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{"parentUuid":"240ac4e1-e21d-481a-a1c7-3fd282742e43","isSidechain":false,"attachment":{"type":"agent_listing_delta","addedTypes":["claude","claude-code-guide","Explore","general-purpose","Plan","statusline-setup"],"addedLines":["- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)","- claude-code-guide: Use this agent when the user asks questions (\"Can Claude...\", \"Does Claude...\", \"How do I...\") about: (1) Claude Code (the CLI tool) - features, hooks, slash commands, MCP servers, settings, IDE integrations, keyboard shortcuts; (2) Claude Agent SDK - building custom agents; (3) Claude API (formerly Anthropic API) - API usage, tool use, Anthropic SDK usage. **IMPORTANT:** Before spawning a new agent, check if there is already a running or recently completed claude-code-guide agent that you can continue via SendMessage. (Tools: Bash, Read, WebFetch, WebSearch)","- Explore: Fast read-only search agent for locating code. Use it to find files by pattern (eg. \"src/components/**/*.tsx\"), grep for symbols or keywords (eg. \"API endpoints\"), or answer \"where is X defined / which files reference Y.\" Do NOT use it for code review, design-doc auditing, cross-file consistency checks, or open-ended analysis — it reads excerpts rather than whole files and will miss content past its read window. When calling, specify search breadth: \"quick\" for a single targeted lookup, \"medium\" for moderate exploration, or \"very thorough\" to search across multiple locations and naming conventions. (Tools: All tools except Agent, ExitPlanMode, Edit, Write, NotebookEdit)","- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)","- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, ExitPlanMode, Edit, Write, NotebookEdit)","- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)"],"removedTypes":[],"isInitial":true,"showConcurrencyNote":true},"type":"attachment","uuid":"5f4d95ad-2b07-4eda-a0d1-c8030b02acd0","timestamp":"2026-06-15T20:46:20.565Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"5f4d95ad-2b07-4eda-a0d1-c8030b02acd0","isSidechain":false,"attachment":{"type":"skill_listing","content":"- deep-research: Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report. - When the user wants a deep, multi-source, fact-checked research report on any topic. BEFORE invoking, check if the question is specific enough to research directly — if underspecified (e.g., \"what car to buy\" without budget/use-case/region), ask 2-3 clarifying questions to narrow scope. Then pass the refined question as args, weaving the answers in.\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- verify: Verify that a code change actually does what it's supposed to by running the app and observing behavior. Use when asked to verify a PR, confirm a fix works, test a change manually, check that a feature works, or validate local changes before pushing.\n- code-review: Review the current diff for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings). Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). 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. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- run: Launch and drive this project's app to see a change working. 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":"e586e768-4c96-44be-9fea-98747fea8043","timestamp":"2026-06-15T20:46:20.565Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"e586e768-4c96-44be-9fea-98747fea8043","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01HhSHXN9iaURLuuov5w1T8r","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"EqUCCmUIDhgCKkBu3NDvhxSZFbC1tBkEXr8YfjLaruz2Lbpeh+yOxoMP03ezsWytcfFORx6EW4+WtAG+d8yvkkhg8Bq4IRAA5umMMhFjbGF1ZGUtc29ubmV0LTQtNjgAQgh0aGlua2luZxIMh58ao4jj5BPHDKGXGgyZ0YIHch6sGvPVkjsiMO8nXtLpUt0t1lykiPUR9FiagPU3jWSjaXsud/LIiLqlI6zdGCloN4WEeeUiq/kgHypuZAUuYpJw3n/yu2Pm6tyYfrvlUAjh8Rqq3LBMak56D0lFZV9X4qq1j2wJBRLhufCqqyv5eO1tIkFQD9vfOiKCWU+kBan7Wn7cBDmK/ib9HG46xrI/+2HfvLq5uqBY7TFDclsks/PxlrnhLmqdUGoYAQ=="}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":4319,"cache_read_input_tokens":21333,"output_tokens":232,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":4319,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":3,"output_tokens":232,"cache_read_input_tokens":21333,"cache_creation_input_tokens":4319,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4319},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5hZfMcKitJwsauYesqY","type":"assistant","uuid":"42baddde-f882-4adb-9913-5c8325947121","timestamp":"2026-06-15T20:46:23.201Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"42baddde-f882-4adb-9913-5c8325947121","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01HhSHXN9iaURLuuov5w1T8r","type":"message","role":"assistant","content":[{"type":"text","text":"Je lis les fichiers clés du projet pendant que l'agent recherche LAM."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":4319,"cache_read_input_tokens":21333,"output_tokens":232,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":4319,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":3,"output_tokens":232,"cache_read_input_tokens":21333,"cache_creation_input_tokens":4319,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4319},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5hZfMcKitJwsauYesqY","type":"assistant","uuid":"da6d994f-f03e-4618-a64e-6f2ad15d55ff","timestamp":"2026-06-15T20:46:23.647Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"da6d994f-f03e-4618-a64e-6f2ad15d55ff","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01HhSHXN9iaURLuuov5w1T8r","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014YCFgsCzcN1KJX8txefwnC","name":"Read","input":{"file_path":"/mnt/DATA/WORK/agent-gui/README.md"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":4319,"cache_read_input_tokens":21333,"output_tokens":232,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":4319,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":3,"output_tokens":232,"cache_read_input_tokens":21333,"cache_creation_input_tokens":4319,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4319},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5hZfMcKitJwsauYesqY","type":"assistant","uuid":"aa62da2e-8394-43e3-ac37-e135e84220d5","timestamp":"2026-06-15T20:46:24.479Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"aa62da2e-8394-43e3-ac37-e135e84220d5","isSidechain":false,"promptId":"daf5fdf4-12b0-4555-ab7b-fb32be7411c8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014YCFgsCzcN1KJX8txefwnC","type":"tool_result","content":"1\t# Agent GUI Local\n2\t\n3\tWeb app locale qui sert de surcouche graphique universelle pour agents LLM CLI.\n4\t\n5\tV1 incluse:\n6\t\n7\t- Backend Python sans dépendance externe.\n8\t- Frontend HTML/CSS/JS vanilla.\n9\t- Chat local.\n10\t- Sessions JSONL.\n11\t- Adaptateur Echo pour tester sans modèle.\n12\t- Adaptateur CLI universel pour brancher Hermes, Claude Code, OpenClaw, etc.\n13\t- Bureau pixel art animé avec petits personnages façon arcade.\n14\t- Avatar de bureau en mode sprite, avec états: idle, thinking, tool, speaking, error.\n15\t\n16\t## Lancer\n17\t\n18\t```bash\n19\tcd /home/wildlama/agent-gui\n20\tpython3 -m agent_gui.app --host 127.0.0.1 --port 8765\n21\t```\n22\t\n23\tPuis ouvrir:\n24\t\n25\t```text\n26\thttp://127.0.0.1:8765\n27\t```\n28\t\n29\t## Tester\n30\t\n31\t```bash\n32\tcd /home/wildlama/agent-gui\n33\tpython3 -m unittest discover -s tests -v\n34\t```\n35\t\n36\tSmoke-test API:\n37\t\n38\t```bash\n39\tcurl http://127.0.0.1:8765/health\n40\tcurl -X POST http://127.0.0.1:8765/api/chat \\\n41\t  -H 'Content-Type: application/json' \\\n42\t  -d '{\"agent\":\"echo\",\"message\":\"hello\"}'\n43\t```\n44\t\n45\t## Ajouter un agent\n46\t\n47\tÉditer:\n48\t\n49\t```text\n50\t/home/wildlama/agent-gui/configs/agents.json\n51\t```\n52\t\n53\tExemple:\n54\t\n55\t```json\n56\t{\n57\t  \"id\": \"mon-agent\",\n58\t  \"name\": \"Mon Agent\",\n59\t  \"type\": \"cli\",\n60\t  \"description\": \"Agent CLI custom\",\n61\t  \"command\": [\"mon-agent\", \"run\", \"{prompt}\"],\n62\t  \"timeout_seconds\": 300,\n63\t  \"enabled\": true\n64\t}\n65\t```\n66\t\n67\tRègles:\n68\t\n69\t- `{prompt}` est remplacé par le message utilisateur.\n70\t- `{session_id}` peut être utilisé si l'agent accepte un identifiant de session.\n71\t- La commande est une liste d'arguments, pas une string shell.\n72\t- Le backend n'utilise pas `shell=True`, pour éviter les injections shell.\n73\t\n74\t## Agents préconfigurés\n75\t\n76\t- `echo`: intégré, marche toujours.\n77\t- `hermes`: `hermes chat -q {prompt}`.\n78\t- `claude-code`: `claude -p --output-format text --permission-mode default {prompt}`.\n79\t- `openclaw`: exemple désactivé, à ajuster selon ton installation.\n80\t\n81\t## Limites V1\n82\t\n83\t- Pas encore de streaming token par token.\n84\t- Pas encore d'approbations interactives dans l'UI.\n85\t- Les agents CLI sont appelés en one-shot.\n86\t- Les sorties tool calls ne sont pas encore parsées profondément, elles sont affichées comme messages ou status.\n87\t\n88\t## Suite logique\n89\t\n90\t- Ajouter SSE ou WebSocket pour streaming temps réel.\n91\t- Ajouter des sprites 2D par état à la place de l'ASCII.\n92\t- Ajouter un protocole d'événements JSON optionnel pour agents compatibles.\n93\t- Ajouter un adaptateur Hermes profond via API gateway ou ACP.\n94\t- Ajouter un panneau fichiers / workspace.\n95\t- Ajouter approvals UI pour commandes dangereuses.\n96\t\n97\tVoir aussi:\n98\t\n99\t```text\n100\t/home/wildlama/agent-gui/PLAN.md\n101\t```\n102\t"}]},"uuid":"5b85f7a4-4cbf-4898-bff5-b2648f3addf3","timestamp":"2026-06-15T20:46:24.503Z","toolUseResult":{"type":"text","file":{"filePath":"/mnt/DATA/WORK/agent-gui/README.md","content":"# Agent GUI Local\n\nWeb app locale qui sert de surcouche graphique universelle pour agents LLM CLI.\n\nV1 incluse:\n\n- Backend Python sans dépendance externe.\n- Frontend HTML/CSS/JS vanilla.\n- Chat local.\n- Sessions JSONL.\n- Adaptateur Echo pour tester sans modèle.\n- Adaptateur CLI universel pour brancher Hermes, Claude Code, OpenClaw, etc.\n- Bureau pixel art animé avec petits personnages façon arcade.\n- Avatar de bureau en mode sprite, avec états: idle, thinking, tool, speaking, error.\n\n## Lancer\n\n```bash\ncd /home/wildlama/agent-gui\npython3 -m agent_gui.app --host 127.0.0.1 --port 8765\n```\n\nPuis ouvrir:\n\n```text\nhttp://127.0.0.1:8765\n```\n\n## Tester\n\n```bash\ncd /home/wildlama/agent-gui\npython3 -m unittest discover -s tests -v\n```\n\nSmoke-test API:\n\n```bash\ncurl http://127.0.0.1:8765/health\ncurl -X POST http://127.0.0.1:8765/api/chat \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"agent\":\"echo\",\"message\":\"hello\"}'\n```\n\n## Ajouter un agent\n\nÉditer:\n\n```text\n/home/wildlama/agent-gui/configs/agents.json\n```\n\nExemple:\n\n```json\n{\n  \"id\": \"mon-agent\",\n  \"name\": \"Mon Agent\",\n  \"type\": \"cli\",\n  \"description\": \"Agent CLI custom\",\n  \"command\": [\"mon-agent\", \"run\", \"{prompt}\"],\n  \"timeout_seconds\": 300,\n  \"enabled\": true\n}\n```\n\nRègles:\n\n- `{prompt}` est remplacé par le message utilisateur.\n- `{session_id}` peut être utilisé si l'agent accepte un identifiant de session.\n- La commande est une liste d'arguments, pas une string shell.\n- Le backend n'utilise pas `shell=True`, pour éviter les injections shell.\n\n## Agents préconfigurés\n\n- `echo`: intégré, marche toujours.\n- `hermes`: `hermes chat -q {prompt}`.\n- `claude-code`: `claude -p --output-format text --permission-mode default {prompt}`.\n- `openclaw`: exemple désactivé, à ajuster selon ton installation.\n\n## Limites V1\n\n- Pas encore de streaming token par token.\n- Pas encore d'approbations interactives dans l'UI.\n- Les agents CLI sont appelés en one-shot.\n- Les sorties tool calls ne sont pas encore parsées profondément, elles sont affichées comme messages ou status.\n\n## Suite logique\n\n- Ajouter SSE ou WebSocket pour streaming temps réel.\n- Ajouter des sprites 2D par état à la place de l'ASCII.\n- Ajouter un protocole d'événements JSON optionnel pour agents compatibles.\n- Ajouter un adaptateur Hermes profond via API gateway ou ACP.\n- Ajouter un panneau fichiers / workspace.\n- Ajouter approvals UI pour commandes dangereuses.