_tokens":1863,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1863},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S67auom2JkYDtrKVsa","type":"assistant","uuid":"2d0e481c-2bb7-4ac2-bba6-00caa2ee2158","timestamp":"2026-06-16T06:04:04.784Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"2d0e481c-2bb7-4ac2-bba6-00caa2ee2158","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01VJWqhELk4NheAiNB3Tg8jY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016eMy1gxJ21Qp7LQGYxx6AS","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n$HOME/miniconda3/bin/python -c \"\ndata = open('lib_lam_renderer.js').read()\nimport re\nfor kw in ['skin.glb','offset.ply','animation.glb','vertex_order','bsData','.ply','.glb']:\n    cnt = data.count(kw)\n    i = data.find(kw)\n    seg = data[max(0,i-60):i+40].replace(chr(10),' ') if i>=0 else ''\n    print(f'{kw}: {cnt}x  | {seg}')\n\" 2>&1","description":"Find expected filenames in renderer bundle"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1863,"cache_read_input_tokens":106672,"output_tokens":387,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1863,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":387,"cache_read_input_tokens":106672,"cache_creation_input_tokens":1863,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1863},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S67auom2JkYDtrKVsa","type":"assistant","uuid":"99f49500-e5e1-4525-94c8-e3750121578b","timestamp":"2026-06-16T06:04:07.221Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"99f49500-e5e1-4525-94c8-e3750121578b","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016eMy1gxJ21Qp7LQGYxx6AS","type":"tool_result","content":"skin.glb: 2x  | se.all([                 this.unpackAndLoadGlb(pathName + '/skin.glb'),                 this.unpackA\noffset.ply: 1x  | ffsetFileUrl = yield renderer.unpackFileAsBlob(fileName + '/offset.ply');                 (options =\nanimation.glb: 1x  | n.glb'),                 this.unpackAndLoadGlb(pathName + '/animation.glb'),                 this.un\nvertex_order: 2x  | json'),                 this.unpackAndLoadJson(pathName + '/vertex_order.json'),                 thi\nbsData: 0x  | \n.ply: 10x  |   class INRIAV1PlyParser {      constructor() {         this.plyParserutils = new PlyParserUtils(); \n.glb: 3x  | ll([                 this.unpackAndLoadGlb(pathName + '/skin.glb'),                 this.unpackAndLo\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"7428a2a8-4ea1-43d6-ad25-3a1fda12ebfc","timestamp":"2026-06-16T06:04:07.325Z","toolUseResult":{"stdout":"skin.glb: 2x  | se.all([                 this.unpackAndLoadGlb(pathName + '/skin.glb'),                 this.unpackA\noffset.ply: 1x  | ffsetFileUrl = yield renderer.unpackFileAsBlob(fileName + '/offset.ply');                 (options =\nanimation.glb: 1x  | n.glb'),                 this.unpackAndLoadGlb(pathName + '/animation.glb'),                 this.un\nvertex_order: 2x  | json'),                 this.unpackAndLoadJson(pathName + '/vertex_order.json'),                 thi\nbsData: 0x  | \n.ply: 10x  |   class INRIAV1PlyParser {      constructor() {         this.plyParserutils = new PlyParserUtils(); \n.glb: 3x  | ll([                 this.unpackAndLoadGlb(pathName + '/skin.glb'),                 this.unpackAndLo","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"99f49500-e5e1-4525-94c8-e3750121578b","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"709518ef-66e8-49c5-b85a-70d0a0c50f88","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01AiDL6EjuFYRV3i329hDjb9","type":"message","role":"assistant","content":[{"type":"text","text":"Le renderer charge `skin.glb`, `animation.glb`, `offset.ply`, `vertex_order.json` depuis le dossier — `bsData` vient du callback. Mon zip est bon. Je vérifie le constructeur (container = div ?) et les options dispo."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":735,"cache_read_input_tokens":108535,"output_tokens":1234,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":735,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1234,"cache_read_input_tokens":108535,"cache_creation_input_tokens":735,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":735},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S6csYHYwMCuC9HtxPj","type":"assistant","uuid":"19b161b5-74ea-44b4-bcef-7189d93510c5","timestamp":"2026-06-16T06:04:20.662Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"19b161b5-74ea-44b4-bcef-7189d93510c5","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01AiDL6EjuFYRV3i329hDjb9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XCofRT193oaR491t8QSQxv","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui/static\n$HOME/miniconda3/bin/python -c \"\ndata = open('lib_lam_renderer.js').read()\n# constructor of GaussianSplatRenderer\ni = data.find('class GaussianSplatRenderer')\nj = data.find('constructor(', i)\nprint('=== constructor ===')\nprint(data[j:j+600])\nprint()\n# options usage block around getExpressionData assignment\nk = data.find('renderer.getExpressionData = options')\nprint('=== options block ===')\nprint(data[k-700:k+500])\n\" 2>&1","description":"Read constructor and options handling"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":735,"cache_read_input_tokens":108535,"output_tokens":1234,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":735,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1234,"cache_read_input_tokens":108535,"cache_creation_input_tokens":735,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":735},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S6csYHYwMCuC9HtxPj","type":"assistant","uuid":"d799c465-7f88-4cde-b158-baaa4ec1032e","timestamp":"2026-06-16T06:04:23.201Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"d799c465-7f88-4cde-b158-baaa4ec1032e","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XCofRT193oaR491t8QSQxv","type":"tool_result","content":"=== constructor ===\nconstructor(_container, zipData) {\n        this.zipUrls = {\n            urls: new Map()\n        };\n        this.useFlame = false;\n        this.lastTime = 0;\n        this.startTime = 0;\n        this.expressionData = {};\n        this.chatState = TYVoiceChatState.Idle;\n        Object.assign(this.zipUrls, {\n            zip: zipData\n        });\n        const { width, height } = _container.getBoundingClientRect();\n        GaussianSplatRenderer._canvas.style.visibility = 'visible';\n        GaussianSplatRenderer._canvas.width = width;\n        GaussianSplatRenderer._canvas.height = height;\n        _con\n\n=== options block ===\n         let backgroundColor = 0xffffff;\n                if (charactorConfig.backgroundColor) {\n                    backgroundColor = parseInt(charactorConfig.backgroundColor, 16);\n                }\n                if (options && options.backgroundColor && renderer.isHexColorStrict(options.backgroundColor)) {\n                    backgroundColor = parseInt(options.backgroundColor, 16);\n                }\n                let alpha = 0;\n                if (options && options.alpha) {\n                    alpha = Math.max(0, Math.min(1, options.alpha));\n                }\n                renderer.getChatState = options === null || options === void 0 ? void 0 : options.getChatState;\n                renderer.getExpressionData = options === null || options === void 0 ? void 0 : options.getExpressionData;\n                if (charactorConfig.useFlame) {\n                    renderer.useFlame = (charactorConfig.useFlame == \"false\") ? false : true;\n                }\n                console.log(cameraPos, backgroundColor, alpha);\n                renderer.viewer = new Viewer({\n                    rootElement: container,\n                    threejsCanvas: GaussianSplatRenderer._canvas,\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"ed7bed52-ef9a-4054-a90b-6a9373bdea9c","timestamp":"2026-06-16T06:04:23.290Z","toolUseResult":{"stdout":"=== constructor ===\nconstructor(_container, zipData) {\n        this.zipUrls = {\n            urls: new Map()\n        };\n        this.useFlame = false;\n        this.lastTime = 0;\n        this.startTime = 0;\n        this.expressionData = {};\n        this.chatState = TYVoiceChatState.Idle;\n        Object.assign(this.zipUrls, {\n            zip: zipData\n        });\n        const { width, height } = _container.getBoundingClientRect();\n        GaussianSplatRenderer._canvas.style.visibility = 'visible';\n        GaussianSplatRenderer._canvas.width = width;\n        GaussianSplatRenderer._canvas.height = height;\n        _con\n\n=== options block ===\n         let backgroundColor = 0xffffff;\n                if (charactorConfig.backgroundColor) {\n                    backgroundColor = parseInt(charactorConfig.backgroundColor, 16);\n                }\n                if (options && options.backgroundColor && renderer.isHexColorStrict(options.backgroundColor)) {\n                    backgroundColor = parseInt(options.backgroundColor, 16);\n                }\n                let alpha = 0;\n                if (options && options.alpha) {\n                    alpha = Math.max(0, Math.min(1, options.alpha));\n                }\n                renderer.getChatState = options === null || options === void 0 ? void 0 : options.getChatState;\n                renderer.getExpressionData = options === null || options === void 0 ? void 0 : options.getExpressionData;\n                if (charactorConfig.useFlame) {\n                    renderer.useFlame = (charactorConfig.useFlame == \"false\") ? false : true;\n                }\n                console.log(cameraPos, backgroundColor, alpha);\n                renderer.viewer = new Viewer({\n                    rootElement: container,\n                    threejsCanvas: GaussianSplatRenderer._canvas,","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"d799c465-7f88-4cde-b158-baaa4ec1032e","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"ed7bed52-ef9a-4054-a90b-6a9373bdea9c","isSidechain":false,"attachment":{"type":"task_reminder","content":[],"itemCount":0},"type":"attachment","uuid":"5c2ad16b-163a-4a63-8269-faeff68ac805","timestamp":"2026-06-16T06:04:23.293Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"je ne vois plus la liste des agents à gauche ( claude, hermes) etil n'y a pas d'avatar qui s'affichent","leafUuid":"5c2ad16b-163a-4a63-8269-faeff68ac805","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"permission-mode","permissionMode":"bypassPermissions","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"parentUuid":"5c2ad16b-163a-4a63-8269-faeff68ac805","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NvGJhMjVzWv9CBY4CMQEaA","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":1898,"cache_read_input_tokens":109270,"output_tokens":1695,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1898,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":1695,"cache_read_input_tokens":109270,"cache_creation_input_tokens":1898,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1898},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S7o8hufB6TM8PZHeFu","type":"assistant","uuid":"7bcfd741-aa73-44dd-a823-8e718408814f","timestamp":"2026-06-16T06:04:30.933Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"7bcfd741-aa73-44dd-a823-8e718408814f","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NvGJhMjVzWv9CBY4CMQEaA","type":"message","role":"assistant","content":[{"type":"text","text":"Parfait. Le renderer gère son propre canvas dans un **div container**, et supporte `backgroundColor` + `alpha`. Je réécris `avatar.js` complètement pour utiliser le vrai renderer Gaussian Splat."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":1898,"cache_read_input_tokens":109270,"output_tokens":1695,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1898,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":1695,"cache_read_input_tokens":109270,"cache_creation_input_tokens":1898,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1898},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S7o8hufB6TM8PZHeFu","type":"assistant","uuid":"d06a6aef-5c4e-42fb-97ab-d0f7af8131c0","timestamp":"2026-06-16T06:04:32.346Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"d06a6aef-5c4e-42fb-97ab-d0f7af8131c0","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01NvGJhMjVzWv9CBY4CMQEaA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017HkXvYQ2djEt9AMor9MzGQ","name":"Write","input":{"file_path":"/mnt/DATA/WORK/agent-gui/static/avatar.js","content":"import { GaussianSplatRenderer } from '/lib_lam_renderer.js';\n\nconst AVATAR_ZIP = '/avatar/barbara.zip';\nconst FPS = 30;\n\nlet rendererInstance = null;\nlet activeBsData = null;\nlet playStartTime = 0;\nlet audioCtx = null, audioBuffer = null, audioSource = null;\n\nfunction setStatus(text) {\n  const el = document.getElementById('avatarStatus');\n  if (el) el.textContent = text;\n}\n\n// Called by the renderer every frame. Returns {blendshapeName: weight} or null (resting pose).\nfunction getExpressionData() {\n  if (!activeBsData) return null;\n  const total = activeBsData.frames.length;\n  const elapsed = performance.now() / 1000 - playStartTime;\n  const frameIdx = Math.floor(elapsed * FPS);\n  if (frameIdx >= total) {\n    activeBsData = null;\n    setStatus('Prêt');\n    return null;\n  }\n  const weights = activeBsData.frames[frameIdx].weights;\n  const out = {};\n  activeBsData.names.forEach((name, i) => {\n    out[name] = weights[i];\n  });\n  return out;\n}\n\nasync function init() {\n  const container = document.getElementById('avatarRoot');\n  if (!container) return;\n  try {\n    setStatus('Chargement avatar...');\n    rendererInstance = await GaussianSplatRenderer.getInstance(container, AVATAR_ZIP, {\n      getExpressionData,\n      backgroundColor: '0x05080f',\n      loadProgress: (p) => setStatus(`Chargement ${Math.round(p * 100)}%`),\n      downloadProgress: (p) => setStatus(`Téléchargement ${Math.round(p * 100)}%`),\n    });\n    setStatus('Prêt');\n  } catch (err) {\n    console.error('Avatar init failed:', err);\n    setStatus('Erreur chargement avatar');\n  }\n}\n\nasync function loadAudio(url) {\n  if (!audioCtx) audioCtx = new AudioContext();\n  const res = await fetch(url);\n  const buf = await res.arrayBuffer();\n  audioBuffer = await audioCtx.decodeAudioData(buf);\n}\n\nfunction playAudio() {\n  if (!audioCtx || !audioBuffer) return;\n  if (audioSource) { try { audioSource.stop(); } catch (_) {} }\n  audioSource = audioCtx.createBufferSource();\n  audioSource.buffer = audioBuffer;\n  audioSource.connect(audioCtx.destination);\n  audioSource.start(0);\n}\n\n// Start blendshape playback (synced with audio start)\nfunction startAnimation(bsData) {\n  activeBsData = bsData;\n  playStartTime = performance.now() / 1000;\n  setStatus('Parle...');\n}\n\nasync function speakWithAnimation(sessionId) {\n  setStatus('Chargement audio...');\n  try {\n    await loadAudio(`/api/avatar/audio?session_id=${encodeURIComponent(sessionId)}`);\n    playAudio();\n  } catch (e) {\n    console.warn('Audio load failed:', e);\n  }\n}\n\nwindow.avatarController = { init, startAnimation, speakWithAnimation, setStatus };\ninit();\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":1898,"cache_read_input_tokens":109270,"output_tokens":1695,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1898,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":1695,"cache_read_input_tokens":109270,"cache_creation_input_tokens":1898,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1898},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6S7o8hufB6TM8PZHeFu","type":"assistant","uuid":"563a5450-55a8-46a0-baa1-936ff26c477b","timestamp":"2026-06-16T06:04:42.332Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"563a5450-55a8-46a0-baa1-936ff26c477b","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017HkXvYQ2djEt9AMor9MzGQ","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/static/avatar.js has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"a12ea7a0-a011-4b5f-b0dd-f359dd33b473","timestamp":"2026-06-16T06:04:42.357Z","toolUseResult":{"type":"update","filePath":"/mnt/DATA/WORK/agent-gui/static/avatar.js","content":"import { GaussianSplatRenderer } from '/lib_lam_renderer.js';\n\nconst AVATAR_ZIP = '/avatar/barbara.zip';\nconst FPS = 30;\n\nlet rendererInstance = null;\nlet activeBsData = null;\nlet playStartTime = 0;\nlet audioCtx = null, audioBuffer = null, audioSource = null;\n\nfunction setStatus(text) {\n  const el = document.getElementById('avatarStatus');\n  if (el) el.textContent = text;\n}\n\n// Called by the renderer every frame. Returns {blendshapeName: weight} or null (resting pose).\nfunction getExpressionData() {\n  if (!activeBsData) return null;\n  const total = activeBsData.frames.length;\n  const elapsed = performance.now() / 1000 - playStartTime;\n  const frameIdx = Math.floor(elapsed * FPS);\n  if (frameIdx >= total) {\n    activeBsData = null;\n    setStatus('Prêt');\n    return null;\n  }\n  const weights = activeBsData.frames[frameIdx].weights;\n  const out = {};\n  activeBsData.names.forEach((name, i) => {\n    out[name] = weights[i];\n  });\n  return out;\n}\n\nasync function init() {\n  const container = document.getElementById('avatarRoot');\n  if (!container) return;\n  try {\n    setStatus('Chargement avatar...');\n    rendererInstance = await GaussianSplatRenderer.getInstance(container, AVATAR_ZIP, {\n      getExpressionData,\n      backgroundColor: '0x05080f',\n      loadProgress: (p) => setStatus(`Chargement ${Math.round(p * 100)}%`),\n      downloadProgress: (p) => setStatus(`Téléchargement ${Math.round(p * 100)}%`),\n    });\n    setStatus('Prêt');\n  } catch (err) {\n    console.error('Avatar init failed:', err);\n    setStatus('Erreur chargement avatar');\n  }\n}\n\nasync function loadAudio(url) {\n  if (!audioCtx) audioCtx = new AudioContext();\n  const res = await fetch(url);\n  const buf = await res.arrayBuffer();\n  audioBuffer = await audioCtx.decodeAudioData(buf);\n}\n\nfunction playAudio() {\n  if (!audioCtx || !audioBuffer) return;\n  if (audioSource) { try { audioSource.stop(); } catch (_) {} }\n  audioSource = audioCtx.createBufferSource();\n  audioSource.buffer = audioBuffer;\n  audioSource.connect(audioCtx.destination);\n  audioSource.start(0);\n}\n\n// Start blendshape playback (synced with audio start)\nfunction startAnimation(bsData) {\n  activeBsData = bsData;\n  playStartTime = performance.now() / 1000;\n  setStatus('Parle...');\n}\n\nasync function speakWithAnimation(sessionId) {\n  setStatus('Chargement audio...');\n  try {\n    await loadAudio(`/api/avatar/audio?session_id=${encodeURIComponent(sessionId)}`);\n    playAudio();\n  } catch (e) {\n    console.warn('Audio load failed:', e);\n  }\n}\n\nwindow.avatarController = { init, startAnimation, speakWithAnimation, setStatus };\ninit();\n","structuredPatch":[{"oldStart":1,"oldLines":100,"newStart":1,"newLines":55,"lines":["-import * as THREE from 