\n\nVoir aussi:\n\n```text\n/home/wildlama/agent-gui/PLAN.md\n```\n","numLines":102,"startLine":1,"totalLines":102}},"sourceToolAssistantUUID":"aa62da2e-8394-43e3-ac37-e135e84220d5","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"7a19263a-009e-4d02-985a-bc31fb6b5f17","isSidechain":false,"promptId":"daf5fdf4-12b0-4555-ab7b-fb32be7411c8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01H8qoK8HJrMDv3smpV3jyjC","type":"tool_result","content":"1\tfrom __future__ import annotations\n2\t\n3\tfrom dataclasses import dataclass\n4\t\n5\t\n6\t@dataclass(frozen=True)\n7\tclass AvatarFrame:\n8\t    state: str\n9\t    label: str\n10\t    art: str\n11\t\n12\t\n13\t_FRAMES: dict[str, AvatarFrame] = {\n14\t    \"idle\": AvatarFrame(\n15\t        state=\"idle\",\n16\t        label=\"En attente\",\n17\t        art='''\n18\t        .-\"\"\"-.\n19\t       /  . .  \\\\\n20\t       |   ^   |\n21\t       | \\\\___/ |\n22\t        \\\\_____/\n23\t       /|  |  |\\\\\n24\t      /_|__|__|_\\\\\n25\t         /_\\\\\n26\t        '''.strip(\"\\n\"),\n27\t    ),\n28\t    \"thinking\": AvatarFrame(\n29\t        state=\"thinking\",\n30\t        label=\"Réflexion\",\n31\t        art='''\n32\t        .-\"\"\"-.\n33\t       /  o o  \\\\    ?\n34\t       |   ~   |  ?\n35\t       |  ___  |\n36\t        \\\\_____/\n37\t       /|  |  |\\\\\n38\t      /_|__|__|_\\\\\n39\t         /_\\\\\n40\t        '''.strip(\"\\n\"),\n41\t    ),\n42\t    \"tool\": AvatarFrame(\n43\t        state=\"tool\",\n44\t        label=\"Outil en cours\",\n45\t        art='''\n46\t        .-\"\"\"-.\n47\t       /  > <  \\\\   [#]\n48\t       |   -   |  /|\\\\\n49\t       | \\\\___/ |   |\n50\t        \\\\_____/\n51\t       /|  |  |\\\\\n52\t      /_|__|__|_\\\\\n53\t         /_\\\\\n54\t        '''.strip(\"\\n\"),\n55\t    ),\n56\t    \"speaking\": AvatarFrame(\n57\t        state=\"speaking\",\n58\t        label=\"Réponse\",\n59\t        art='''\n60\t        .-\"\"\"-.\n61\t       /  ^ ^  \\\\  )))\n62\t       |   O   |\n63\t       | \\\\___/ |\n64\t        \\\\_____/\n65\t       /|  |  |\\\\\n66\t      /_|__|__|_\\\\\n67\t         /_\\\\\n68\t        '''.strip(\"\\n\"),\n69\t    ),\n70\t    \"error\": AvatarFrame(\n71\t        state=\"error\",\n72\t        label=\"Erreur\",\n73\t        art='''\n74\t        .-\"\"\"-.\n75\t       /  x x  \\\\   !\n76\t       |   _   |  !!!\n77\t       | /___\\\\ |\n78\t        \\\\_____/\n79\t       /|  |  |\\\\\n80\t      /_|__|__|_\\\\\n81\t         /_\\\\\n82\t        '''.strip(\"\\n\"),\n83\t    ),\n84\t}\n85\t\n86\t\n87\tdef get_avatar(state: str = \"idle\") -> dict[str, str]:\n88\t    frame = _FRAMES.get(state, _FRAMES[\"idle\"])\n89\t    return {\"state\": frame.state, \"label\": frame.label, \"art\": frame.art}\n90\t\n91\t\n92\tdef available_states() -> list[str]:\n93\t    return list(_FRAMES.keys())\n94\t"}]},"uuid":"053f9990-fedf-4104-bd7b-d538ba198e8b","timestamp":"2026-06-15T20:46:24.945Z","toolUseResult":{"type":"text","file":{"filePath":"/mnt/DATA/WORK/agent-gui/agent_gui/avatar.py","content":"from __future__ import annotations\n\nfrom dataclasses import dataclass\n\n\n@dataclass(frozen=True)\nclass AvatarFrame:\n    state: str\n    label: str\n    art: str\n\n\n_FRAMES: dict[str, AvatarFrame] = {\n    \"idle\": AvatarFrame(\n        state=\"idle\",\n        label=\"En attente\",\n        art='''\n        .-\"\"\"-.\n       /  . .  \\\\\n       |   ^   |\n       | \\\\___/ |\n        \\\\_____/\n       /|  |  |\\\\\n      /_|__|__|_\\\\\n         /_\\\\\n        '''.strip(\"\\n\"),\n    ),\n    \"thinking\": AvatarFrame(\n        state=\"thinking\",\n        label=\"Réflexion\",\n        art='''\n        .-\"\"\"-.\n       /  o o  \\\\    ?\n       |   ~   |  ?\n       |  ___  |\n        \\\\_____/\n       /|  |  |\\\\\n      /_|__|__|_\\\\\n         /_\\\\\n        '''.strip(\"\\n\"),\n    ),\n    \"tool\": AvatarFrame(\n        state=\"tool\",\n        label=\"Outil en cours\",\n        art='''\n        .-\"\"\"-.\n       /  > <  \\\\   [#]\n       |   -   |  /|\\\\\n       | \\\\___/ |   |\n        \\\\_____/\n       /|  |  |\\\\\n      /_|__|__|_\\\\\n         /_\\\\\n        '''.strip(\"\\n\"),\n    ),\n    \"speaking\": AvatarFrame(\n        state=\"speaking\",\n        label=\"Réponse\",\n        art='''\n        .-\"\"\"-.\n       /  ^ ^  \\\\  )))\n       |   O   |\n       | \\\\___/ |\n        \\\\_____/\n       /|  |  |\\\\\n      /_|__|__|_\\\\\n         /_\\\\\n        '''.strip(\"\\n\"),\n    ),\n    \"error\": AvatarFrame(\n        state=\"error\",\n        label=\"Erreur\",\n        art='''\n        .-\"\"\"-.\n       /  x x  \\\\   !\n       |   _   |  !!!\n       | /___\\\\ |\n        \\\\_____/\n       /|  |  |\\\\\n      /_|__|__|_\\\\\n         /_\\\\\n        '''.strip(\"\\n\"),\n    ),\n}\n\n\ndef get_avatar(state: str = \"idle\") -> dict[str, str]:\n    frame = _FRAMES.get(state, _FRAMES[\"idle\"])\n    return {\"state\": frame.state, \"label\": frame.label, \"art\": frame.art}\n\n\ndef available_states() -> list[str]:\n    return list(_FRAMES.keys())\n","numLines":94,"startLine":1,"totalLines":94}},"sourceToolAssistantUUID":"7a19263a-009e-4d02-985a-bc31fb6b5f17","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"9881ada6-7cb1-4b2e-b040-cea73ca19678","isSidechain":false,"promptId":"daf5fdf4-12b0-4555-ab7b-fb32be7411c8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018HbMpwsSh3CiXi9mRtqQdH","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":"8cf02786-1e0d-4362-ade3-7a996da165bf","timestamp":"2026-06-15T20:46:25.486Z","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  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{"type":"last-prompt","lastPrompt":"/mnt/DATA/WORK/agent-gui ici je build une GUI pour mes agent IA, pour qu'ils aient un avatar et que je puisse les modeler comme je veux. est ce qu'on pourrait mettre ça dessus? comme ça les avatars po…","leafUuid":"8cf02786-1e0d-4362-ade3-7a996da165bf","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7"}
{"type":"ai-title","aiTitle":"Intégrer LAM pour avatars parlants dans GUI agent IA","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7"}
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{"parentUuid":"028d6bbb-dfdb-476f-815c-2a650362ae4a","isSidechain":false,"promptId":"daf5fdf4-12b0-4555-ab7b-fb32be7411c8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011THBiKSFhJAnCvcSNfPqZj","type":"tool_result","content":"1\t# Agent GUI Local - Plan de construction\n2\t\n3\t## Vision\n4\t\n5\tCréer une web app locale qui sert de surcouche graphique universelle pour des agents LLM existants.\n6\t\n7\tObjectif: ne pas remplacer Hermes, Claude Code, OpenClaw ou autres agents, mais leur fournir une interface commune avec chat, historique, timeline d'activité, logs, approbations futures, et avatar visuel. La première version utilise un avatar ASCII, puis pourra évoluer vers sprites 2D.\n8\t\n9\t## Principes produit\n10\t\n11\t- Local-first: l'app tourne sur `127.0.0.1`, sans cloud obligatoire.\n12\t- Agent-agnostic: chaque agent est branché via un adaptateur.\n13\t- Simple à installer: Python stdlib pour le backend, HTML/CSS/JS vanilla pour le frontend.\n14\t- Extensible: ajout d'un agent via un fichier de config ou une nouvelle classe adapter.\n15\t- Lisible pour reprise par d'autres agents: code commenté, README, tests, plan de continuité.\n16\t\n17\t## Architecture prévue\n18\t\n19\t```text\n20\tagent-gui/\n21\t  agent_gui/\n22\t    __init__.py\n23\t    app.py              # serveur HTTP local, API REST, endpoints statiques\n24\t    adapters.py         # protocole commun + adaptateurs CLI\n25\t    sessions.py         # stockage léger en JSONL\n26\t    config.py           # chargement configs d'agents\n27\t    avatar.py           # états et frames ASCII\n28\t  static/\n29\t    index.html          # UI principale\n30\t    styles.css          # style app + avatar ASCII\n31\t    app.js              # logique frontend, appels API\n32\t  configs/\n33\t    agents.json         # agents disponibles: echo, hermes, claude, openclaw\n34\t  tests/\n35\t    test_adapters.py\n36\t    test_sessions.py\n37\t    test_avatar.py\n38\t  README.md\n39\t  PLAN.md\n40\t```\n41\t\n42\t## Protocole interne commun\n43\t\n44\tChaque agent externe doit être ramené à des événements standardisés:\n45\t\n46\t```json\n47\t{\n48\t  \"type\": \"assistant_message | user_message | tool_request | tool_result | status | error\",\n49\t  \"agent\": \"hermes\",\n50\t  \"session_id\": \"...\",\n51\t  \"content\": \"texte visible\",\n52\t  \"raw\": \"sortie brute optionnelle\",\n53\t  \"created_at\": \"ISO timestamp\"\n54\t}\n55\t```\n56\t\n57\tLa V1 simplifie: elle exécute un agent CLI en mode one-shot, récupère stdout/stderr, puis retourne un message assistant + événements status.\n58\t\n59\t## Adaptateurs V1\n60\t\n61\t### EchoAdapter\n62\t\n63\tAdaptateur de test intégré. Il renvoie le prompt utilisateur sans appeler de modèle. Il sert aux tests et au smoke-test sans credentials.\n64\t\n65\t### CliAdapter\n66\t\n67\tAdaptateur universel pour commandes terminal.\n68\t\n69\tExemples prévus dans `configs/agents.json`:\n70\t\n71\t- Hermes:\n72\t  - commande: `hermes chat -q {prompt}`\n73\t- Claude Code:\n74\t  - commande indicative: `claude -p {prompt}`\n75\t- OpenClaw:\n76\t  - commande indicative à ajuster selon installation\n77\t\n78\tImportant: l'adaptateur ne doit jamais passer le prompt par interpolation shell non protégée. Il utilisera une liste d'arguments avec placeholder `{prompt}`.\n79\t\n80\t## UI V1\n81\t\n82\tÉcran unique:\n83\t\n84\t- Sidebar agents\n85\t- Sélecteur d'agent\n86\t- Chat central\n87\t- Zone de saisie\n88\t- Avatar ASCII à droite\n89\t- Panneau timeline / logs\n90\t- Boutons:\n91\t  - envoyer\n92\t  - nouvelle session\n93\t  - recharger agents\n94\t\n95\tÉtats avatar:\n96\t\n97\t- idle: repos\n98\t- thinking: réflexion\n99\t- tool: utilisation outil / commande\n100\t- speaking: réponse reçue\n101\t- error: erreur\n102\t\n103\t## Tests prévus\n104\t\n105\t- Avatar: chaque état retourne une frame non vide.\n106\t- Config: chargement du fichier agents JSON.\n107\t- Adapters:\n108\t  - EchoAdapter renvoie bien un événement assistant.\n109\t  - CliAdapter exécute une commande de test Python simple.\n110\t- Sessions:\n111\t  - append événement JSONL\n112\t  - relire historique\n113\t\n114\tCommandes de validation:\n115\t\n116\t```bash\n117\tcd /home/wildlama/agent-gui\n118\tpython3 -m unittest discover -s tests -v\n119\tpython3 -m agent_gui.app --host 127.0.0.1 --port 8765\n120\tcurl http://127.0.0.1:8765/health\n121\tcurl -X POST http://127.0.0.1:8765/api/chat \\\n122\t  -H 'Content-Type: application/json' \\\n123\t  -d '{\"agent\":\"echo\",\"message\":\"hello\"}'\n124\t```\n125\t\n126\t## État actuel du travail\n127\t\n128\tDébuté le 2026-06-15.\n129\t\n130\tFait:\n131\t\n132\t- Dossier projet créé: `/home/wildlama/agent-gui`\n133\t- Package Python créé: `/home/wildlama/agent-gui/agent_gui/__init__.py`\n134\t- Plan de reprise créé: `/home/wildlama/agent-gui/PLAN.md`\n135\t- Backend Python stdlib créé: `agent_gui/app.py`\n136\t- Adaptateurs créés: `agent_gui/adapters.py`\n137\t- Config agents créée: `agent_gui/config.py` + `configs/agents.json`\n138\t- Stockage sessions JSONL créé: `agent_gui/sessions.py`\n139\t- Avatar ASCII à états créé: `agent_gui/avatar.py`\n140\t- Frontend créé: `static/index.html`, `static/styles.css`, `static/app.js`\n141\t- Tests créés: `tests/test_avatar.py`, `tests/test_adapters.py`, `tests/test_config.py`, `tests/test_sessions.py`\n142\t- README créé: `/home/wildlama/agent-gui/README.md`\n143\t- Tests unitaires passés: `python3 -m unittest discover -s tests -v` -> 10 tests OK\n144\t- Serveur lancé en arrière-plan sur `http://127.0.0.1:8765`\n145\t- Smoke-test API OK: `/health`, `/api/agents`, `/api/chat` avec agent `echo`\n146\t- Smoke-test navigateur OK: message envoyé via l'UI, réponse Echo affichée\n147\t- Hermes CLI détecté sur cette machine: `/home/wildlama/.local/bin/hermes`\n148\t\n149\tEn cours:\n150\t\n151\t- L'interface a été transformée en bureau pixel art avec sprites animés et ambiance arcade.\n152\t- Rien de bloquant. La V2 visuelle est en place.\n153\t\n154\tÀ faire ensuite si un autre agent reprend:\n155\t\n156\t1. Ajouter streaming temps réel via SSE ou WebSocket.\n157\t2. Ajouter parsing d'événements JSON structurés pour agents compatibles.\n158\t3. Ajouter approbations interactives côté UI.\n159\t4. Ajouter sprites 2D à la place de l'avatar ASCII.\n160\t5. Créer un adaptateur Hermes profond, au-delà du simple `hermes chat -q`.\n161\t6. Créer un adaptateur Claude Code profond si son format stream/JSON est disponible.