'three';","-import { GLTFLoader } from 'three/addons/loaders/GLTFLoader.js';","+import { GaussianSplatRenderer } from '/lib_lam_renderer.js';"," ","-const AVATAR_GLB = '/avatar/barbara/skin.glb';","+const AVATAR_ZIP = '/avatar/barbara.zip';"," const FPS = 30;"," ","-let renderer, scene, camera, avatarMesh, morphDict, morphNames;","-let frames = [], frameIndex = 0, animTimer = null;","+let rendererInstance = null;","+let activeBsData = null;","+let playStartTime = 0;"," let audioCtx = null, audioBuffer = null, audioSource = null;"," ","-function init() {","-  const canvas = document.getElementById('avatarCanvas');","-  if (!canvas) return;","-","-  renderer = new THREE.WebGLRenderer({ canvas, antialias: true, alpha: true });","-  renderer.setPixelRatio(window.devicePixelRatio);","-  renderer.setSize(canvas.clientWidth, canvas.clientHeight, false);","-  renderer.outputColorSpace = THREE.SRGBColorSpace;","-","-  scene = new THREE.Scene();","-","-  camera = new THREE.PerspectiveCamera(35, canvas.clientWidth / canvas.clientHeight, 0.01, 100);","-  camera.position.set(0, 0, 3);","-","-  const ambient = new THREE.AmbientLight(0xffffff, 1.2);","-  scene.add(ambient);","-  const dir = new THREE.DirectionalLight(0xffffff, 1.5);","-  dir.position.set(1, 2, 3);","-  scene.add(dir);","-","-  loadAvatar();","-  renderLoop();","+function setStatus(text) {","+  const el = document.getElementById('avatarStatus');","+  if (el) el.textContent = text;"," }"," ","-function loadAvatar() {","-  const loader = new GLTFLoader();","-  loader.load(AVATAR_GLB, (gltf) => {","-    const root = gltf.scene;","-    scene.add(root);","-","-    root.traverse((node) => {","-      if (node.isMesh && node.morphTargetDictionary && Object.keys(node.morphTargetDictionary).length > 0) {","-        avatarMesh = node;","-        morphDict = node.morphTargetDictionary;","-      }","-    });","-","-    fitCamera(root);","+// Called by the renderer every frame. Returns {blendshapeName: weight} or null (resting pose).","+function getExpressionData() {","+  if (!activeBsData) return null;","+  const total = activeBsData.frames.length;","+  const elapsed = performance.now() / 1000 - playStartTime;","+  const frameIdx = Math.floor(elapsed * FPS);","+  if (frameIdx >= total) {","+    activeBsData = null;","     setStatus('Prêt');","-  }, undefined, (err) => {","-    setStatus('Erreur chargement avatar');","-    console.error(err);","+    return null;","+  }","+  const weights = activeBsData.frames[frameIdx].weights;","+  const out = {};","+  activeBsData.names.forEach((name, i) => {","+    out[name] = weights[i];","   });","+  return out;"," }"," ","-function fitCamera(object) {","-  const box = new THREE.Box3().setFromObject(object);","-  const center = box.getCenter(new THREE.Vector3());","-  const size = box.getSize(new THREE.Vector3());","-  const maxDim = Math.max(size.x, size.y);","-  const fovRad = camera.fov * Math.PI / 180;","-  const dist = (maxDim / 2) / Math.tan(fovRad / 2);","-","-  // Focus on upper half (face/head)","-  camera.position.set(center.x, center.y + size.y * 0.15, center.z + dist * 1.1);","-  camera.lookAt(center.x, center.y + size.y * 0.15, center.z);","-  camera.near = dist / 100;","-  camera.far = dist * 100;","-  camera.updateProjectionMatrix();","-}","-","-function applyFrame(frame) {","-  if (!avatarMesh || !morphDict || !frame) return;","-  const weights = frame.weights;","-  for (let i = 0; i < morphNames.length; i++) {","-    const name = morphNames[i];","-    if (name in morphDict) {","-      avatarMesh.morphTargetInfluences[morphDict[name]] = weights[i] ?? 0;","-    }","+async function init() {","+  const container = document.getElementById('avatarRoot');","+  if (!container) return;","+  try {","+    setStatus('Chargement avatar...');","+    rendererInstance = await GaussianSplatRenderer.getInstance(container, AVATAR_ZIP, {","+      getExpressionData,","+      backgroundColor: '0x05080f',","+      loadProgress: (p) => setStatus(`Chargement ${Math.round(p * 100)}%`),","+      downloadProgress: (p) => setStatus(`Téléchargement ${Math.round(p * 100)}%`),","+    });","+    setStatus('Prêt');","+  } catch (err) {","+    console.error('Avatar init failed:', err);","+    setStatus('Erreur chargement avatar');","   }"," }"," ","-function resetMorphs() {","-  if (!avatarMesh) return;","-  avatarMesh.morphTargetInfluences.fill(0);","-}","-","-function renderLoop() {","-  requestAnimationFrame(renderLoop);","-  renderer.render(scene, camera);","-}","-","-function setStatus(text) {","-  const el = document.getElementById('avatarStatus');","-  if (el) el.textContent = text;","-}","-"," async function loadAudio(url) {","   if (!audioCtx) audioCtx = new AudioContext();","   const res = await fetch(url);"]},{"oldStart":111,"oldLines":27,"newStart":66,"newLines":15,"lines":["   audioSource.start(0);"," }"," ","+// Start blendshape playback (synced with audio start)"," function startAnimation(bsData) {","-  if (animTimer) clearInterval(animTimer);","-  morphNames = bsData.metadata.blendshape_names;","-  frames = bsData.frames;","-  frameIndex = 0;","-  setStatus('Animation...');","-","-  animTimer = setInterval(() => {","-    if (frameIndex >= frames.length) {","-      clearInterval(animTimer);","-      animTimer = null;","-      resetMorphs();","-      setStatus('Prêt');","-      return;","-    }","-    applyFrame(frames[frameIndex++]);","-  }, 1000 / FPS);","+  activeBsData = bsData;","+  playStartTime = performance.now() / 1000;","+  setStatus('Parle...');"," }"," "," async function speakWithAnimation(sessionId) {","-  setStatus('Génération audio...');","+  setStatus('Chargement audio...');","   try {","     await loadAudio(`/api/avatar/audio?session_id=${encodeURIComponent(sessionId)}`);","     playAudio();"]},{"oldStart":140,"oldLines":6,"newStart":83,"newLines":5,"lines":["   }"," }"," ","-// Exported controller — auto-init since modules execute after DOM is parsed"," window.avatarController = { init, startAnimation, speakWithAnimation, setStatus };"," init();"]}],"originalFile":"import * as THREE from 'three';\nimport { GLTFLoader } from 'three/addons/loaders/GLTFLoader.js';\n\nconst AVATAR_GLB = '/avatar/barbara/skin.glb';\nconst FPS = 30;\n\nlet renderer, scene, camera, avatarMesh, morphDict, morphNames;\nlet frames = [], frameIndex = 0, animTimer = null;\nlet audioCtx = null, audioBuffer = null, audioSource = null;\n\nfunction init() {\n  const canvas = document.getElementById('avatarCanvas');\n  if (!canvas) return;\n\n  renderer = new THREE.WebGLRenderer({ canvas, antialias: true, alpha: true });\n  renderer.setPixelRatio(window.devicePixelRatio);\n  renderer.setSize(canvas.clientWidth, canvas.clientHeight, false);\n  renderer.outputColorSpace = THREE.SRGBColorSpace;\n\n  scene = new THREE.Scene();\n\n  camera = new THREE.PerspectiveCamera(35, canvas.clientWidth / canvas.clientHeight, 0.01, 100);\n  camera.position.set(0, 0, 3);\n\n  const ambient = new THREE.AmbientLight(0xffffff, 1.2);\n  scene.add(ambient);\n  const dir = new THREE.DirectionalLight(0xffffff, 1.5);\n  dir.position.set(1, 2, 3);\n  scene.add(dir);\n\n  loadAvatar();\n  renderLoop();\n}\n\nfunction loadAvatar() {\n  const loader = new GLTFLoader();\n  loader.load(AVATAR_GLB, (gltf) => {\n    const root = gltf.scene;\n    scene.add(root);\n\n    root.traverse((node) => {\n      if (node.isMesh && node.morphTargetDictionary && Object.keys(node.morphTargetDictionary).length > 0) {\n        avatarMesh = node;\n        morphDict = node.morphTargetDictionary;\n      }\n    });\n\n    fitCamera(root);\n    setStatus('Prêt');\n  }, undefined, (err) => {\n    setStatus('Erreur chargement avatar');\n    console.error(err);\n  });\n}\n\nfunction fitCamera(object) {\n  const box = new THREE.Box3().setFromObject(object);\n  const center = box.getCenter(new THREE.Vector3());\n  const size = box.getSize(new THREE.Vector3());\n  const maxDim = Math.max(size.x, size.y);\n  const fovRad = camera.fov * Math.PI / 180;\n  const dist = (maxDim / 2) / Math.tan(fovRad / 2);\n\n  // Focus on upper half (face/head)\n  camera.position.set(center.x, center.y + size.y * 0.15, center.z + dist * 1.1);\n  camera.lookAt(center.x, center.y + size.y * 0.15, center.z);\n  camera.near = dist / 100;\n  camera.far = dist * 100;\n  camera.updateProjectionMatrix();\n}\n\nfunction applyFrame(frame) {\n  if (!avatarMesh || !morphDict || !frame) return;\n  const weights = frame.weights;\n  for (let i = 0; i < morphNames.length; i++) {\n    const name = morphNames[i];\n    if (name in morphDict) {\n      avatarMesh.morphTargetInfluences[morphDict[name]] = weights[i] ?? 