\n162\t7. Ajouter packaging desktop Tauri si besoin.\n163\t\n164\t## Notes pour un agent qui reprendrait le travail\n165\t\n166\t- Ne pas demander plus de specs: partir sur une V1 minimaliste mais fonctionnelle.\n167\t- Ne pas installer de dépendances lourdes pour la V1.\n168\t- Préférer Python stdlib pour que ça marche immédiatement.\n169\t- Ne pas faire une simple maquette statique: il faut une vraie app qui lance au moins l'adaptateur echo et peut appeler des commandes CLI configurées.\n170\t- Pour les commandes externes, éviter `shell=True`.\n171\t- Prévoir que Hermes existe probablement sur cette machine, mais ne pas rendre les tests dépendants de Hermes.\n172\t- Le README doit expliquer comment ajouter un nouvel agent.\n173\t\n174\t## Roadmap après V1\n175\t\n176\t- Streaming temps réel via SSE ou WebSocket.\n177\t- Approbations de commandes dans l'UI.\n178\t- Import/export sessions.\n179\t- Avatars sprites 2D par état.\n180\t- Adaptateur Hermes plus profond via gateway API ou session store.\n181\t- Adaptateur Claude Code plus profond si son format JSON/stream est disponible.\n182\t- Thèmes visuels par agent.\n183\t"}]},"uuid":"90996f3d-ec48-4274-a4f2-04e786d1be61","timestamp":"2026-06-15T20:46:29.862Z","toolUseResult":{"type":"text","file":{"filePath":"/mnt/DATA/WORK/agent-gui/PLAN.md","content":"# Agent GUI Local - Plan de construction\n\n## Vision\n\nCréer une web app locale qui sert de surcouche graphique universelle pour des agents LLM existants.\n\nObjectif: ne pas remplacer Hermes, Claude Code, OpenClaw ou autres agents, mais leur fournir une interface commune avec chat, historique, timeline d'activité, logs, approbations futures, et avatar visuel. La première version utilise un avatar ASCII, puis pourra évoluer vers sprites 2D.\n\n## Principes produit\n\n- Local-first: l'app tourne sur `127.0.0.1`, sans cloud obligatoire.\n- Agent-agnostic: chaque agent est branché via un adaptateur.\n- Simple à installer: Python stdlib pour le backend, HTML/CSS/JS vanilla pour le frontend.\n- Extensible: ajout d'un agent via un fichier de config ou une nouvelle classe adapter.\n- Lisible pour reprise par d'autres agents: code commenté, README, tests, plan de continuité.\n\n## Architecture prévue\n\n```text\nagent-gui/\n  agent_gui/\n    __init__.py\n    app.py              # serveur HTTP local, API REST, endpoints statiques\n    adapters.py         # protocole commun + adaptateurs CLI\n    sessions.py         # stockage léger en JSONL\n    config.py           # chargement configs d'agents\n    avatar.py           # états et frames ASCII\n  static/\n    index.html          # UI principale\n    styles.css          # style app + avatar ASCII\n    app.js              # logique frontend, appels API\n  configs/\n    agents.json         # agents disponibles: echo, hermes, claude, openclaw\n  tests/\n    test_adapters.py\n    test_sessions.py\n    test_avatar.py\n  README.md\n  PLAN.md\n```\n\n## Protocole interne commun\n\nChaque agent externe doit être ramené à des événements standardisés:\n\n```json\n{\n  \"type\": \"assistant_message | user_message | tool_request | tool_result | status | error\",\n  \"agent\": \"hermes\",\n  \"session_id\": \"...\",\n  \"content\": \"texte visible\",\n  \"raw\": \"sortie brute optionnelle\",\n  \"created_at\": \"ISO timestamp\"\n}\n```\n\nLa V1 simplifie: elle exécute un agent CLI en mode one-shot, récupère stdout/stderr, puis retourne un message assistant + événements status.\n\n## Adaptateurs V1\n\n### EchoAdapter\n\nAdaptateur de test intégré. Il renvoie le prompt utilisateur sans appeler de modèle. Il sert aux tests et au smoke-test sans credentials.\n\n### CliAdapter\n\nAdaptateur universel pour commandes terminal.\n\nExemples prévus dans `configs/agents.json`:\n\n- Hermes:\n  - commande: `hermes chat -q {prompt}`\n- Claude Code:\n  - commande indicative: `claude -p {prompt}`\n- OpenClaw:\n  - commande indicative à ajuster selon installation\n\nImportant: l'adaptateur ne doit jamais passer le prompt par interpolation shell non protégée. Il utilisera une liste d'arguments avec placeholder `{prompt}`.\n\n## UI V1\n\nÉcran unique:\n\n- Sidebar agents\n- Sélecteur d'agent\n- Chat central\n- Zone de saisie\n- Avatar ASCII à droite\n- Panneau timeline / logs\n- Boutons:\n  - envoyer\n  - nouvelle session\n  - recharger agents\n\nÉtats avatar:\n\n- idle: repos\n- thinking: réflexion\n- tool: utilisation outil / commande\n- speaking: réponse reçue\n- error: erreur\n\n## Tests prévus\n\n- Avatar: chaque état retourne une frame non vide.\n- Config: chargement du fichier agents JSON.\n- Adapters:\n  - EchoAdapter renvoie bien un événement assistant.\n  - CliAdapter exécute une commande de test Python simple.\n- Sessions:\n  - append événement JSONL\n  - relire historique\n\nCommandes de validation:\n\n```bash\ncd /home/wildlama/agent-gui\npython3 -m unittest discover -s tests -v\npython3 -m agent_gui.app --host 127.0.0.1 --port 8765\ncurl http://127.0.0.1:8765/health\ncurl -X POST http://127.0.0.1:8765/api/chat \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"agent\":\"echo\",\"message\":\"hello\"}'\n```\n\n## État actuel du travail\n\nDébuté le 2026-06-15.\n\nFait:\n\n- Dossier projet créé: `/home/wildlama/agent-gui`\n- Package Python créé: `/home/wildlama/agent-gui/agent_gui/__init__.py`\n- Plan de reprise créé: `/home/wildlama/agent-gui/PLAN.md`\n- Backend Python stdlib créé: `agent_gui/app.py`\n- Adaptateurs créés: `agent_gui/adapters.py`\n- Config agents créée: `agent_gui/config.py` + `configs/agents.json`\n- Stockage sessions JSONL créé: `agent_gui/sessions.py`\n- Avatar ASCII à états créé: `agent_gui/avatar.py`\n- Frontend créé: `static/index.html`, `static/styles.css`, `static/app.js`\n- Tests créés: `tests/test_avatar.py`, `tests/test_adapters.py`, `tests/test_config.py`, `tests/test_sessions.py`\n- README créé: `/home/wildlama/agent-gui/README.md`\n- Tests unitaires passés: `python3 -m unittest discover -s tests -v` -> 10 tests OK\n- Serveur lancé en arrière-plan sur `http://127.0.0.1:8765`\n- Smoke-test API OK: `/health`, `/api/agents`, `/api/chat` avec agent `echo`\n- Smoke-test navigateur OK: message envoyé via l'UI, réponse Echo affichée\n- Hermes CLI détecté sur cette machine: `/home/wildlama/.local/bin/hermes`\n\nEn cours:\n\n- L'interface a été transformée en bureau pixel art avec sprites animés et ambiance arcade.\n- Rien de bloquant. La V2 visuelle est en place.\n\nÀ faire ensuite si un autre agent reprend:\n\n1. Ajouter streaming temps réel via SSE ou WebSocket.\n2. Ajouter parsing d'événements JSON structurés pour agents compatibles.\n3. Ajouter approbations interactives côté UI.\n4. Ajouter sprites 2D à la place de l'avatar ASCII.\n5. Créer un adaptateur Hermes profond, au-delà du simple `hermes chat -q`.\n6. Créer un adaptateur Claude Code profond si son format stream/JSON est disponible.\n7. Ajouter packaging desktop Tauri si besoin.\n\n## Notes pour un agent qui reprendrait le travail\n\n- Ne pas demander plus de specs: partir sur une V1 minimaliste mais fonctionnelle.\n- Ne pas installer de dépendances lourdes pour la V1.\n- Préférer Python stdlib pour que ça marche immédiatement.\n- Ne pas faire une simple maquette statique: il faut une vraie app qui lance au moins l'adaptateur echo et peut appeler des commandes CLI configurées.\n- Pour les commandes externes, éviter `shell=True`.\n- Prévoir que Hermes existe probablement sur cette machine, mais ne pas rendre les tests dépendants de Hermes.\n- Le README doit expliquer comment ajouter un nouvel agent.\n\n## Roadmap après V1\n\n- Streaming temps réel via SSE ou WebSocket.\n- Approbations de commandes dans l'UI.\n- Import/export sessions.\n- Avatars sprites 2D par état.\n- Adaptateur Hermes plus profond via gateway API ou session store.\n- Adaptateur Claude Code plus profond si son format JSON/stream est disponible.\n- Thèmes visuels par agent.\n","numLines":183,"startLine":1,"totalLines":183}},"sourceToolAssistantUUID":"028d6bbb-dfdb-476f-815c-2a650362ae4a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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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}","numLines":80,"startLine":1,"totalLines":844}},"sourceToolAssistantUUID":"c81911af-21ec-428b-8234-6d0086280853","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"49d2b648-3525-4cbe-91c0-727654695791","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01KcXRNYo1Xyu2txMSt3aTVd","type":"message","role":"assistant","content":[{"type":"text","text":"J'ai une bonne vision du projet. En attendant le résultat de la recherche LAM, voici ce que je vois déjà :\n\n---\n\n**Ton projet actuel** — stack Python stdlib + JS vanilla, avatar ASCII multi-états, canvas pixel-art animé. La roadmap mentionne explicitement \"sprites 2D\" comme prochaine étape.\n\n**Ce que LAM fait** (d'après ce que je connais déjà) — Large Animation Model génère un avatar 3D parlant depuis une photo portrait + audio, avec lip-sync et expressions faciales. Output : mesh 3D animé ou vidéo rendue.\n\n**L'intégration est faisable, mais en deux couches distinctes :**\n\n```\n[Agent répond] → [TTS → fichier audio] → [LAM → animation 3D/vidéo] → [Affichage canvas]\n```\n\nLes vrais points de friction à vérifier :\n1. **Latence** — LAM est un modèle lourd (GPU requis), ça va être offline/asynchrone, pas du temps réel\n2. **Format de sortie** — si c'est un `.glb`/`.obj` animé, on peut le lire avec Three.js dans le canvas ; si c'est une vidéo MP4, encore plus simple\n3. **API Python** — s'il y a une API, on branche ça comme un nouvel adaptateur dans `avatar.py`\n\nJe récupère les détails précis dès que l'agent revient. Une chose est sûre : LAM s'intègrerait comme un **renderer d'avatar optionnel**, en parallèle de l'ASCII existant, sans casser l'architecture."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3701,"cache_read_input_tokens":29018,"output_tokens":784,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3701,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":784,"cache_read_input_tokens":29018,"cache_creation_input_tokens":3701,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3701},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5haP2czekNggWuXnxtM","type":"assistant","uuid":"34cd98d3-93a1-4351-be2f-66d005eca9ed","timestamp":"2026-06-15T20:46:48.329Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"type":"queue-operation","operation":"enqueue","timestamp":"2026-06-15T20:47:24.454Z","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","content":"<task-notification>\n<task-id>ac89a0f264ece3ae0</task-id>\n<tool-use-id>toolu_019HqPzuqEXi1FDySjwe4Bvh</tool-use-id>\n<output-file>/tmp/claude-1000/-home-wildlama/7de9b929-9050-430b-9b36-f74849c1c0a7/tasks/ac89a0f264ece3ae0.output</output-file>\n<status>completed</status>\n<summary>Agent \"Research LAM GitHub repo capabilities\" completed</summary>\n<result>Here is a concise integration-focused breakdown of LAM:\n\n---\n\n**1. What LAM Actually Does**\n\nLAM stands for **Large Avatar Model** (not \"Animation Model\"). It reconstructs a **photorealistic, animatable 3D Gaussian Splatting head avatar from a single input photo in ~1.4 seconds**. A companion model called **Audio2Expression** (released April 2025) then drives that avatar with audio input to produce lip-synced facial animation. The project also integrates with **OpenAvatarChat** for real-time AI chatting avatar use cases.\n\n---\n\n**2. Inputs**\n\n- **Avatar creation**: one static photograph (single image)\n- **Animation**: a motion sequence file (passed via `${MOTION_SEQ}` argument) — likely facial landmark / expression data\n- **Audio-driven animation**: the Audio2Expression module takes audio input (format not specified in README; likely WAV/MP3) and drives the avatar's face\n\nThere is no text-to-avatar or video input mentioned.\n\n---\n\n**3. Outputs**\n\n- **3D Gaussian Splatting representation** (not a traditional mesh like GLB/FBX/OBJ — this is a point-cloud-like volumetric format)\n- **Real-time rendering**: 562.9 FPS on A100 GPU, 110+ FPS on a mobile phone (Xiaomi 14)\n- **ZIP export** compatible with OpenAvatarChat for deployment in interactive chat apps\n- No traditional video file output mentioned; the pipeline is designed for real-time streaming/rendering\n\n---\n\n**4. System Requirements**\n\n- **GPU**: NVIDIA GPU required; tested on A100. CUDA 12.1 or 11.8 (Linux scripts provided); CUDA 12.8 one-click Windows installer also available\n- **Python**: Not pinned in README; `requirements.txt` exists in the repo\n- **VRAM/RAM**: Not documented explicitly\n- **Platforms**: Linux (primary), Windows (one-click installer), mobile rendering supported via a separate cross-platform path\n\n---\n\n**5. Python API / Web Demo**\n\n- **No clean Python import API** — usage is CLI (`sh ./scripts/inference.sh`) or Gradio web UI (`python app_lam.py`)\n- **Gradio local GUI**: `python app_lam.py`\n- **Hosted demos**: HuggingFace Space at `spaces/3DAIGC/LAM`, ModelScope studio, and OpenAvatarChat HuggingFace/ModelScope spaces\n\n---\n\n**6. Integration into a Web/Desktop GUI App**\n\nFeasible but with caveats:\n\n- The Gradio app (`app_lam.py`) is the intended local GUI — you could wrap or extend it\n- For deeper integration, you'd need to call the inference script or reverse-engineer it into importable Python functions, since there's no published SDK\n- The Gaussian Splatting output format is **not standard 3D** (no GLB/FBX), so rendering in a typical WebGL/three.js or Unity app requires a Gaussian Splatting renderer\n- Real-time performance is excellent once the avatar is built, making it viable for live chat/streaming apps via OpenAvatarChat\n- A desktop app wrapper around the Gradio interface (e.g., Electron + localhost) is the path of least resistance\n\n---\n\n**7. License**\n\n**Apache License 2.0** — permissive, allows commercial use with attribution. No model weight license restrictions called out in the README beyond this.\n\n---\n\n**Integration Feasibility Summary**\n\nLAM is feasible to integrate into a chatbot/avatar application, especially via the OpenAvatarChat pipeline it was designed for. The main friction points are: (1) no clean Python API — you'd work at the script/CLI level or fork the Gradio app; (2) the Gaussian Splatting output requires a specialized renderer, not a standard 3D engine; (3) hardware requirements are non-trivial (CUDA GPU needed for inference, though rendering works on mobile). If your goal is a talking-head AI avatar app, the Audio2Expression + LAM + OpenAvatarChat stack is the intended path.</result>\n<usage><subagent_tokens>15372</subagent_tokens><tool_uses>12</tool_uses><duration_ms>197795</duration_ms></usage>\n</task-notification>"}
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{"parentUuid":"be914edd-feff-4f39-9994-30fd0ede262b","isSidechain":false,"promptId":"2f314e39-39ff-453a-bce2-4cace41848d6","type":"user","message":{"role":"user","content":"<task-notification>\n<task-id>ac89a0f264ece3ae0</task-id>\n<tool-use-id>toolu_019HqPzuqEXi1FDySjwe4Bvh</tool-use-id>\n<output-file>/tmp/claude-1000/-home-wildlama/7de9b929-9050-430b-9b36-f74849c1c0a7/tasks/ac89a0f264ece3ae0.output</output-file>\n<status>completed</status>\n<summary>Agent \"Research LAM GitHub repo capabilities\" completed</summary>\n<result>Here is a concise integration-focused breakdown of LAM:\n\n---\n\n**1. What LAM Actually Does**\n\nLAM stands for **Large Avatar Model** (not \"Animation Model\"). It reconstructs a **photorealistic, animatable 3D Gaussian Splatting head avatar from a single input photo in ~1.4 seconds**. A companion model called **Audio2Expression** (released April 2025) then drives that avatar with audio input to produce lip-synced facial animation. The project also integrates with **OpenAvatarChat** for real-time AI chatting avatar use cases.\n\n---\n\n**2. Inputs**\n\n- **Avatar creation**: one static photograph (single image)\n- **Animation**: a motion sequence file (passed via `${MOTION_SEQ}` argument) — likely facial landmark / expression data\n- **Audio-driven animation**: the Audio2Expression module takes audio input (format not specified in README; likely WAV/MP3) and drives the avatar's face\n\nThere is no text-to-avatar or video input mentioned.\n\n---\n\n**3. Outputs**\n\n- **3D Gaussian Splatting representation** (not a traditional mesh like GLB/FBX/OBJ — this is a point-cloud-like volumetric format)\n- **Real-time rendering**: 562.9 FPS on A100 GPU, 110+ FPS on a mobile phone (Xiaomi 14)\n- **ZIP export** compatible with OpenAvatarChat for deployment in interactive chat apps\n- No traditional video file output mentioned; the pipeline is designed for real-time streaming/rendering\n\n---\n\n**4. System Requirements**\n\n- **GPU**: NVIDIA GPU required; tested on A100. CUDA 12.1 or 11.8 (Linux scripts provided); CUDA 12.8 one-click Windows installer also available\n- **Python**: Not pinned in README; `requirements.txt` exists in the repo\n- **VRAM/RAM**: Not documented explicitly\n- **Platforms**: Linux (primary), Windows (one-click installer), mobile rendering supported via a separate cross-platform path\n\n---\n\n**5. Python API / Web Demo**\n\n- **No clean Python import API** — usage is CLI (`sh ./scripts/inference.sh`) or Gradio web UI (`python app_lam.py`)\n- **Gradio local GUI**: `python app_lam.py`\n- **Hosted demos**: HuggingFace Space at `spaces/3DAIGC/LAM`, ModelScope studio, and OpenAvatarChat HuggingFace/ModelScope spaces\n\n---\n\n**6. Integration into a Web/Desktop GUI App**\n\nFeasible but with caveats:\n\n- The Gradio app (`app_lam.py`) is the intended local GUI — you could wrap or extend it\n- For deeper integration, you'd need to call the inference script or reverse-engineer it into importable Python functions, since there's no published SDK\n- The Gaussian Splatting output format is **not standard 3D** (no GLB/FBX), so rendering in a typical WebGL/three.js or Unity app requires a Gaussian Splatting renderer\n- Real-time performance is excellent once the avatar is built, making it viable for live chat/streaming apps via OpenAvatarChat\n- A desktop app wrapper around the Gradio interface (e.g., Electron + localhost) is the path of least resistance\n\n---\n\n**7. License**\n\n**Apache License 2.0** — permissive, allows commercial use with attribution. No model weight license restrictions called out in the README beyond this.\n\n---\n\n**Integration Feasibility Summary**\n\nLAM is feasible to integrate into a chatbot/avatar application, especially via the OpenAvatarChat pipeline it was designed for. The main friction points are: (1) no clean Python API — you'd work at the script/CLI level or fork the Gradio app; (2) the Gaussian Splatting output requires a specialized renderer, not a standard 3D engine; (3) hardware requirements are non-trivial (CUDA GPU needed for inference, though rendering works on mobile). If your goal is a talking-head AI avatar app, the Audio2Expression + LAM + OpenAvatarChat stack is the intended path.</result>\n<usage><subagent_tokens>15372</subagent_tokens><tool_uses>12</tool_uses><duration_ms>197795</duration_ms></usage>\n</task-notification>"},"uuid":"e1cdf24a-9570-4ed8-a98c-72c17bb54791","timestamp":"2026-06-15T20:47:24.471Z","permissionMode":"default","origin":{"kind":"task-notification"},"promptSource":"system","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"/mnt/DATA/WORK/agent-gui ici je build une GUI pour mes agent IA, pour qu'ils aient un avatar et que je puisse les modeler comme je veux. est ce qu'on pourrait mettre ça dessus? comme ça les avatars po…","leafUuid":"e1cdf24a-9570-4ed8-a98c-72c17bb54791","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7"}
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{"parentUuid":"a7100a5c-f4c8-413d-b6a4-f4b29d3e5515","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01X44ajjYW6VCkZrwr6qpxx4","type":"message","role":"assistant","content":[{"type":"text","text":"Voici le bilan complet.\n\n---\n\n## LAM — ce que c'est vraiment\n\n**Large Avatar Model** (pas Animation) : génère un avatar 3D photoréaliste depuis **une seule photo** en ~1.4 secondes. Le module compagnon **Audio2Expression** anime ensuite cet avatar avec de l'audio pour un lip-sync en temps réel.\n\n### Points clés pour ton projet\n\n| Critère | Réalité |\n|---|---|\n| **Output** | 3D Gaussian Splatting — **pas** du GLB/FBX standard |\n| **Rendu** | 562 FPS sur A100, 110+ FPS sur mobile — très rapide une fois l'avatar généré |\n| **API Python** | Aucune propre — CLI (`inference.sh`) ou Gradio local (`app_lam.py`) |\n| **GPU requis** | CUDA obligatoire (testé A100) |\n| **Licence** | Apache 2.0 — libre |\n| **Intégration prévue** | OpenAvatarChat — exactement un use-case \"avatar qui parle dans une app de chat\" |\n\n---\n\n## Faisabilité sur ton agent-gui\n\n**Oui, c'est intégrable** — mais il y a deux frictions réelles :\n\n**1. Le format Gaussian Splatting n'est pas du WebGL classique**\nThree.js ne lit pas ça nativement. Il faut une lib JS dédiée (ex: [gsplat.js](https://github.com/antimatter15/splat)) dans ton canvas. C'est faisable mais non-trivial.\n\n**2. Pas d'API Python propre**\nIl faut soit appeler les scripts CLI en subprocess, soit forker le code Gradio pour l'importer comme module.\n\n---\n\n## Architecture d'intégration recommandée\n\n```\n[Agent répond] → [TTS → WAV] → [Audio2Expression] → [rendu Gaussian Splatting]\n                                       ↓\n                              canvas HTML (via gsplat.js)\n```\n\n**La voie la plus rapide** : lancer `app_lam.py` (Gradio) en subprocess et l'afficher dans un `<iframe>` dans la sidebar — pas élégant mais opérationnel rapidement.\n\n**La voie propre** : remplacer ton canvas pixel-art par un renderer Gaussian Splatting, et brancher Audio2Expression via un endpoint Python local ajouté à `app.py`.\n\n---\n\n## Ma recommandation\n\nAvant de toucher au code, deux questions pratiques :\n1. **Tu as un GPU NVIDIA avec CUDA ?** Sans ça, LAM ne tourne pas du tout.\n2. **Tu veux du live (avatar qui parle en temps réel pendant que l'agent répond) ou du pré-rendu (vidéo générée après la réponse) ?**\n\nSi tu as le GPU et que tu veux du temps réel, **OpenAvatarChat** est le chemin directement prévu pour ce cas — on pourrait l'embarquer ou s'y connecter depuis ton GUI. Si tu veux rester léger, on peut d'abord ajouter **TTS + lèvres animées 2D** (bien plus simple) comme étape intermédiaire vers LAM."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1969,"cache_read_input_tokens":32719,"output_tokens":1302,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1969,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":3,"output_tokens":1302,"cache_read_input_tokens":32719,"cache_creation_input_tokens":1969,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1969},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5heMJYcLeMPfLtkENJH","type":"assistant","uuid":"db32b1bf-df19-4f43-82b5-e4c28b8a9f6d","timestamp":"2026-06-15T20:47:51.855Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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ECC |\n| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n|                                         |                        |               MIG M. |\n|=========================================+========================+======================|\n|   0  NVIDIA GeForce RTX 5090        Off |   00000000:01:00.0  On |                  N/A |\n|  0%   51C    P8             38W /  575W |     822MiB /  32607MiB |      0%      Default |\n|                                         |                        |                  N/A |\n+-----------------------------------------+------------------------+----------------------+\n\n+-----------------------------------------------------------------------------------------+\n| Processes:                                                                              |\n|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n|        ID   ID                                                               Usage      |\n|=========================================================================================|\n|    0   N/A  N/A            7078      G   /usr/bin/gnome-shell                    211MiB |\n|    0   N/A  N/A            7421      G   /usr/bin/Xwayland                        12MiB |\n|    0   N/A  N/A            8760    C+G   /usr/bin/nautilus                        74MiB |\n|    0   N/A  N/A            8851    C+G   /usr/bin/ptyxis                          52MiB |\n|    0   N/A  N/A           10162    C+G   ...rack-uuid=3190708988185955192        284MiB |\n+-----------------------------------------------------------------------------------------+\n---\n/bin/bash: line 1: nvcc: command not found\n---\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import torch; print('PyTorch:', torch.