0;\n    }\n  }\n}\n\nfunction resetMorphs() {\n  if (!avatarMesh) return;\n  avatarMesh.morphTargetInfluences.fill(0);\n}\n\nfunction renderLoop() {\n  requestAnimationFrame(renderLoop);\n  renderer.render(scene, camera);\n}\n\nfunction setStatus(text) {\n  const el = document.getElementById('avatarStatus');\n  if (el) el.textContent = text;\n}\n\nasync function loadAudio(url) {\n  if (!audioCtx) audioCtx = new AudioContext();\n  const res = await fetch(url);\n  const buf = await res.arrayBuffer();\n  audioBuffer = await audioCtx.decodeAudioData(buf);\n}\n\nfunction playAudio() {\n  if (!audioCtx || !audioBuffer) return;\n  if (audioSource) { try { audioSource.stop(); } catch (_) {} }\n  audioSource = audioCtx.createBufferSource();\n  audioSource.buffer = audioBuffer;\n  audioSource.connect(audioCtx.destination);\n  audioSource.start(0);\n}\n\nfunction startAnimation(bsData) {\n  if (animTimer) clearInterval(animTimer);\n  morphNames = bsData.metadata.blendshape_names;\n  frames = bsData.frames;\n  frameIndex = 0;\n  setStatus('Animation...');\n\n  animTimer = setInterval(() => {\n    if (frameIndex >= frames.length) {\n      clearInterval(animTimer);\n      animTimer = null;\n      resetMorphs();\n      setStatus('Prêt');\n      return;\n    }\n    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{"parentUuid":"1e593bab-591d-46a4-90b0-fbdafbdb0aac","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015VM89dYvEAfSTHCcZEzan4","type":"tool_result","content":"from 'https://my-cnd-server.com/assets/models/model.bin'\nfrom 'https://my-cnd-server.com/assets/textures/texture.png'\nfrom \"Physically Based Shading on Mobile\"\nfrom \"srgb-linear\"\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"0db75a72-3d46-4f74-ae42-bb02463d608e","timestamp":"2026-06-16T06:05:22.068Z","toolUseResult":{"stdout":"from 'https://my-cnd-server.com/assets/models/model.bin'\nfrom 'https://my-cnd-server.com/assets/textures/texture.png'\nfrom \"Physically Based Shading on Mobile\"\nfrom \"srgb-linear\"","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"1e593bab-591d-46a4-90b0-fbdafbdb0aac","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"1f982f91-3756-483c-8c35-4b1dd450e27a","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01X1PqRPrHQGY5MPnBPrqvhh","type":"message","role":"assistant","content":[{"type":"text","text":"Tout est servi correctement. Les deux problèmes sont corrigés :\n\n**1. Liste d'agents disparue** → c'était une erreur de syntaxe : mon edit précédent avait injecté des guillemets courbes (`'…'`) comme délimiteurs de strings dans `app.js`, ce qui cassait tout le parsing JS et empêchait `init()` de tourner. Corrigé — les agents (echo, hermes, claude-code) reviennent.\n\n**2. Avatar invisible** → la vraie cause : `skin.glb` est une géométrie nue (pas de normales/textures/couleur). Le rendu photoréaliste de LAM se fait en **Gaussian Splatting**, pas en mesh standard. J'ai donc basculé sur le vrai renderer officiel `gaussian-splat-renderer-for-lam` (récupéré depuis npm, 4MB, three.js bundlé), et créé `barbara.zip` avec la structure attendue (dossier `barbara/` + les 5 fichiers).\n\n**Architecture finale :**\n- `GaussianSplatRenderer.getInstance(div, '/avatar/barbara.zip', {getExpressionData})` charge l'avatar 3DGS\n- `getExpressionData()` est appelé à chaque frame par le renderer et retourne `{blendshapeName: weight}` calculé depuis le bsData (format identique à la démo Gradio qui marchait)\n- Pipeline complet : message → réponse → TTS (edge-tts) → A2E (subprocess lam_a2e) → polling → audio + animation synchronisés\n\n**Fais un hard-refresh (Ctrl+Shift+R)** pour vider le cache. Au premier chargement il télécharge ~8MB (le bundle renderer + le zip avatar), donc le status affichera la progression. Ensuite Barbara devrait apparaître, et parler quand l'agent répond.\n\nDis-moi ce que tu vois dans la console navigateur si ça coince."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":448,"cache_read_input_tokens":116260,"output_tokens":2537,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":448,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":2537,"cache_read_input_tokens":116260,"cache_creation_input_tokens":448,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":448},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SDjbJYtFSQozjejjNY","type":"assistant","uuid":"5472adf1-b57a-4b2c-b80a-7ba551f9b8f1","timestamp":"2026-06-16T06:06:01.323Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"5472adf1-b57a-4b2c-b80a-7ba551f9b8f1","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01X1PqRPrHQGY5MPnBPrqvhh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FBUxo5w5BgNY241HPfweqr","name":"Edit","input":{"replace_all":false,"file_path":"/home/wildlama/.claude/projects/-home-wildlama/memory/project_agent_gui_lam.md","old_string":"**Alternative rapide sans Blender :**\nUtiliser les avatars sample fournis (`barbara`, `james`) pour tester le pipeline complet avant d'avoir son propre avatar.","new_string":"**Alternative rapide sans Blender :**\nUtiliser les avatars sample fournis (`barbara`, `james`) pour tester le pipeline complet avant d'avoir son propre avatar.\n\n---\n\n### Intégration RÉALISÉE dans agent-gui — 2026-06-16\n\nPipeline TTS→A2E→avatar 3DGS intégré et fonctionnel côté serveur. Avatar **barbara** (sample).\n\n**Blender :** pas réinstallé — version 5.1.2 déjà extraite dans `~/Downloads/blender-5.1.2-linux-x64/blender` (fonctionne). Étape export avatar perso reste à faire ; on a utilisé le sample barbara pour l'instant.\n\n**Nouveaux fichiers agent-gui :**\n- `agent_gui/tts.py` — edge-tts (installé dans miniconda base) → mp3 → ffmpeg → WAV 16kHz mono\n- `agent_gui/avatar_lam.py` — subprocess `conda run -n lam_a2e python run_inference.py` → bsData JSON\n- `LAM_Audio2Expression/run_inference.py` — CLI wrapper (audio.wav → bsData.json), créé pour être appelé en subprocess\n- `static/avatar.js` — module ES, utilise le renderer Gaussian Splat\n- `static/lib_lam_renderer.js` — bundle npm `gaussian-splat-renderer-for-lam@0.0.9-alpha.2` (4MB, three.js inclus), téléchargé depuis jsdelivr\n- `static/avatar/barbara.zip` — zip avatar (dossier `barbara/` + animation.glb, skin.glb, offset.ply, vertex_order.json, bsData.json)\n\n**Modifs app.py :** thread background lancé après chaque réponse agent (TTS+A2E), endpoints `GET /api/avatar/anim?session_id=` (polling: pending/ready/error+bsData) et `GET /api/avatar/audio?session_id=` (WAV). Store `_anim_store` en mémoire protégé par lock.\n\n**API renderer LAM (clé) :** `GaussianSplatRenderer.getInstance(containerDiv, '/path/avatar.zip', {getExpressionData, backgroundColor:'0x05080f', loadProgress, downloadProgress})`. Le zip DOIT contenir un dossier (nom=characterName) avec skin.glb/animation.glb/offset.ply/vertex_order.json. L'URL doit matcher `/([^/]+?)\\.zip/`. `getExpressionData()` appelé chaque frame, retourne `{blendshapeName: weight}` ou null (pose repos). Format identique à `getArkitFaceFrame` du composant Gradio : `out[names[i]] = frames[frameIdx].weights[i]`.\n\n**ATTENTION pièges rencontrés :**\n- skin.glb seul = géométrie nue (pas normales/texture) → invisible en mesh Three.js standard. Le photoréalisme vient du Gaussian Splatting (offset.ply). NE PAS tenter de rendre via GLTFLoader simple.\n- Éviter les guillemets courbes (' ') dans les edits de fichiers JS — ils cassent le parsing. Toujours des guillemets droits.\n\n**Lancer :** `cd /mnt/DATA/WORK/agent-gui && $HOME/miniconda3/bin/python -m agent_gui.app` → http://127.0.0.1:8765\n\n**Reste à faire :** confirmer rendu visuel navigateur ; exporter avatar perso via Blender + app_lam.py ; choix voix TTS (actuellement en-US-AriaNeural anglais — passer en français fr-FR-* selon besoin)."},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":448,"cache_read_input_tokens":116260,"output_tokens":2537,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":448,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":2537,"cache_read_input_tokens":116260,"cache_creation_input_tokens":448,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":448},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SDjbJYtFSQozjejjNY","type":"assistant","uuid":"b6bc8eb3-2d7a-4035-91aa-958989955030","timestamp":"2026-06-16T06:06:19.113Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"b6bc8eb3-2d7a-4035-91aa-958989955030","isSidechain":false,"promptId":"9684aa13-e613-4628-97d0-704e4879d9ed","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FBUxo5w5BgNY241HPfweqr","type":"tool_result","content":"The file /home/wildlama/.claude/projects/-home-wildlama/memory/project_agent_gui_lam.md has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"ba436031-2247-4015-a82c-e97b6dda15d5","timestamp":"2026-06-16T06:06:19.147Z","toolUseResult":{"filePath":"/home/wildlama/.claude/projects/-home-wildlama/memory/project_agent_gui_lam.md","oldString":"**Alternative rapide sans Blender :**\nUtiliser les avatars sample fournis (`barbara`, `james`) pour tester le pipeline complet avant d'avoir son propre avatar.","newString":"**Alternative rapide sans Blender :**\nUtiliser les avatars sample fournis (`barbara`, `james`) pour tester le pipeline complet avant d'avoir son propre avatar.\n\n---\n\n### Intégration RÉALISÉE dans agent-gui — 2026-06-16\n\nPipeline TTS→A2E→avatar 3DGS intégré et fonctionnel côté serveur. Avatar **barbara** (sample).\n\n**Blender :** pas réinstallé — version 5.1.2 déjà extraite dans `~/Downloads/blender-5.1.2-linux-x64/blender` (fonctionne). Étape export avatar perso reste à faire ; on a utilisé le sample barbara pour l'instant.\n\n**Nouveaux fichiers agent-gui :**\n- `agent_gui/tts.py` — edge-tts (installé dans miniconda base) → mp3 → ffmpeg → WAV 16kHz mono\n- `agent_gui/avatar_lam.py` — subprocess `conda run -n lam_a2e python run_inference.py` → bsData JSON\n- `LAM_Audio2Expression/run_inference.py` — CLI wrapper (audio.wav → bsData.json), créé pour être appelé en subprocess\n- `static/avatar.js` — module ES, utilise le renderer Gaussian Splat\n- `static/lib_lam_renderer.js` — bundle npm `gaussian-splat-renderer-for-lam@0.0.9-alpha.2` (4MB, three.js inclus), téléchargé depuis jsdelivr\n- `static/avatar/barbara.zip` — zip avatar (dossier `barbara/` + animation.glb, skin.glb, offset.ply, vertex_order.json, bsData.json)\n\n**Modifs app.py :** thread background lancé après chaque réponse agent (TTS+A2E), endpoints `GET /api/avatar/anim?session_id=` (polling: pending/ready/error+bsData) et `GET /api/avatar/audio?session_id=` (WAV). Store `_anim_store` en mémoire protégé par lock.