__version__); print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'N/A')\n    ^^^^^^^^^^^^\nModuleNotFoundError: No module named 'torch'","is_error":true,"tool_use_id":"toolu_01JZuCQ65yKUrnkGauoWqZQ8"}]},"uuid":"a734fa01-3598-4760-894a-c9523eade695","timestamp":"2026-06-15T20:48:54.828Z","toolUseResult":"Error: Exit code 1\nMon Jun 15 22:48:54 2026       \n+-----------------------------------------------------------------------------------------+\n| NVIDIA-SMI 595.71.05              Driver Version: 595.71.05      CUDA Version: 13.2     |\n+-----------------------------------------+------------------------+----------------------+\n| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n|                                         |                        |               MIG M. |\n|=========================================+========================+======================|\n|   0  NVIDIA GeForce RTX 5090        Off |   00000000:01:00.0  On |                  N/A |\n|  0%   51C    P8             38W /  575W |     822MiB /  32607MiB |      0%      Default |\n|                                         |                        |                  N/A |\n+-----------------------------------------+------------------------+----------------------+\n\n+-----------------------------------------------------------------------------------------+\n| Processes:                                                                              |\n|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n|        ID   ID                                                               Usage      |\n|=========================================================================================|\n|    0   N/A  N/A            7078      G   /usr/bin/gnome-shell                    211MiB |\n|    0   N/A  N/A            7421      G   /usr/bin/Xwayland                        12MiB |\n|    0   N/A  N/A            8760    C+G   /usr/bin/nautilus                        74MiB |\n|    0   N/A  N/A            8851    C+G   /usr/bin/ptyxis                          52MiB |\n|    0   N/A  N/A           10162    C+G   ...rack-uuid=3190708988185955192        284MiB |\n+-----------------------------------------------------------------------------------------+\n---\n/bin/bash: line 1: nvcc: command not found\n---\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import torch; print('PyTorch:', torch.__version__); print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'N/A')\n    ^^^^^^^^^^^^\nModuleNotFoundError: No module named 'torch'","sourceToolAssistantUUID":"cd548924-4b87-42f1-a803-693f97a0b600","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"1f92257c-ddfe-4350-afe7-4b40ba52f1ec","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01RRLxxgFGc1Mdj4Q1buFbFw","type":"message","role":"assistant","content":[{"type":"text","text":"Voilà le tableau complet :\n\n| Élément | État |\n|---|---|\n| GPU | RTX 5090, 32 Go VRAM ✅ |\n| Drivers NVIDIA | 595.71 ✅ |\n| CUDA Toolkit (`nvcc`) | Absent ❌ |\n| Python | 3.14 (trop récent pour PyTorch) ❌ |\n| pip | Absent ❌ |\n| conda | Absent ❌ |\n\n**Le vrai problème** : Python 3.14 n'est pas encore supporté par PyTorch ou LAM. Il faut un env isolé Python 3.11. La voie la plus propre est **Miniconda**.\n\nVoici le plan d'installation complet, étape par étape. Chaque bloc `!commande` tu peux le taper directement dans ce chat.\n\n---\n\n## Étape 1 — Installer Miniconda\n\n```bash\n! wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda.sh && bash /tmp/miniconda.sh -b -p $HOME/miniconda3\n```\n\nPuis active conda pour ta session :\n\n```bash\n! $HOME/miniconda3/bin/conda init bash && source ~/.bashrc\n```\n\n---\n\n## Étape 2 — Créer un env Python 3.11 avec CUDA\n\n```bash\n! $HOME/miniconda3/bin/conda create -n lam python=3.11 -y\n```\n\n---\n\n## Étape 3 — Installer CUDA Toolkit 12.8\n\nPour le RTX 5090 (architecture Blackwell), il faut CUDA 12.8 minimum :\n\n```bash\n! $HOME/miniconda3/bin/conda run -n lam conda install -c nvidia cuda-toolkit=12.8 -y\n```\n\n---\n\n## Étape 4 — Installer PyTorch 2.7+ (support Blackwell)\n\n```bash\n! $HOME/miniconda3/bin/conda run -n lam pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128\n```\n\nVérifie que ça marche :\n\n```bash\n! $HOME/miniconda3/bin/conda run -n lam python -c \"import torch; print(torch.__version__); print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))\"\n```\n\n---\n\nUne fois PyTorch confirmé, on clône LAM + OpenAvatarChat et on intègre le stream dans ton agent-gui. Lance l'étape 1 et dis-moi ce que tu obtiens."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":606,"cache_read_input_tokens":37917,"output_tokens":1157,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":606,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":1157,"cache_read_input_tokens":37917,"cache_creation_input_tokens":606,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":606},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5hogcQWoXte59SmbwcC","type":"assistant","uuid":"e38478b2-fca8-4ece-9b09-5b552b0c1a33","timestamp":"2026-06-15T20:49:49.850Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"873f998e-e41a-4c94-a9b9-37e3a64425d4","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_016hPiAGDZjoZWeykDB5vxvx","type":"message","role":"assistant","content":[{"type":"text","text":"Le fichier est déjà téléchargé dans `/tmp`. Il faut juste lancer l'installeur. Tape ça dans ton terminal :\n\n```bash\nbash /tmp/miniconda.sh -b -p $HOME/miniconda3 && $HOME/miniconda3/bin/conda init bash && source ~/.bashrc\n```\n\nLe `-b` = mode silencieux sans questions. Si ça plante, colle-moi l'erreur complète."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":364,"cache_read_input_tokens":39820,"output_tokens":160,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":364,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":160,"cache_read_input_tokens":39820,"cache_creation_input_tokens":364,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":364},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5i8qTVNadaQUUbpECRM","type":"assistant","uuid":"d62ff7c1-c8bc-49a8-86cb-9b64efb013b9","timestamp":"2026-06-15T20:53:55.656Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
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{"parentUuid":"b7892525-48b3-449e-986c-2280afc5e8a6","isSidechain":false,"type":"system","subtype":"away_summary","content":"Installing Miniconda to get Python 3.11 and PyTorch working for LAM (talking 3D avatar on RTX 5090). Run: `bash /tmp/miniconda.sh -b -p $HOME/miniconda3 && $HOME/miniconda3/bin/conda init bash && source ~/.bashrc` (disable recaps in /config)","timestamp":"2026-06-15T20:57:28.353Z","uuid":"cc16841c-6c22-4faf-bea1-e7f9cc51ced2","isMeta":false,"userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"type":"file-history-snapshot","messageId":"dd69c219-c971-4bc2-abac-5d4c9e2699d6","snapshot":{"messageId":"dd69c219-c971-4bc2-abac-5d4c9e2699d6","trackedFileBackups":{},"timestamp":"2026-06-15T20:57:44.007Z"},"isSnapshotUpdate":false}
{"parentUuid":"cc16841c-6c22-4faf-bea1-e7f9cc51ced2","isSidechain":false,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","type":"user","message":{"role":"user","content":"(base) wildlama@wildlama-MS-7E07:~$ $HOME/miniconda3/bin/conda run -n lam python -c \"import torch; print(torch.__version__); print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))\"\n2.11.0+cu128\nTrue\nNVIDIA GeForce RTX 5090\n(base) wildlama@wildlama-MS-7E07:~$ \n"},"uuid":"dd69c219-c971-4bc2-abac-5d4c9e2699d6","timestamp":"2026-06-15T20:57:44.006Z","permissionMode":"default","promptSource":"typed","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"dd69c219-c971-4bc2-abac-5d4c9e2699d6","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01L4TjikB32dbSVj4VWnirV8","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":122,"cache_read_input_tokens":40342,"output_tokens":638,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":122,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":638,"cache_read_input_tokens":40342,"cache_creation_input_tokens":122,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":122},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5iS26vasiXG3aNki9zA","type":"assistant","uuid":"a6f8ebea-f800-4228-87e8-90d7e9d19837","timestamp":"2026-06-15T20:57:48.350Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"a6f8ebea-f800-4228-87e8-90d7e9d19837","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01L4TjikB32dbSVj4VWnirV8","type":"message","role":"assistant","content":[{"type":"text","text":"PyTorch 2.11 + CUDA 12.8 + RTX 5090 — tout fonctionne. On peut attaquer LAM.\n\nClône le repo et installe les dépendances :\n\n```bash\ncd /mnt/DATA/WORK && git clone https://github.com/aigc3d/LAM.git && cd LAM && $HOME/miniconda3/bin/conda run -n lam pip install -r requirements.txt\n```\n\nÇa va prendre quelques minutes. Lance ça et colle-moi la fin de l'output (surtout les erreurs éventuelles).\n\nEn parallèle, je regarde la structure du repo pour préparer l'intégration avec ton agent-gui."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":122,"cache_read_input_tokens":40342,"output_tokens":638,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":122,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":638,"cache_read_input_tokens":40342,"cache_creation_input_tokens":122,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":122},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5iS26vasiXG3aNki9zA","type":"assistant","uuid":"8013f53b-88b9-4ffd-ae59-85d64ec1a522","timestamp":"2026-06-15T20:57:51.402Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"8013f53b-88b9-4ffd-ae59-85d64ec1a522","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01L4TjikB32dbSVj4VWnirV8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012cr9yeTkvK1u9GwmRgvtBW","name":"Agent","input":{"description":"Research LAM repo structure and OpenAvatarChat integration","prompt":"I need to understand the LAM repository (https://github.com/aigc3d/LAM) and OpenAvatarChat integration for a real-time talking avatar setup. Please fetch and read:\n\n1. The LAM repo README (already known: uses Gaussian Splatting, has app_lam.py Gradio app, Audio2Expression module)\n2. Look for the OpenAvatarChat repo at https://github.com/aigc3d/OpenAvatarChat - read its README carefully\n3. Understand the pipeline: how does audio go in, how does the rendered output come out (WebSocket? HTTP stream? Video frames?)\n4. Look for any Python API or importable functions (not just CLI scripts)\n5. Look for the Audio2Expression module specifically - what does it accept as input format?\n\nI want to integrate this into a local web app (Python backend + vanilla JS frontend). The goal is:\n- User sends text to AI agent → agent responds → TTS generates audio → LAM animates avatar in real-time → rendered in browser canvas\n\nReport:\n- The exact pipeline for real-time audio-driven avatar\n- How OpenAvatarChat handles the browser rendering (WebRTC? WebSocket? iframe?)\n- Whether there's a Python callable API for inference\n- Key files/classes to look at for integration"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":122,"cache_read_input_tokens":40342,"output_tokens":638,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":122,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":638,"cache_read_input_tokens":40342,"cache_creation_input_tokens":122,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":122},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5iS26vasiXG3aNki9zA","type":"assistant","uuid":"de1b196f-e2b0-4f23-8331-169101b11912","timestamp":"2026-06-15T20:57:58.657Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"de1b196f-e2b0-4f23-8331-169101b11912","isSidechain":false,"type":"system","subtype":"informational","content":"Auto mode lets Claude handle permission prompts automatically — Claude checks each tool call for risky actions and prompt injection before executing. Actions Claude identifies as safe are executed, while actions Claude identifies as risky are blocked and Claude may try a different approach. Ideal for long-running tasks. Sessions are slightly more expensive. Claude can make mistakes that allow harmful commands to run, it's recommended to only use in isolated environments. Shift+Tab to change mode.","isMeta":false,"timestamp":"2026-06-15T21:03:15.044Z","uuid":"652b3943-16c4-43e1-8ddb-5d5319cffeb8","level":"notice","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD"}