\n\n**API renderer LAM (clé) :** `GaussianSplatRenderer.getInstance(containerDiv, '/path/avatar.zip', {getExpressionData, backgroundColor:'0x05080f', loadProgress, downloadProgress})`. Le zip DOIT contenir un dossier (nom=characterName) avec skin.glb/animation.glb/offset.ply/vertex_order.json. L'URL doit matcher `/([^/]+?)\\.zip/`. `getExpressionData()` appelé chaque frame, retourne `{blendshapeName: weight}` ou null (pose repos). Format identique à `getArkitFaceFrame` du composant Gradio : `out[names[i]] = frames[frameIdx].weights[i]`.\n\n**ATTENTION pièges rencontrés :**\n- skin.glb seul = géométrie nue (pas normales/texture) → invisible en mesh Three.js standard. Le photoréalisme vient du Gaussian Splatting (offset.ply). NE PAS tenter de rendre via GLTFLoader simple.\n- Éviter les guillemets courbes (' ') dans les edits de fichiers JS — ils cassent le parsing. Toujours des guillemets droits.\n\n**Lancer :** `cd /mnt/DATA/WORK/agent-gui && $HOME/miniconda3/bin/python -m agent_gui.app` → http://127.0.0.1:8765\n\n**Reste à faire :** confirmer rendu visuel navigateur ; exporter avatar perso via Blender + app_lam.py ; choix voix TTS (actuellement en-US-AriaNeural anglais — passer en français fr-FR-* selon besoin).","originalFile":"---\nname: project-agent-gui-lam\ndescription: \"GUI pour agents IA avec avatars LAM 3D parlants en temps réel — état d'avancement complet\"\nmetadata: \n  node_type: memory\n  type: project\n  originSessionId: 7de9b929-9050-430b-9b36-f74849c1c0a7\n---\n\n## Projet principal\n\n`/mnt/DATA/WORK/agent-gui` — web app locale (Python stdlib + vanilla JS) pour piloter des agents LLM avec interface graphique. V1 fonctionnelle avec avatar ASCII, canvas pixel-art animé, chat, sessions JSONL, adaptateurs CLI.\n\n**Why:** Créer une surcouche graphique universelle pour agents IA (Hermes, Claude Code, etc.) avec avatars visuels modélisables.\n\n**How to apply:** Quand on reprend le travail, partir de cet état et continuer vers l'intégration LAM.\n\n---\n\n## Intégration LAM — état au 2026-06-15\n\n### Objectif\nAjouter des avatars 3D photoréalistes parlants en temps réel dans agent-gui. Pipeline :\n```\n[Agent répond] → [TTS → audio 16kHz mono]\n    → [Python: Audio2Expression → 52 blendshapes ARKit @ 30fps]\n        → [WebSocket JSON] → [Browser: WebGL Gaussian Splatting renderer]\n```\n\n### Ce qui est installé et fonctionnel ✅\n\n**Conda envs sur cette machine :**\n- `lam` (Python 3.11) — pour LAM core (génération avatar depuis photo)\n  - PyTorch 2.11+cu128, pytorch3d 0.7.9, diff-gaussian-rasterization, nvdiffrast, simple-knn\n  - gcc 13 installé dans l'env (nécessaire pour compiler pytorch3d avec CUDA 12.8)\n- `lam_a2e` (Python 3.10) — pour Audio2Expression (live talking)\n  - PyTorch 2.11+cu128, transformers, librosa, gradio-gaussian-render\n\n**Repos clonés :**\n- `/mnt/DATA/WORK/LAM` — modèle LAM core (génère avatar 3D depuis photo)\n- `/mnt/DATA/WORK/LAM_Audio2Expression` — Audio2Expression (audio → blendshapes)\n  - Poids téléchargés et extraits : `pretrained_models/lam_audio2exp_flow/`\n  - **Testé et fonctionnel** : 52 canaux ARKit, 30 FPS, 4ms/seconde d'audio après warm-up\n\n**Résultat test Audio2Expression :**\n- Input : `assets/sample_audio/BarackObama_english.wav`\n- Output : `bsData.json` — 240 frames à 30fps, 52 blendshapes ARKit par frame\n- Format : `{ \"names\": [\"browDownLeft\", \"jawOpen\", ...], \"frames\": [[...], ...], \"metadata\": {\"fps\": 30} }`\n\n**Hardware :**\n- GPU : RTX 5090, 32 Go VRAM\n- Drivers NVIDIA : 595.71, CUDA 13.2 (max supporté)\n- CUDA Toolkit 12.8 installé via conda dans les envs\n\n---\n\n### Ce qui reste à faire\n\n**Étape 1 — Exporter l'avatar 3D (bloquant : nécessite Blender 4.0+)**\n- Vérifier si Blender est installé : `blender --version`\n- Si oui : lancer `app_lam.py` dans l'env `lam`, charger une photo portrait, exporter le ZIP avatar\n- Le ZIP contient : `animation.glb`, `skin.glb`, `offset.ply`, `bsData.json`, `vertex_order.json`\n- Des avatars sample sont déjà dispo dans `LAM_Audio2Expression/assets/sample_lam/` (barbara, james, status, vfhq_case1) — peut être utilisé pour tester sans Blender\n\n**Étape 2 — Ajouter TTS dans agent-gui**\n- Options légères : `pyttsx3` (offline, stdlib-like), `edge-tts` (Microsoft gratuit), `kokoro` (qualité)\n- Output : fichier WAV 16kHz mono → passé à Audio2Expression\n\n**Étape 3 — Ajouter WebSocket dans agent_gui/app.py**\n- Ajouter endpoint WebSocket `/ws/avatar`\n- Au moment où l'agent répond : TTS → Audio2Expression → stream blendshapes JSON à 30fps vers browser\n\n**Étape 4 — Remplacer canvas pixel-art par renderer WebGL**\n- Package npm : `gaussian-splat-renderer-for-lam` (repo : `aigc3d/LAM_WebRender`)\n- API : charge le ZIP avatar une fois, polling `getExpressionData()` à chaque frame rAF\n- Ou utiliser les avatars sample GLB directement si le format est compatible\n\n**Alternative rapide sans Blender :**\nUtiliser les avatars sample fournis (`barbara`, `james`) pour tester le pipeline complet avant d'avoir son propre avatar.\n\n---\n\n### Architecture finale visée\n\n```\nagent_gui/\n  app.py          ← ajouter WebSocket + appel subprocess Audio2Expression\n  avatar_lam.py   ← nouveau module : lance lam_a2e env, streame blendshapes\n  tts.py          ← nouveau module : TTS → WAV 16kHz\nstatic/\n  app.js          ← remplacer canvas pixel-art par WebGL renderer LAM\n  avatar.zip      ← avatar exporté depuis photo (ou sample barbara/james)\n```\n\n### Commandes utiles pour reprendre\n\n```bash\n# Tester Audio2Expression\ncd /mnt/DATA/WORK/LAM_Audio2Expression\n$HOME/miniconda3/bin/conda run -n lam_a2e python inference_streaming_audio.py\n\n# Lancer app Gradio LAM (pour exporter avatar depuis photo, nécessite Blender)\ncd /mnt/DATA/WORK/LAM\n$HOME/miniconda3/bin/conda run -n lam python app_lam.py\n\n# Lancer app Gradio Audio2Expression (demo complète avec WebGL)\ncd /mnt/DATA/WORK/LAM_Audio2Expression\n$HOME/miniconda3/bin/conda run -n lam_a2e python app_lam_audio2exp.py\n```\n","structuredPatch":[{"oldStart":80,"oldLines":6,"newStart":80,"newLines":34,"lines":[" "," ---"," ","+### Intégration RÉALISÉE dans agent-gui — 2026-06-16","+","+Pipeline TTS→A2E→avatar 3DGS intégré et fonctionnel côté serveur. Avatar **barbara** (sample).","+","+**Blender :** pas réinstallé — version 5.1.2 déjà extraite dans `~/Downloads/blender-5.1.2-linux-x64/blender` (fonctionne). Étape export avatar perso reste à faire ; on a utilisé le sample barbara pour l'instant.","+","+**Nouveaux fichiers agent-gui :**","+- `agent_gui/tts.py` — edge-tts (installé dans miniconda base) → mp3 → ffmpeg → WAV 16kHz mono","+- `agent_gui/avatar_lam.py` — subprocess `conda run -n lam_a2e python run_inference.py` → bsData JSON","+- `LAM_Audio2Expression/run_inference.py` — CLI wrapper (audio.wav → bsData.json), créé pour être appelé en subprocess","+- `static/avatar.js` — module ES, utilise le renderer Gaussian Splat","+- `static/lib_lam_renderer.js` — bundle npm `gaussian-splat-renderer-for-lam@0.0.9-alpha.2` (4MB, three.js inclus), téléchargé depuis jsdelivr","+- `static/avatar/barbara.zip` — zip avatar (dossier `barbara/` + animation.glb, skin.glb, offset.ply, vertex_order.json, bsData.json)","+","+**Modifs app.py :** thread background lancé après chaque réponse agent (TTS+A2E), endpoints `GET /api/avatar/anim?session_id=` (polling: pending/ready/error+bsData) et `GET /api/avatar/audio?session_id=` (WAV). Store `_anim_store` en mémoire protégé par lock.","+","+**API renderer LAM (clé) :** `GaussianSplatRenderer.getInstance(containerDiv, '/path/avatar.zip', {getExpressionData, backgroundColor:'0x05080f', loadProgress, downloadProgress})`. Le zip DOIT contenir un dossier (nom=characterName) avec skin.glb/animation.glb/offset.ply/vertex_order.json. L'URL doit matcher `/([^/]+?)\\.zip/`. `getExpressionData()` appelé chaque frame, retourne `{blendshapeName: weight}` ou null (pose repos). Format identique à `getArkitFaceFrame` du composant Gradio : `out[names[i]] = frames[frameIdx].weights[i]`.","+","+**ATTENTION pièges rencontrés :**","+- skin.glb seul = géométrie nue (pas normales/texture) → invisible en mesh Three.js standard. Le photoréalisme vient du Gaussian Splatting (offset.ply). NE PAS tenter de rendre via GLTFLoader simple.","+- Éviter les guillemets courbes (' ') dans les edits de fichiers JS — ils cassent le parsing. Toujours des guillemets droits.","+","+**Lancer :** `cd /mnt/DATA/WORK/agent-gui && $HOME/miniconda3/bin/python -m agent_gui.app` → http://127.0.0.1:8765","+","+**Reste à faire :** confirmer rendu visuel navigateur ; exporter avatar perso via Blender + app_lam.py ; choix voix TTS (actuellement en-US-AriaNeural anglais — passer en français fr-FR-* selon besoin).","+","+---","+"," ### Architecture finale visée"," "," ```"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"b6bc8eb3-2d7a-4035-91aa-958989955030","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"9a3a5400-2b16-4104-bf87-df6b2856b955","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01PY7EeUUFnfrZZesVfUPkcc","type":"message","role":"assistant","content":[{"type":"text","text":"TTS doit détecter la langue automatiquement. Le texte parlé est la réponse de l'agent. J'ajoute une détection de langue avec mapping vers les voix edge-tts. Je regarde les voix françaises dispo et j'installe un détecteur léger."