{"parentUuid":"de1b196f-e2b0-4f23-8331-169101b11912","isSidechain":false,"promptId":"6258cf10-d6f1-42e0-aae1-f959f6bc9725","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012cr9yeTkvK1u9GwmRgvtBW","type":"tool_result","content":[{"type":"text","text":"I now have all the information needed for a comprehensive answer. Let me compile the full picture.\n\nHere is a comprehensive technical report on the LAM + OpenAvatarChat pipeline for real-time talking avatar integration.\n\n---\n\n## Pipeline Architecture Overview\n\nThe full stack has three distinct layers: **Audio2Expression** (Python, server-side), **LAM Gaussian Splatting Renderer** (either Python server-side render *or* WebGL client-side), and a **WebRTC/WebSocket frontend**.\n\n---\n\n## 1. Real-Time Audio-Driven Avatar Pipeline\n\nThe end-to-end flow in OpenAvatarChat (`HumanAIGC-Engineering/OpenAvatarChat`, not `aigc3d/OpenAvatarChat`) is:\n\n```\nBrowser audio (mic) \n  → WebRTC → Python server\n    → VAD (Silero) → ASR (SenseVoice) → LLM → TTS (CosyVoice)\n      → Audio bytes → LAM Audio2Expression → ARKit blendshapes [52 floats/frame @ 30fps]\n        → WebSocket to browser → gaussian-splat-renderer-for-lam (WebGL)\n          → Canvas render\n```\n\nFor your TTS→Avatar use case, the VAD/ASR/LLM modules are bypassed, and TTS audio feeds directly into Audio2Expression.\n\n---\n\n## 2. Audio2Expression Module (`aigc3d/LAM_Audio2Expression`)\n\n**Key repo:** `https://github.com/aigc3d/LAM_Audio2Expression`\n\n**Input format:** Raw NumPy float32 audio at **16 kHz mono**, fed in 1-second chunks (16,000 samples per call).\n\n**Python callable API:**\n```python\nfrom engines.defaults import default_argument_parser, default_config_parser, default_setup\nfrom engines.infer import INFER\n\ncfg = default_config_parser('configs/lam_audio2exp_config_streaming.py', options)\ncfg = default_setup(cfg)\ninfer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\ninfer.model.eval()\n\ncontext = None\n# For each 1-second audio chunk:\noutput, context = infer.infer_streaming_audio(audio_chunk_np, sample_rate=16000, context)\n# output['expression'] → np.ndarray shape [N_frames, 52]  (30fps ARKit blendshapes)\n```\n\n**Streaming context structure:**\n```python\ncontext = {\n    'is_initial_input': bool,\n    'previous_audio': np.ndarray | None,\n    'previous_expression': np.ndarray | None,\n    'previous_volume': np.ndarray | None,\n    'first_input_flag': bool\n}\n```\n\n**Output:** `[N_frames, 52]` NumPy array. Each frame = 52 ARKit blendshape weights (0.0–1.0). The 52 names are the standard Apple ARKit face channels: `browDownLeft`, `browDownRight`, `browInnerUp`, `cheekPuff`, `eyeBlinkLeft`, `jawOpen`, `mouthSmileLeft`, `tongueOut`, etc.\n\n**Required models:** `wav2vec2-base-960h` (audio encoder) + `LAM_audio2exp` weights (as `LAM_audio2exp.tar` — **note: as of early 2026 this tarball is not yet publicly released**, per open issue #91 in the LAM repo).\n\n---\n\n## 3. Browser Rendering: WebGL + WebSocket (not WebRTC for avatar frames)\n\n**Key repo:** `https://github.com/aigc3d/LAM_WebRender`\n\nThe browser rendering is done via the NPM package `gaussian-splat-renderer-for-lam`. It is **WebGL-based Gaussian Splatting**, not video streaming. The avatar geometry (Gaussian Splat model) is loaded once as a ZIP file; only the expression data streams per frame.\n\n**JavaScript API:**\n```typescript\nimport * as GaussianSplats3D from \"gaussian-splat-renderer-for-lam\";\n\nconst renderer = await GaussianSplats3D.GaussianSplatRenderer.getInstance(\n  divElement,          // HTMLDivElement canvas container\n  \"./avatar.zip\",      // Pre-exported LAM avatar (GLB+Gaussian data)\n  {\n    getChatState: () => \"Responding\",  // \"Idle\"|\"Listening\"|\"Thinking\"|\"Responding\"\n    getExpressionData: () => ({        // Called every frame; return dict of 52 blendshape name→value\n      jawOpen: 0.4,\n      mouthSmileLeft: 0.2,\n      // ...all 52 ARKit channels\n    }),\n    backgroundColor: \"0x000000\",\n    alpha: 0.0\n  }\n);\n```\n\nThe renderer **polls `getExpressionData()` each animation frame** — there is no WebSocket push from server to renderer. This means your Python backend feeds blendshapes to a JS bridge (your own WebSocket), and the JS side passes them into `getExpressionData()` on each rAF tick.\n\n**Avatar asset format:** A ZIP file containing the LAM-exported GLB (Gaussian Splat geometry baked from a source photo). Generated via `app_lam.py --blender_path /path/to/blender` with the \"Export ZIP for Chatting Avatar\" checkbox enabled. Requires Blender 4.0+ and the FBX SDK.\n\n---\n\n## 4. OpenAvatarChat WebRTC: for Audio I/O Only\n\n**The WebRTC layer** (`gradio-webrtc`) is used *only for browser microphone audio input and TTS audio output back to the browser* — not for streaming avatar frames. The avatar render happens entirely in WebGL client-side. The LAM client handler is a **WebSocket** (`ws_lam_client_handler`) that delivers blendshape data from Python to the browser's JavaScript renderer.\n\n---\n\n## 5. LAM Core Model: Python-Importable Inference\n\nThe LAM 3D Gaussian model itself (`lam/models/modeling_lam.py`) is callable programmatically:\n\n```python\nfrom lam.runners.infer.lam import LAMInferrer\n\ninferrer = LAMInferrer()\n# Takes: source image + pre-computed FLAME params (from motion sequences)\n# Returns: rendered RGB frames [Nv, H, W, 3]\n```\n\n**FLAME parameter keys** (`gs_renderer.py`):\n- `expr`: [B, Nv, 100] — expression coefficients  \n- `betas`: [B, 100] — shape (constant per identity)  \n- `rotation`, `neck_pose`, `jaw_pose`, `eyes_pose`, `translation` — pose per frame  \n- `teeth_bs` (optional) — teeth blendshapes\n\nHowever, for your use case you **do not need to call the LAM Gaussian renderer in Python at all** — the WebGL renderer does the rendering client-side from the pre-exported ZIP. The Python side only needs Audio2Expression.\n\n---\n\n## 6. Key Files to Study for Integration\n\n| File | Repo | Purpose |\n|---|---|---|\n| `engines/infer.py` | `aigc3d/LAM_Audio2Expression` | `infer_streaming_audio()` — core callable API |\n| `models/network.py` | `aigc3d/LAM_Audio2Expression` | `Audio2Expression` model (Wav2Vec2 → 52 blendshapes) |\n| `models/utils.py` | `aigc3d/LAM_Audio2Expression` | `ARKitBlendShape` list + `export_blendshape_animation()` |\n| `inference_streaming_audio.py` | `aigc3d/LAM_Audio2Expression` | Reference streaming usage |\n| `src/gaussianAvatar.ts` | `aigc3d/LAM_WebRender` | JS renderer API and expression data protocol |\n| `handlers/avatar/lam/avatar_handler_lam_audio2expression.py` | `HumanAIGC-Engineering/OpenAvatarChat` | How audio bytes → blendshapes → WebSocket works in production |\n| `config/chat_with_lam.yaml` | `HumanAIGC-Engineering/OpenAvatarChat` | Full handler stack configuration |\n| `app_lam.py` | `aigc3d/LAM` | Avatar export (ZIP) with Blender |\n\n---\n\n## 7. Integration Plan for Your Use Case\n\nYour pipeline (TTS audio → animated avatar in browser canvas):\n\n1. **Python backend**: Load `Audio2ExpressionInfer` once at startup. For each TTS response, convert audio to 16kHz numpy, call `infer_streaming_audio()` in 1-second chunks, buffer output blendshape frames.\n2. **WebSocket**: Stream blendshape frames as JSON `{jawOpen: 0.4, eyeBlinkLeft: 0.1, ...}` to the browser at 30fps.\n3. **Browser JS**: Load `gaussian-splat-renderer-for-lam` with the pre-exported avatar ZIP. Wire `getExpressionData()` to read from the WebSocket buffer. The renderer calls this each animation frame and deforms the Gaussian Splat mesh live in WebGL.\n\n**Critical blocker to verify:** The `LAM_audio2exp` model weights (`LAM_audio2exp.tar`) appear to not be publicly available as of February 2026 (GitHub issue #91 in `aigc3d/LAM` is open and unanswered). You may need to contact the maintainers directly or monitor the repo for a release."},{"type":"text","text":"agentId: a8905ecd71f2dd288 (use SendMessage with to: 'a8905ecd71f2dd288' to continue this agent)\n<usage>subagent_tokens: 72637\ntool_uses: 142\nduration_ms: 759572</usage>"}]}]},"uuid":"b5266ed6-282c-4a5f-9095-1db30be92073","timestamp":"2026-06-15T21:10:38.236Z","toolUseResult":{"status":"completed","prompt":"I need to understand the LAM repository (https://github.com/aigc3d/LAM) and OpenAvatarChat integration for a real-time talking avatar setup. Please fetch and read:\n\n1. The LAM repo README (already known: uses Gaussian Splatting, has app_lam.py Gradio app, Audio2Expression module)\n2. Look for the OpenAvatarChat repo at https://github.com/aigc3d/OpenAvatarChat - read its README carefully\n3. Understand the pipeline: how does audio go in, how does the rendered output come out (WebSocket? HTTP stream? Video frames?)\n4. Look for any Python API or importable functions (not just CLI scripts)\n5. Look for the Audio2Expression module specifically - what does it accept as input format?\n\nI want to integrate this into a local web app (Python backend + vanilla JS frontend). The goal is:\n- User sends text to AI agent → agent responds → TTS generates audio → LAM animates avatar in real-time → rendered in browser canvas\n\nReport:\n- The exact pipeline for real-time audio-driven avatar\n- How OpenAvatarChat handles the browser rendering (WebRTC? WebSocket? iframe?)\n- Whether there's a Python callable API for inference\n- Key files/classes to look at for integration","agentId":"a8905ecd71f2dd288","agentType":"general-purpose","content":[{"type":"text","text":"I now have all the information needed for a comprehensive answer. Let me compile the full picture.\n\nHere is a comprehensive technical report on the LAM + OpenAvatarChat pipeline for real-time talking avatar integration.\n\n---\n\n## Pipeline Architecture Overview\n\nThe full stack has three distinct layers: **Audio2Expression** (Python, server-side), **LAM Gaussian Splatting Renderer** (either Python server-side render *or* WebGL client-side), and a **WebRTC/WebSocket frontend**.\n\n---\n\n## 1. Real-Time Audio-Driven Avatar Pipeline\n\nThe end-to-end flow in OpenAvatarChat (`HumanAIGC-Engineering/OpenAvatarChat`, not `aigc3d/OpenAvatarChat`) is:\n\n```\nBrowser audio (mic) \n  → WebRTC → Python server\n    → VAD (Silero) → ASR (SenseVoice) → LLM → TTS (CosyVoice)\n      → Audio bytes → LAM Audio2Expression → ARKit blendshapes [52 floats/frame @ 30fps]\n        → WebSocket to browser → gaussian-splat-renderer-for-lam (WebGL)\n          → Canvas render\n```\n\nFor your TTS→Avatar use case, the VAD/ASR/LLM modules are bypassed, and TTS audio feeds directly into Audio2Expression.\n\n---\n\n## 2. Audio2Expression Module (`aigc3d/LAM_Audio2Expression`)\n\n**Key repo:** `https://github.com/aigc3d/LAM_Audio2Expression`\n\n**Input format:** Raw NumPy float32 audio at **16 kHz mono**, fed in 1-second chunks (16,000 samples per call).\n\n**Python callable API:**\n```python\nfrom engines.defaults import default_argument_parser, default_config_parser, default_setup\nfrom engines.infer import INFER\n\ncfg = default_config_parser('configs/lam_audio2exp_config_streaming.py', options)\ncfg = default_setup(cfg)\ninfer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))\ninfer.model.eval()\n\ncontext = None\n# For each 1-second audio chunk:\noutput, context = infer.infer_streaming_audio(audio_chunk_np, sample_rate=16000, context)\n# output['expression'] → np.ndarray shape [N_frames, 52]  (30fps ARKit blendshapes)\n```\n\n**Streaming context structure:**\n```python\ncontext = {\n    'is_initial_input': bool,\n    'previous_audio': np.ndarray | None,\n    'previous_expression': np.ndarray | None,\n    'previous_volume': np.ndarray | None,\n    'first_input_flag': bool\n}\n```\n\n**Output:** `[N_frames, 52]` NumPy array. Each frame = 52 ARKit blendshape weights (0.0–1.0). The 52 names are the standard Apple ARKit face channels: `browDownLeft`, `browDownRight`, `browInnerUp`, `cheekPuff`, `eyeBlinkLeft`, `jawOpen`, `mouthSmileLeft`, `tongueOut`, etc.\n\n**Required models:** `wav2vec2-base-960h` (audio encoder) + `LAM_audio2exp` weights (as `LAM_audio2exp.tar` — **note: as of early 2026 this tarball is not yet publicly released**, per open issue #91 in the LAM repo).\n\n---\n\n## 3. Browser Rendering: WebGL + WebSocket (not WebRTC for avatar frames)\n\n**Key repo:** `https://github.com/aigc3d/LAM_WebRender`\n\nThe browser rendering is done via the NPM package `gaussian-splat-renderer-for-lam`. It is **WebGL-based Gaussian Splatting**, not video streaming. The avatar geometry (Gaussian Splat model) is loaded once as a ZIP file; only the expression data streams per frame.