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":322,"cache_read_input_tokens":119339,"output_tokens":683,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":322,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":683,"cache_read_input_tokens":119339,"cache_creation_input_tokens":322,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":322},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SXuZLFMb76TjMB2BJE","type":"assistant","uuid":"d2e62f81-9487-4340-a58d-fcc3cce685a6","timestamp":"2026-06-16T06:09:57.993Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"b37cc75f-0e78-450c-a1db-20bad4dcdcfe","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01HEPtwKYjBKoyhdToV6JNAs","type":"message","role":"assistant","content":[{"type":"text","text":"`langdetect` fonctionne (fr/en détectés). Je modifie `tts.py` pour détecter la langue et mapper vers une voix appropriée, avec fallback robuste."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":274,"cache_read_input_tokens":120835,"output_tokens":974,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":274,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":974,"cache_read_input_tokens":120835,"cache_creation_input_tokens":274,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":274},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SZ3mih63YzoaDnHpwV","type":"assistant","uuid":"49eb0238-2a5b-4aef-b045-3192f6ce073d","timestamp":"2026-06-16T06:10:12.259Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"49eb0238-2a5b-4aef-b045-3192f6ce073d","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01HEPtwKYjBKoyhdToV6JNAs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015f7QEPZwe7aKsmn67zgiN6","name":"Write","input":{"file_path":"/mnt/DATA/WORK/agent-gui/agent_gui/tts.py","content":"from __future__ import annotations\n\nimport subprocess\nfrom pathlib import Path\n\n_EDGE_TTS = Path.home() / \"miniconda3/bin/edge-tts\"\n\n# Voix par langue détectée (code ISO 639-1 -> voix edge-tts)\n_VOICE_BY_LANG = {\n    \"fr\": \"fr-FR-DeniseNeural\",\n    \"en\": \"en-US-AriaNeural\",\n    \"es\": \"es-ES-ElviraNeural\",\n    \"de\": \"de-DE-KatjaNeural\",\n    \"it\": \"it-IT-ElsaNeural\",\n    \"pt\": \"pt-BR-FranciscaNeural\",\n    \"nl\": \"nl-NL-ColetteNeural\",\n    \"ja\": \"ja-JP-NanamiNeural\",\n    \"zh-cn\": \"zh-CN-XiaoxiaoNeural\",\n    \"ko\": \"ko-KR-SunHiNeural\",\n    \"ru\": \"ru-RU-SvetlanaNeural\",\n    \"ar\": \"ar-SA-ZariyahNeural\",\n}\n_DEFAULT_VOICE = \"fr-FR-DeniseNeural\"\n\n\ndef detect_voice(text: str) -> str:\n    try:\n        from langdetect import detect\n\n        lang = detect(text).lower()\n    except Exception:\n        return _DEFAULT_VOICE\n    if lang in _VOICE_BY_LANG:\n        return _VOICE_BY_LANG[lang]\n    # langdetect renvoie parfois \"zh-tw\" etc. — réduire au préfixe\n    return _VOICE_BY_LANG.get(lang.split(\"-\")[0], _DEFAULT_VOICE)\n\n\ndef synthesize(text: str, wav_path: Path, voice: str | None = None) -> None:\n    voice = voice or detect_voice(text)\n    mp3_path = wav_path.with_suffix(\".mp3\")\n    subprocess.run(\n        [str(_EDGE_TTS), \"--text\", text, \"--voice\", voice, \"--write-media\", str(mp3_path)],\n        check=True,\n        timeout=60,\n        capture_output=True,\n    )\n    subprocess.run(\n        [\"ffmpeg\", \"-y\", \"-i\", str(mp3_path), \"-ar\", \"16000\", \"-ac\", \"1\", str(wav_path)],\n        check=True,\n        capture_output=True,\n    )\n    mp3_path.unlink(missing_ok=True)\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":274,"cache_read_input_tokens":120835,"output_tokens":974,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":274,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":974,"cache_read_input_tokens":120835,"cache_creation_input_tokens":274,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":274},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SZ3mih63YzoaDnHpwV","type":"assistant","uuid":"6f22cdf1-9b0a-47f5-9961-387729abd41a","timestamp":"2026-06-16T06:10:20.603Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"6f22cdf1-9b0a-47f5-9961-387729abd41a","isSidechain":false,"promptId":"465d8e8a-0e79-4a8d-b40d-a729c73b1e60","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015f7QEPZwe7aKsmn67zgiN6","type":"tool_result","content":"The file /mnt/DATA/WORK/agent-gui/agent_gui/tts.py has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"uuid":"d5219912-a4e7-48f9-9cf1-ea6c2bc7ee1c","timestamp":"2026-06-16T06:10:20.636Z","toolUseResult":{"type":"update","filePath":"/mnt/DATA/WORK/agent-gui/agent_gui/tts.py","content":"from __future__ import annotations\n\nimport subprocess\nfrom pathlib import Path\n\n_EDGE_TTS = Path.home() / \"miniconda3/bin/edge-tts\"\n\n# Voix par langue détectée (code ISO 639-1 -> voix edge-tts)\n_VOICE_BY_LANG = {\n    \"fr\": \"fr-FR-DeniseNeural\",\n    \"en\": \"en-US-AriaNeural\",\n    \"es\": \"es-ES-ElviraNeural\",\n    \"de\": \"de-DE-KatjaNeural\",\n    \"it\": \"it-IT-ElsaNeural\",\n    \"pt\": \"pt-BR-FranciscaNeural\",\n    \"nl\": \"nl-NL-ColetteNeural\",\n    \"ja\": \"ja-JP-NanamiNeural\",\n    \"zh-cn\": \"zh-CN-XiaoxiaoNeural\",\n    \"ko\": \"ko-KR-SunHiNeural\",\n    \"ru\": \"ru-RU-SvetlanaNeural\",\n    \"ar\": \"ar-SA-ZariyahNeural\",\n}\n_DEFAULT_VOICE = \"fr-FR-DeniseNeural\"\n\n\ndef detect_voice(text: str) -> str:\n    try:\n        from langdetect import detect\n\n        lang = detect(text).lower()\n    except Exception:\n        return _DEFAULT_VOICE\n    if lang in _VOICE_BY_LANG:\n        return _VOICE_BY_LANG[lang]\n    # langdetect renvoie parfois \"zh-tw\" etc. — réduire au préfixe\n    return _VOICE_BY_LANG.get(lang.split(\"-\")[0], _DEFAULT_VOICE)\n\n\ndef synthesize(text: str, wav_path: Path, voice: str | None = None) -> None:\n    voice = voice or detect_voice(text)\n    mp3_path = wav_path.with_suffix(\".mp3\")\n    subprocess.run(\n        [str(_EDGE_TTS), \"--text\", text, \"--voice\", voice, \"--write-media\", str(mp3_path)],\n        check=True,\n        timeout=60,\n        capture_output=True,\n    )\n    subprocess.run(\n        [\"ffmpeg\", \"-y\", \"-i\", str(mp3_path), \"-ar\", \"16000\", \"-ac\", \"1\", str(wav_path)],\n        check=True,\n        capture_output=True,\n    )\n    mp3_path.unlink(missing_ok=True)\n","structuredPatch":[{"oldStart":5,"oldLines":8,"newStart":5,"newLines":39,"lines":[" "," _EDGE_TTS = Path.home() / \"miniconda3/bin/edge-tts\""," ","+# Voix par langue détectée (code ISO 639-1 -> voix edge-tts)","+_VOICE_BY_LANG = {","+    \"fr\": \"fr-FR-DeniseNeural\",","+    \"en\": \"en-US-AriaNeural\",","+    \"es\": \"es-ES-ElviraNeural\",","+    \"de\": \"de-DE-KatjaNeural\",","+    \"it\": \"it-IT-ElsaNeural\",","+    \"pt\": \"pt-BR-FranciscaNeural\",","+    \"nl\": \"nl-NL-ColetteNeural\",","+    \"ja\": \"ja-JP-NanamiNeural\",","+    \"zh-cn\": \"zh-CN-XiaoxiaoNeural\",","+    \"ko\": \"ko-KR-SunHiNeural\",","+    \"ru\": \"ru-RU-SvetlanaNeural\",","+    \"ar\": \"ar-SA-ZariyahNeural\",","+}","+_DEFAULT_VOICE = \"fr-FR-DeniseNeural\""," ","-def synthesize(text: str, wav_path: Path, voice: str = \"en-US-AriaNeural\") -> None:","+","+def detect_voice(text: str) -> str:","+    try:","+        from langdetect import detect","+","+        lang = detect(text).lower()","+    except Exception:","+        return _DEFAULT_VOICE","+    if lang in _VOICE_BY_LANG:","+        return _VOICE_BY_LANG[lang]","+    # langdetect renvoie parfois \"zh-tw\" etc. — réduire au préfixe","+    return _VOICE_BY_LANG.get(lang.split(\"-\")[0], _DEFAULT_VOICE)","+","+","+def synthesize(text: str, wav_path: Path, voice: str | None = None) -> None:","+    voice = voice or detect_voice(text)","     mp3_path = wav_path.with_suffix(\".mp3\")","     subprocess.run(","         [str(_EDGE_TTS), \"--text\", text, \"--voice\", voice, \"--write-media\", str(mp3_path)],"]}],"originalFile":"from __future__ import annotations\n\nimport subprocess\nfrom pathlib import Path\n\n_EDGE_TTS = Path.home() / \"miniconda3/bin/edge-tts\"\n\n\ndef synthesize(text: str, wav_path: Path, voice: str = \"en-US-AriaNeural\") -> None:\n    mp3_path = wav_path.with_suffix(\".mp3\")\n    subprocess.run(\n        [str(_EDGE_TTS), \"--text\", text, \"--voice\", voice, \"--write-media\", str(mp3_path)],\n        check=True,\n        timeout=60,\n        capture_output=True,\n    )\n    subprocess.run(\n        [\"ffmpeg\", \"-y\", \"-i\", str(mp3_path), \"-ar\", \"16000\", \"-ac\", \"1\", str(wav_path)],\n        check=True,\n        capture_output=True,\n    )\n    mp3_path.unlink(missing_ok=True)\n","userModified":false},"sourceToolAssistantUUID":"6f22cdf1-9b0a-47f5-9961-387729abd41a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"d5219912-a4e7-48f9-9cf1-ea6c2bc7ee1c","isSidechain":false,"attachment":{"type":"task_reminder","content":[],"itemCount":0},"type":"attachment","uuid":"e5d7fb3c-59ad-425d-8a92-c030d7aa4d3b","timestamp":"2026-06-16T06:10:20.770Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e5d7fb3c-59ad-425d-8a92-c030d7aa4d3b","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01681LheoEXEd6PnFBvHEopW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XdH9VhrwHVheqfvQsAYNhF","name":"Bash","input":{"command":"$HOME/miniconda3/bin/python -c \"\nimport sys; sys.path.insert(0,'/mnt/DATA/WORK/agent-gui')\nfrom agent_gui.tts import detect_voice\nprint('FR:', detect_voice('Bonjour, je suis votre assistant. Comment puis-je vous aider aujourd hui ?'))\nprint('EN:', detect_voice('Hello, I am your assistant. How can I help you today?'))\nprint('ES:', detect_voice('Hola, soy tu asistente. ¿Cómo puedo ayudarte?'))\nprint('short FR:', detect_voice('Bonjour !'))\n\"","description":"Test voice detection across languages"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":1035,"cache_read_input_tokens":121109,"output_tokens":293,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1035,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":293,"cache_read_input_tokens":121109,"cache_creation_input_tokens":1035,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1035},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6Sa9YmDum5V3jNt59Ep","type":"assistant","uuid":"6132657a-54e3-4c71-bd29-736eebb139b6","timestamp":"2026-06-16T06:10:25.599Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"6132657a-54e3-4c71-bd29-736eebb139b6","isSidechain":false,"promptId":"465d8e8a-0e79-4a8d-b40d-a729c73b1e60","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XdH9VhrwHVheqfvQsAYNhF","type":"tool_result","content":"FR: fr-FR-DeniseNeural\nEN: en-US-AriaNeural\nES: es-ES-ElviraNeural\nshort FR: fr-FR-DeniseNeural","is_error":false}]},"uuid":"3c11ac0b-e9b3-47e0-9473-f56b67383ef9","timestamp":"2026-06-16T06:10:25.812Z","toolUseResult":{"stdout":"FR: fr-FR-DeniseNeural\nEN: en-US-AriaNeural\nES: es-ES-ElviraNeural\nshort FR: fr-FR-DeniseNeural","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"6132657a-54e3-4c71-bd29-736eebb139b6","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"3c11ac0b-e9b3-47e0-9473-f56b67383ef9","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_014pjwQuFpUtKgRgNscWPGPw","type":"message","role":"assistant","content":[{"type":"text","text":"La détection marche. Je redémarre le serveur pour charger le nouveau `tts.py`."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":491,"cache_read_input_tokens":122144,"output_tokens":287,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":491,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":287,"cache_read_input_tokens":122144,"cache_creation_input_tokens":491,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":491},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SaX54gbZkLyfL2L7f7","type":"assistant","uuid":"06c17aeb-a625-419b-9519-880ac2437190","timestamp":"2026-06-16T06:10:28.843Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"06c17aeb-a625-419b-9519-880ac2437190","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_014pjwQuFpUtKgRgNscWPGPw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016kJZgxtidk9fTxZLHNqvYF","name":"Bash","input":{"command":"pkill -f \"agent_gui\" 2>/dev/null; sleep 2\ncd /mnt/DATA/WORK/agent-gui && nohup $HOME/miniconda3/bin/python -m agent_gui.app > /tmp/agentgui.log 2>&1 &\ndisown\nsleep 3\n$HOME/miniconda3/bin/python -c \"import urllib.request; print('UP:', urllib.request.urlopen('http://127.0.0.1:8765/health').status)\"\ncat /tmp/agentgui.log","description":"Restart server with new tts.py"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":491,"cache_read_input_tokens":122144,"output_tokens":287,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":491,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":287,"cache_read_input_tokens":122144,"cache_creation_input_tokens":491,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":491},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SaX54gbZkLyfL2L7f7","type":"assistant","uuid":"dca31c0e-4499-48cd-a96a-8c40f38f9ebc","timestamp":"2026-06-16T06:10:30.271Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"dca31c0e-4499-48cd-a96a-8c40f38f9ebc","isSidechain":false,"promptId":"465d8e8a-0e79-4a8d-b40d-a729c73b1e60","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 144","is_error":true,"tool_use_id":"toolu_016kJZgxtidk9fTxZLHNqvYF"}]},"uuid":"23d965a5-496d-49bd-b100-df415dd9aaf6","timestamp":"2026-06-16T06:10:30.357Z","toolUseResult":"Error: Exit code 144","sourceToolAssistantUUID":"dca31c0e-4499-48cd-a96a-8c40f38f9ebc","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"23d965a5-496d-49bd-b100-df415dd9aaf6","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01BrM4bj61XWUTztQxrcU2bM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LMDWa1fiBfNpkNKRJjwhuR","name":"Bash","input":{"command":"sleep 1; $HOME/miniconda3/bin/python -c \"import urllib.request; print('UP:', urllib.request.urlopen('http://127.0.0.1:8765/health').status)\" 2>&1","description":"Check server is up"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":304,"cache_read_input_tokens":122635,"output_tokens":142,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":304,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":142,"cache_read_input_tokens":122635,"cache_creation_input_tokens":304,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":304},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SarX6r6amf31uf5iM2","type":"assistant","uuid":"2a4ff867-8805-499a-977c-3e87f0bf2ad5","timestamp":"2026-06-16T06:10:34.809Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"2a4ff867-8805-499a-977c-3e87f0bf2ad5","isSidechain":false,"promptId":"465d8e8a-0e79-4a8d-b40d-a729c73b1e60","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 1319, in do_open\n    h.request(req.get_method(), req.selector, req.data, headers,\n    ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              encode_chunked=req.has_header('Transfer-encoding'))\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1358, in request\n    self._send_request(method, url, body, headers, encode_chunked)\n    ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1404, in _send_request\n    self.endheaders(body, encode_chunked=encode_chunked)\n    ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1353, in endheaders\n    self._send_output(message_body, encode_chunked=encode_chunked)\n    ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1113, in _send_output\n    self.send(msg)\n    ~~~~~~~~~^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1057, in send\n    self.connect()\n    ~~~~~~~~~~~~^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1023, in connect\n    self.sock = self._create_connection(\n                ~~~~~~~~~~~~~~~~~~~~~~~^\n        (self.host,self.port), self.timeout, self.source_address)\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/socket.py\", line 864, in create_connection\n    raise exceptions[0]\n  File \"/home/wildlama/miniconda3/lib/python3.13/socket.py\", line 849, in create_connection\n    sock.connect(sa)\n    ~~~~~~~~~~~~^^^^\nConnectionRefusedError: [Errno 111] Connection refused\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import urllib.request; print('UP:', urllib.request.urlopen('http://127.0.0.1:8765/health').status)\n                                        ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 189, in urlopen\n    return opener.open(url, data, timeout)\n           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 489, in open\n    response = self._open(req, data)\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 506, in _open\n    result = self._call_chain(self.handle_open, protocol, protocol +\n                              '_open', req)\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 466, in _call_chain\n    result = func(*args)\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 1348, in http_open\n    return self.do_open(http.client.HTTPConnection, req)\n           ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 1322, in do_open\n    raise URLError(err)\nurllib.error.URLError: <urlopen error [Errno 111] Connection refused>","is_error":true,"tool_use_id":"toolu_01LMDWa1fiBfNpkNKRJjwhuR"}]},"uuid":"00e84403-97ea-4e9c-95f7-038ba18656c6","timestamp":"2026-06-16T06:10:35.883Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 1319, in do_open\n    h.request(req.get_method(), req.selector, req.data, headers,\n    ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n              encode_chunked=req.has_header('Transfer-encoding'))\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1358, in request\n    self._send_request(method, url, body, headers, encode_chunked)\n    ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1404, in _send_request\n    self.endheaders(body, encode_chunked=encode_chunked)\n    ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1353, in endheaders\n    self._send_output(message_body, encode_chunked=encode_chunked)\n    ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1113, in _send_output\n    self.send(msg)\n    ~~~~~~~~~^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1057, in send\n    self.connect()\n    ~~~~~~~~~~~~^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/http/client.py\", line 1023, in connect\n    self.sock = self._create_connection(\n                ~~~~~~~~~~~~~~~~~~~~~~~^\n        (self.host,self.port), self.timeout, self.source_address)\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/socket.py\", line 864, in create_connection\n    raise exceptions[0]\n  File \"/home/wildlama/miniconda3/lib/python3.13/socket.py\", line 849, in create_connection\n    sock.connect(sa)\n    ~~~~~~~~~~~~^^^^\nConnectionRefusedError: [Errno 111] Connection refused\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import urllib.request; print('UP:', urllib.request.urlopen('http://127.0.0.1:8765/health').status)\n                                        ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 189, in urlopen\n    return opener.open(url, data, timeout)\n           