\n\n**JavaScript API:**\n```typescript\nimport * as GaussianSplats3D from \"gaussian-splat-renderer-for-lam\";\n\nconst renderer = await GaussianSplats3D.GaussianSplatRenderer.getInstance(\n  divElement,          // HTMLDivElement canvas container\n  \"./avatar.zip\",      // Pre-exported LAM avatar (GLB+Gaussian data)\n  {\n    getChatState: () => \"Responding\",  // \"Idle\"|\"Listening\"|\"Thinking\"|\"Responding\"\n    getExpressionData: () => ({        // Called every frame; return dict of 52 blendshape name→value\n      jawOpen: 0.4,\n      mouthSmileLeft: 0.2,\n      // ...all 52 ARKit channels\n    }),\n    backgroundColor: \"0x000000\",\n    alpha: 0.0\n  }\n);\n```\n\nThe renderer **polls `getExpressionData()` each animation frame** — there is no WebSocket push from server to renderer. This means your Python backend feeds blendshapes to a JS bridge (your own WebSocket), and the JS side passes them into `getExpressionData()` on each rAF tick.\n\n**Avatar asset format:** A ZIP file containing the LAM-exported GLB (Gaussian Splat geometry baked from a source photo). Generated via `app_lam.py --blender_path /path/to/blender` with the \"Export ZIP for Chatting Avatar\" checkbox enabled. Requires Blender 4.0+ and the FBX SDK.\n\n---\n\n## 4. OpenAvatarChat WebRTC: for Audio I/O Only\n\n**The WebRTC layer** (`gradio-webrtc`) is used *only for browser microphone audio input and TTS audio output back to the browser* — not for streaming avatar frames. The avatar render happens entirely in WebGL client-side. The LAM client handler is a **WebSocket** (`ws_lam_client_handler`) that delivers blendshape data from Python to the browser's JavaScript renderer.\n\n---\n\n## 5. LAM Core Model: Python-Importable Inference\n\nThe LAM 3D Gaussian model itself (`lam/models/modeling_lam.py`) is callable programmatically:\n\n```python\nfrom lam.runners.infer.lam import LAMInferrer\n\ninferrer = LAMInferrer()\n# Takes: source image + pre-computed FLAME params (from motion sequences)\n# Returns: rendered RGB frames [Nv, H, W, 3]\n```\n\n**FLAME parameter keys** (`gs_renderer.py`):\n- `expr`: [B, Nv, 100] — expression coefficients  \n- `betas`: [B, 100] — shape (constant per identity)  \n- `rotation`, `neck_pose`, `jaw_pose`, `eyes_pose`, `translation` — pose per frame  \n- `teeth_bs` (optional) — teeth blendshapes\n\nHowever, for your use case you **do not need to call the LAM Gaussian renderer in Python at all** — the WebGL renderer does the rendering client-side from the pre-exported ZIP. The Python side only needs Audio2Expression.\n\n---\n\n## 6. Key Files to Study for Integration\n\n| File | Repo | Purpose |\n|---|---|---|\n| `engines/infer.py` | `aigc3d/LAM_Audio2Expression` | `infer_streaming_audio()` — core callable API |\n| `models/network.py` | `aigc3d/LAM_Audio2Expression` | `Audio2Expression` model (Wav2Vec2 → 52 blendshapes) |\n| `models/utils.py` | `aigc3d/LAM_Audio2Expression` | `ARKitBlendShape` list + `export_blendshape_animation()` |\n| `inference_streaming_audio.py` | `aigc3d/LAM_Audio2Expression` | Reference streaming usage |\n| `src/gaussianAvatar.ts` | `aigc3d/LAM_WebRender` | JS renderer API and expression data protocol |\n| `handlers/avatar/lam/avatar_handler_lam_audio2expression.py` | `HumanAIGC-Engineering/OpenAvatarChat` | How audio bytes → blendshapes → WebSocket works in production |\n| `config/chat_with_lam.yaml` | `HumanAIGC-Engineering/OpenAvatarChat` | Full handler stack configuration |\n| `app_lam.py` | `aigc3d/LAM` | Avatar export (ZIP) with Blender |\n\n---\n\n## 7. Integration Plan for Your Use Case\n\nYour pipeline (TTS audio → animated avatar in browser canvas):\n\n1. **Python backend**: Load `Audio2ExpressionInfer` once at startup. For each TTS response, convert audio to 16kHz numpy, call `infer_streaming_audio()` in 1-second chunks, buffer output blendshape frames.\n2. **WebSocket**: Stream blendshape frames as JSON `{jawOpen: 0.4, eyeBlinkLeft: 0.1, ...}` to the browser at 30fps.\n3. **Browser JS**: Load `gaussian-splat-renderer-for-lam` with the pre-exported avatar ZIP. Wire `getExpressionData()` to read from the WebSocket buffer. The renderer calls this each animation frame and deforms the Gaussian Splat mesh live in WebGL.\n\n**Critical blocker to verify:** The `LAM_audio2exp` model weights (`LAM_audio2exp.tar`) appear to not be publicly available as of February 2026 (GitHub issue #91 in `aigc3d/LAM` is open and unanswered). 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{"parentUuid":"375c2350-aa3f-4115-bf73-25a4768ab18a","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01RPcGTwRSvACR5xDi3Y6ci9","type":"message","role":"assistant","content":[{"type":"text","text":"Voilà l'architecture complète — c'est très clair maintenant.\n\n---\n\n## Pipeline exact pour du live\n\n```\n[Agent répond] → [TTS → audio 16kHz mono]\n    → [Python: Audio2Expression → 52 blendshapes ARKit @ 30fps]\n        → [WebSocket JSON] → [Browser: gaussian-splat-renderer-for-lam WebGL]\n                                    ↑\n                              Avatar ZIP (pré-exporté depuis ta photo)\n```\n\n**Ce qui est élégant** : le renderer WebGL tourne côté browser, Python n'envoie que des tableaux de 52 floats par frame — très léger.\n\n---\n\n## Un blocage potentiel à vérifier\n\nLe modèle `LAM_audio2exp.tar` (les poids d'Audio2Expression) n'était **pas encore publié** début 2026 (issue #91 du repo LAM ouverte). On est en juin 2026, ça a peut-être changé. Vérifie ici :\n```\nhttps://github.com/aigc3d/LAM/issues/91\n```\n\n---\n\n## Ce qu'il faut faire dans l'ordre\n\n**Pendant que `pip install -r requirements.txt` tourne**, dis-moi :\n\n1. Est-ce que le pip install LAM s'est bien terminé ?\n2. Est-ce que tu as **Blender 4.0+** installé ? (nécessaire pour exporter l'avatar ZIP depuis une photo)\n\nEnsuite les étapes seront :\n\n1. Clôner `LAM_Audio2Expression` + vérifier que les poids sont disponibles\n2. Exporter ton avatar ZIP via `app_lam.py` (une photo → ton avatar 3D)\n3. Ajouter un **WebSocket** dans `agent_gui/app.py`\n4. Ajouter le renderer WebGL (`gaussian-splat-renderer-for-lam`) dans ton canvas HTML"}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3084,"cache_read_input_tokens":40464,"output_tokens":931,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3084,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":931,"cache_read_input_tokens":40464,"cache_creation_input_tokens":3084,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3084},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5jR6JXU5skcp2HKtf4W","type":"assistant","uuid":"9b77577f-0a16-4897-ba5c-51c8f76cae98","timestamp":"2026-06-15T21:10:59.006Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"1bc0dc74-f2aa-40ff-888e-8044b01e5928","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01Cp4RvcSFRN8ZXbQhizUj63","type":"message","role":"assistant","content":[{"type":"text","text":"Base installée. Maintenant pytorch3d avec `--no-build-isolation` pour qu'il trouve torch :"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1190,"cache_read_input_tokens":49453,"output_tokens":224,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1190,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":224,"cache_read_input_tokens":49453,"cache_creation_input_tokens":1190,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1190},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kGyE1ugEVCvoCagNLa","type":"assistant","uuid":"2b3defad-5d6e-4857-bfa2-0be2d8c8fa5e","timestamp":"2026-06-15T21:21:57.811Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"2b3defad-5d6e-4857-bfa2-0be2d8c8fa5e","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01Cp4RvcSFRN8ZXbQhizUj63","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HTcYMK6xGqHGj6DK6S1bqU","name":"Bash","input":{"command":"$HOME/miniconda3/bin/conda run -n lam pip install --no-build-isolation \"git+https://github.com/facebookresearch/pytorch3d.git\" 2>&1 | tail -10","description":"Install pytorch3d with no-build-isolation","timeout":600000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1190,"cache_read_input_tokens":49453,"output_tokens":224,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1190,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":224,"cache_read_input_tokens":49453,"cache_creation_input_tokens":1190,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1190},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kGyE1ugEVCvoCagNLa","type":"assistant","uuid":"530fafbd-905c-4f08-81a9-9d1832e8d5fe","timestamp":"2026-06-15T21:21:58.914Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"530fafbd-905c-4f08-81a9-9d1832e8d5fe","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HTcYMK6xGqHGj6DK6S1bqU","type":"tool_result","content":"Downloading portalocker-3.2.0-py3-none-any.whl (22 kB)\nBuilding wheels for collected packages: pytorch3d, iopath\n  Building wheel for pytorch3d (pyproject.toml): started\n  Building wheel for pytorch3d (pyproject.toml): finished with status 'error'\n  Building wheel for iopath (pyproject.toml): started\n  Building wheel for iopath (pyproject.toml): finished with status 'done'\n  Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31533 sha256=ddbf7263ac8a79bc2c5145090b16d70a49e4774e026cd6acecf5c85c88c0f518\n  Stored in directory: /home/wildlama/.cache/pip/wheels/ba/5e/16/6117f8fe7e9c0c161a795e10d94645ebcf301ccbd01f66d8ec\nSuccessfully built iopath\nFailed to build pytorch3d","is_error":false}]},"uuid":"002e72d9-7011-4a0f-8ddd-a704b8a42a08","timestamp":"2026-06-15T21:22:06.593Z","toolUseResult":{"stdout":"Downloading portalocker-3.2.0-py3-none-any.whl (22 kB)\nBuilding wheels for collected packages: pytorch3d, iopath\n  Building wheel for pytorch3d (pyproject.toml): started\n  Building wheel for pytorch3d (pyproject.toml): finished with status 'error'\n  Building wheel for iopath (pyproject.toml): started\n  Building wheel for iopath (pyproject.toml): finished with status 'done'\n  Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31533 sha256=ddbf7263ac8a79bc2c5145090b16d70a49e4774e026cd6acecf5c85c88c0f518\n  Stored in directory: /home/wildlama/.cache/pip/wheels/ba/5e/16/6117f8fe7e9c0c161a795e10d94645ebcf301ccbd01f66d8ec\nSuccessfully built iopath\nFailed to build pytorch3d","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"530fafbd-905c-4f08-81a9-9d1832e8d5fe","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"002e72d9-7011-4a0f-8ddd-a704b8a42a08","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01CHS7UShqCqEhoF5Z5nujfP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JVdP1jVSTBdstf49Q2Qhyr","name":"Bash","input":{"command":"$HOME/miniconda3/bin/conda run -n lam pip install --no-build-isolation \"git+https://github.com/facebookresearch/pytorch3d.git\" 2>&1 | grep -A5 \"error\\|Error\" | head -30","description":"Get pytorch3d build error details","timeout":300000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":502,"cache_read_input_tokens":50643,"output_tokens":157,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":502,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":157,"cache_read_input_tokens":50643,"cache_creation_input_tokens":502,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":502},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kHqK3e2LZXqqJYScTc","type":"assistant","uuid":"f264b25c-6615-4618-83e3-3c38dc993e0b","timestamp":"2026-06-15T21:22:09.434Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"f264b25c-6615-4618-83e3-3c38dc993e0b","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JVdP1jVSTBdstf49Q2Qhyr","type":"tool_result","content":"  error: subprocess-exited-with-error\n  \n  × Building wheel for pytorch3d (pyproject.toml) did not run successfully.\n  │ exit code: 1\n  ╰─> [296 lines of output]\n      <string>:109: UserWarning: The environment variable `CUB_HOME` was not found. NVIDIA CUB is required for compilation and can be downloaded from `https://github.com/NVIDIA/cub/releases`. You can unpack it to a location of your choice and set the environment variable `CUB_HOME` to the folder containing the `CMakeListst.txt` file.\n--\n          raise RuntimeError(\n      RuntimeError: The current installed version of /home/wildlama/miniconda3/envs/lam/bin/x86_64-conda-linux-gnu-c++ (14.3.0) is greater than the maximum required version by CUDA 12.8. Please make sure to use an adequate version of /home/wildlama/miniconda3/envs/lam/bin/x86_64-conda-linux-gnu-c++ (>=6.0.0, <14.0).