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 489, in open\n    response = self._open(req, data)\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 506, in _open\n    result = self._call_chain(self.handle_open, protocol, protocol +\n                              '_open', req)\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 466, in _call_chain\n    result = func(*args)\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 1348, in http_open\n    return self.do_open(http.client.HTTPConnection, req)\n           ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/wildlama/miniconda3/lib/python3.13/urllib/request.py\", line 1322, in do_open\n    raise URLError(err)\nurllib.error.URLError: <urlopen error [Errno 111] Connection refused>","sourceToolAssistantUUID":"2a4ff867-8805-499a-977c-3e87f0bf2ad5","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"top ca fonctionne mais il faut que le TTS s'adapte à la langue en detectant automatiquement. Mes messages sont en francais mais le TTS est en anglais","leafUuid":"00e84403-97ea-4e9c-95f7-038ba18656c6","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"mode","mode":"normal","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"parentUuid":"00e84403-97ea-4e9c-95f7-038ba18656c6","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01UGE5XHyFnAbJP9tUPvzJaX","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1821,"cache_read_input_tokens":122939,"output_tokens":290,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1821,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":290,"cache_read_input_tokens":122939,"cache_creation_input_tokens":1821,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1821},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SbG9R2o9LJAbvA3sLr","type":"assistant","uuid":"d859e13e-86fe-453a-9e64-5fd6e23f95b3","timestamp":"2026-06-16T06:10:39.764Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"d859e13e-86fe-453a-9e64-5fd6e23f95b3","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01UGE5XHyFnAbJP9tUPvzJaX","type":"message","role":"assistant","content":[{"type":"text","text":"Le serveur ne reste pas up — le backgrounding via nohup est tué quand le shell se termine. J'utilise le vrai mode background du tool."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1821,"cache_read_input_tokens":122939,"output_tokens":290,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1821,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":290,"cache_read_input_tokens":122939,"cache_creation_input_tokens":1821,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1821},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SbG9R2o9LJAbvA3sLr","type":"assistant","uuid":"ed12424d-7ca6-4d13-9e05-cca9a49596fc","timestamp":"2026-06-16T06:10:40.619Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"ed12424d-7ca6-4d13-9e05-cca9a49596fc","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01UGE5XHyFnAbJP9tUPvzJaX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DCbzrKifWkxDEgNNtsgfB7","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/agent-gui && $HOME/miniconda3/bin/python -m agent_gui.app","description":"Run agent-gui server in background","run_in_background":true},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1821,"cache_read_input_tokens":122939,"output_tokens":290,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1821,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":290,"cache_read_input_tokens":122939,"cache_creation_input_tokens":1821,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1821},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SbG9R2o9LJAbvA3sLr","type":"assistant","uuid":"1d7b945a-1877-4a92-9799-1f7a357d4a36","timestamp":"2026-06-16T06:10:41.580Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"1d7b945a-1877-4a92-9799-1f7a357d4a36","isSidechain":false,"promptId":"465d8e8a-0e79-4a8d-b40d-a729c73b1e60","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DCbzrKifWkxDEgNNtsgfB7","type":"tool_result","content":"Command running in background with ID: bmyok7v78. Output is being written to: /tmp/claude-1000/-home-wildlama/60abcd12-7757-4c6d-8864-a329cd90680a/tasks/bmyok7v78.output. You will be notified when it completes. To check interim output, use Read on that file path.","is_error":false}]},"uuid":"b5672ce7-2cb2-4988-8b60-dba6e3c83a0d","timestamp":"2026-06-16T06:10:41.592Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false,"backgroundTaskId":"bmyok7v78"},"sourceToolAssistantUUID":"1d7b945a-1877-4a92-9799-1f7a357d4a36","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"b5672ce7-2cb2-4988-8b60-dba6e3c83a0d","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_013YnrvDSBu7pA8Y9XXxVEgT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LJns1HYMvU4uANWq6oyp4B","name":"Bash","input":{"command":"sleep 3; $HOME/miniconda3/bin/python -c \"import urllib.request; print('UP:', urllib.request.urlopen('http://127.0.0.1:8765/health').status)\"","description":"Verify server is up"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":401,"cache_read_input_tokens":124760,"output_tokens":138,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":401,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":138,"cache_read_input_tokens":124760,"cache_creation_input_tokens":401,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":401},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6SbgqUHVHRju3s49jVr","type":"assistant","uuid":"249d0c81-270d-4cf1-b035-0bafa9a1f15a","timestamp":"2026-06-16T06:10:47.628Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"249d0c81-270d-4cf1-b035-0bafa9a1f15a","isSidechain":false,"promptId":"465d8e8a-0e79-4a8d-b40d-a729c73b1e60","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LJns1HYMvU4uANWq6oyp4B","type":"tool_result","content":"UP: 200","is_error":false}]},"uuid":"ce0f2327-cf28-4915-a421-8c28a9dba4db","timestamp":"2026-06-16T06:10:50.733Z","toolUseResult":{"stdout":"UP: 200","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"249d0c81-270d-4cf1-b035-0bafa9a1f15a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"ce0f2327-cf28-4915-a421-8c28a9dba4db","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_01TZ81Eo9JGHALfhDtcNKnxB","type":"message","role":"assistant","content":[{"type":"text","text":"Le serveur tourne maintenant correctement. Le TTS détecte désormais la langue automatiquement.\n\n**Ce qui a changé dans `tts.py` :**\n- Détection auto via `langdetect` sur le texte de la réponse de l'agent\n- Mapping vers la bonne voix : **français → `fr-FR-DeniseNeural`**, anglais → Aria, + espagnol, allemand, italien, portugais, japonais, chinois, coréen, russe, arabe\n- Fallback en français si la détection échoue (texte trop court ou ambigu)\n\nTes messages en français déclencheront maintenant une réponse parlée en français par Barbara. Teste avec un nouveau message — pas besoin de rafraîchir la page, c'est côté serveur.\n\nUne remarque : la détection se fait sur **le texte de la réponse de l'agent**, pas sur ton message. Donc si l'agent répond en anglais à un message français, la voix sera anglaise (ce qui est correct — la voix suit la langue parlée). Si tu préfères forcer la voix selon **ta** langue à toi, dis-le moi et je passe la détection sur le message utilisateur à la place."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":148,"cache_read_input_tokens":125161,"output_tokens":400,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":148,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":400,"cache_read_input_tokens":125161,"cache_creation_input_tokens":148,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":148},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc6ScMeqDNidbU4kQFLVr","type":"assistant","uuid":"939158fb-b1ac-48a4-b353-2cc4b1702489","timestamp":"2026-06-16T06:10:59.258Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"939158fb-b1ac-48a4-b353-2cc4b1702489","isSidechain":false,"type":"system","subtype":"turn_duration","durationMs":69072,"messageCount":387,"timestamp":"2026-06-16T06:10:59.458Z","uuid":"7d3c69a7-e77e-425b-a9de-39f481d9bbad","isMeta":false,"userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"7d3c69a7-e77e-425b-a9de-39f481d9bbad","isSidechain":false,"type":"system","subtype":"away_summary","content":"Goal: add talking 3D LAM avatars to your agent-gui. The TTS now auto-detects language so French replies speak French via Barbara. Next: send a French message to confirm Barbara speaks French. (disable recaps in /config)","timestamp":"2026-06-16T06:14:03.101Z","uuid":"b1c901b7-6677-4ddb-872f-070df2d56c9f","isMeta":false,"userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"top ca fonctionne mais il faut que le TTS s'adapte à la langue en detectant automatiquement. Mes messages sont en francais mais le TTS est en anglais","leafUuid":"b1c901b7-6677-4ddb-872f-070df2d56c9f","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
{"type":"ai-title","aiTitle":"Installation et configuration de Blender","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-06-17T18:06:40.168Z","sessionId":"60abcd12-7757-4c6d-8864-a329cd90680a","content":"<task-notification>\n<task-id>bmyok7v78</task-id>\n<tool-use-id>toolu_01DCbzrKifWkxDEgNNtsgfB7</tool-use-id>\n<output-file>/tmp/claude-1000/-home-wildlama/60abcd12-7757-4c6d-8864-a329cd90680a/tasks/bmyok7v78.output</output-file>\n<status>killed</status>\n<summary>Background command \"Run agent-gui server in background\" was stopped</summary>\n</task-notification>"}