\n      [end of output]\n  \n  note: This error originates from a subprocess, and is likely not a problem with pip.\n  ERROR: Failed building wheel for pytorch3d\nerror: failed-wheel-build-for-install\n\n× Failed to build installable wheels for some pyproject.toml based projects\n╰─> pytorch3d\nERROR conda.cli.main_run:execute(148): `conda run pip install --no-build-isolation git+https://github.com/facebookresearch/pytorch3d.git` failed. (See above for error)\nCollecting git+https://github.com/facebookresearch/pytorch3d.git\n  Cloning https://github.com/facebookresearch/pytorch3d.git to /tmp/pip-req-build-8q92azyg\n  Resolved https://github.com/facebookresearch/pytorch3d.git to commit 7f8a8a142fa1383a7895ed01dd7082105f9a81fb\n  Preparing metadata (pyproject.toml): started\n  Preparing metadata (pyproject.toml): finished with status 'done'\n--\n  Building wheel for pytorch3d (pyproject.toml): finished with status 'error'\nFailed to build pytorch3d","is_error":false}]},"uuid":"5b5e8eec-1e5f-4212-bae9-0975975896e2","timestamp":"2026-06-15T21:22:16.880Z","toolUseResult":{"stdout":"  error: subprocess-exited-with-error\n  \n  × Building wheel for pytorch3d (pyproject.toml) did not run successfully.\n  │ exit code: 1\n  ╰─> [296 lines of output]\n      <string>:109: UserWarning: The environment variable `CUB_HOME` was not found. NVIDIA CUB is required for compilation and can be downloaded from `https://github.com/NVIDIA/cub/releases`. You can unpack it to a location of your choice and set the environment variable `CUB_HOME` to the folder containing the `CMakeListst.txt` file.\n--\n          raise RuntimeError(\n      RuntimeError: The current installed version of /home/wildlama/miniconda3/envs/lam/bin/x86_64-conda-linux-gnu-c++ (14.3.0) is greater than the maximum required version by CUDA 12.8. Please make sure to use an adequate version of /home/wildlama/miniconda3/envs/lam/bin/x86_64-conda-linux-gnu-c++ (>=6.0.0, <14.0).\n      [end of output]\n  \n  note: This error originates from a subprocess, and is likely not a problem with pip.\n  ERROR: Failed building wheel for pytorch3d\nerror: failed-wheel-build-for-install\n\n× Failed to build installable wheels for some pyproject.toml based projects\n╰─> pytorch3d\nERROR conda.cli.main_run:execute(148): `conda run pip install --no-build-isolation git+https://github.com/facebookresearch/pytorch3d.git` failed. (See above for error)\nCollecting git+https://github.com/facebookresearch/pytorch3d.git\n  Cloning https://github.com/facebookresearch/pytorch3d.git to /tmp/pip-req-build-8q92azyg\n  Resolved https://github.com/facebookresearch/pytorch3d.git to commit 7f8a8a142fa1383a7895ed01dd7082105f9a81fb\n  Preparing metadata (pyproject.toml): started\n  Preparing metadata (pyproject.toml): finished with status 'done'\n--\n  Building wheel for pytorch3d (pyproject.toml): finished with status 'error'\nFailed to build pytorch3d","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"f264b25c-6615-4618-83e3-3c38dc993e0b","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"11f5ef91-f8e0-4fd1-b712-ec306ac8707a","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DWQi8oHbNtS8yZbqgpjgsv","type":"tool_result","content":"# LAM-A2E: Audio to Expression\n\n[![Website](https://raw.githubusercontent.com/prs-eth/Marigold/main/doc/badges/badge-website.svg)](https://aigc3d.github.io/projects/LAM/) \n[![Apache License](https://img.shields.io/badge/📃-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)\n[![ModelScope Demo](https://img.shields.io/badge/%20ModelScope%20-Space-blue)](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E) \n\n## Description\n#### This project leverages audio input to generate ARKit blendshapes-driven facial expressions in ⚡real-time⚡, powering ultra-realistic 3D avatars generated by [LAM](https://github.com/aigc3d/LAM). \nTo enable ARKit-driven animation of the LAM model, we adapted ARKit blendshapes to align with FLAME's facial topology through manual customization. The LAM-A2E network follows an encoder-decoder architecture, as shown below. We adopt the state-of-the-art pre-trained speech model Wav2Vec for the audio encoder. The features extracted from the raw audio waveform are combined with style features and fed into the decoder, which outputs stylized blendshape coefficients. \n\n<div align=\"center\">\n<img src=\"./assets/images/framework.png\" alt=\"Architecture\" width=\"90%\" align=center/>\n</div>\n\n## Demo\n\n<div align=\"center\">\n  <video controls src=\"https://github.com/user-attachments/assets/a89a0d70-a573-4d61-91bd-4f09a0b6ce2c\">\n  </video>\n</div>\n\n## 📢 News\n\n**[May 21, 2025]** We have released a [Avatar Export Feature](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM_Large_Avatar_Model), enabling users to generate facial expressions from audio using any [LAM-generated](https://github.com/aigc3d/LAM) 3D digital humans.  <br>\n**[April 21, 2025]** We have released the [ModelScope](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E) Space ! <br>\n**[April 21, 2025]** We have released the WebGL Interactive Chatting Avatar SDK on [OpenAvatarChat](https://github.com/HumanAIGC-Engineering/OpenAvatarChat) (including LLM, ASR, TTS, Avatar), with which you can freely chat with our generated 3D Digital Human ! 🔥 <br>\n\n### To do list\n- [ ] Release Huggingface space.\n- [x] Release [Modelscope demo space](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E). You can try the demo or pull the demo source code and deploy it on your own machine.\n- [ ] Release the LAM-A2E model based on the Flame expression.\n- [x] Release Interactive Chatting Avatar SDK with [OpenAvatarChat](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E), including LLM, ASR, TTS, LAM-Avatars.\n\n\n\n## 🚀 Get Started\n### Environment Setup\n```bash\ngit clone git@github.com:aigc3d/LAM_Audio2Expression.git\ncd LAM_Audio2Expression\n# Create conda environment (currently only supports Python 3.10)\nconda create -n lam_a2e python=3.10\n# Activate the conda environment\nconda activate lam_a2e\n# Install with Cuda 12.1\nsh  ./scripts/install/install_cu121.sh\n# Or Install with Cuda 11.8\nsh ./scripts/install/install_cu118.sh\n```\n\n\n### Download\n\n```\n# HuggingFace download\n# Download Assets and Model Weights\nhuggingface-cli download 3DAIGC/LAM_audio2exp --local-dir ./\ntar -xzvf LAM_audio2exp_assets.tar && rm -f LAM_audio2exp_assets.tar\ntar -xzvf LAM_audio2exp_streaming.tar && rm -f LAM_audio2exp_streaming.tar\n\n# Or OSS Download (In case of HuggingFace download failing)\n# Download Assets\nwget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_assets.tar\ntar -xzvf LAM_audio2exp_assets.tar && rm -f LAM_audio2exp_assets.tar\n# Download Model Weights\nwget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_streaming.tar\ntar -xzvf LAM_audio2exp_streaming.tar && rm -f LAM_audio2exp_streaming.tar\n\nOr Modelscope Download\ngit clone https://www.modelscope.cn/Damo_XR_Lab/LAM_audio2exp.git ./modelscope_download\n```\n\n\n### Quick Start Guide\n#### Using <a href=\"https://github.com/gradio-app/gradio\">Gradio</a> Interface: \nWe provide a simple Gradio demo with **WebGL Render**, and you can get rendering results by uploading audio in seconds.\n\n[//]: # (<img src=\"./assets/images/snapshot.png\" alt=\"teaser\" width=\"1000\"/>)\n<div align=\"center\">\n  <video controls src=\"https://github.com/user-attachments/assets/2bb4e74f-cd96-4c50-9833-fae10b1ead4c","is_error":false}]},"uuid":"5dc8b5e7-d5a1-4612-b7d7-2c435ddd4cdf","timestamp":"2026-06-15T21:25:32.227Z","toolUseResult":{"stdout":"# LAM-A2E: Audio to Expression\n\n[![Website](https://raw.githubusercontent.com/prs-eth/Marigold/main/doc/badges/badge-website.svg)](https://aigc3d.github.io/projects/LAM/) \n[![Apache License](https://img.shields.io/badge/📃-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)\n[![ModelScope Demo](https://img.shields.io/badge/%20ModelScope%20-Space-blue)](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E) \n\n## Description\n#### This project leverages audio input to generate ARKit blendshapes-driven facial expressions in ⚡real-time⚡, powering ultra-realistic 3D avatars generated by [LAM](https://github.com/aigc3d/LAM). \nTo enable ARKit-driven animation of the LAM model, we adapted ARKit blendshapes to align with FLAME's facial topology through manual customization. The LAM-A2E network follows an encoder-decoder architecture, as shown below. We adopt the state-of-the-art pre-trained speech model Wav2Vec for the audio encoder. The features extracted from the raw audio waveform are combined with style features and fed into the decoder, which outputs stylized blendshape coefficients. \n\n<div align=\"center\">\n<img src=\"./assets/images/framework.png\" alt=\"Architecture\" width=\"90%\" align=center/>\n</div>\n\n## Demo\n\n<div align=\"center\">\n  <video controls src=\"https://github.com/user-attachments/assets/a89a0d70-a573-4d61-91bd-4f09a0b6ce2c\">\n  </video>\n</div>\n\n## 📢 News\n\n**[May 21, 2025]** We have released a [Avatar Export Feature](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM_Large_Avatar_Model), enabling users to generate facial expressions from audio using any [LAM-generated](https://github.com/aigc3d/LAM) 3D digital humans.  <br>\n**[April 21, 2025]** We have released the [ModelScope](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E) Space ! <br>\n**[April 21, 2025]** We have released the WebGL Interactive Chatting Avatar SDK on [OpenAvatarChat](https://github.com/HumanAIGC-Engineering/OpenAvatarChat) (including LLM, ASR, TTS, Avatar), with which you can freely chat with our generated 3D Digital Human ! 🔥 <br>\n\n### To do list\n- [ ] Release Huggingface space.\n- [x] Release [Modelscope demo space](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E). You can try the demo or pull the demo source code and deploy it on your own machine.\n- [ ] Release the LAM-A2E model based on the Flame expression.\n- [x] Release Interactive Chatting Avatar SDK with [OpenAvatarChat](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E), including LLM, ASR, TTS, LAM-Avatars.\n\n\n\n## 🚀 Get Started\n### Environment Setup\n```bash\ngit clone git@github.com:aigc3d/LAM_Audio2Expression.git\ncd LAM_Audio2Expression\n# Create conda environment (currently only supports Python 3.10)\nconda create -n lam_a2e python=3.10\n# Activate the conda environment\nconda activate lam_a2e\n# Install with Cuda 12.1\nsh  ./scripts/install/install_cu121.sh\n# Or Install with Cuda 11.8\nsh ./scripts/install/install_cu118.sh\n```\n\n\n### Download\n\n```\n# HuggingFace download\n# Download Assets and Model Weights\nhuggingface-cli download 3DAIGC/LAM_audio2exp --local-dir ./\ntar -xzvf LAM_audio2exp_assets.tar && rm -f LAM_audio2exp_assets.tar\ntar -xzvf LAM_audio2exp_streaming.tar && rm -f LAM_audio2exp_streaming.tar\n\n# Or OSS Download (In case of HuggingFace download failing)\n# Download Assets\nwget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_assets.tar\ntar -xzvf LAM_audio2exp_assets.tar && rm -f LAM_audio2exp_assets.tar\n# Download Model Weights\nwget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_streaming.tar\ntar -xzvf LAM_audio2exp_streaming.tar && rm -f LAM_audio2exp_streaming.tar\n\nOr Modelscope Download\ngit clone https://www.modelscope.cn/Damo_XR_Lab/LAM_audio2exp.git ./modelscope_download\n```\n\n\n### Quick Start Guide\n#### Using <a href=\"https://github.com/gradio-app/gradio\">Gradio</a> Interface: \nWe provide a simple Gradio demo with **WebGL Render**, and you can get rendering results by uploading audio in seconds.\n\n[//]: # (<img src=\"./assets/images/snapshot.png\" alt=\"teaser\" width=\"1000\"/>)\n<div align=\"center\">\n  <video controls src=\"https://github.com/user-attachments/assets/2bb4e74f-cd96-4c50-9833-fae10b1ead4c","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"11f5ef91-f8e0-4fd1-b712-ec306ac8707a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"63abf752-2b16-405e-b834-6d9480432b92","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014YwsoLyFA1wMWjzHJUfAyH","type":"tool_result","content":"    Found existing installation: setuptools 82.0.1\n    Uninstalling setuptools-82.0.1:\n      Successfully uninstalled setuptools-82.0.1\n\nSuccessfully installed MarkupSafe-3.0.3 cuda-bindings-12.9.4 cuda-pathfinder-1.2.2 cuda-toolkit-12.8.1 filelock-3.29.0 fsspec-2026.4.0 jinja2-3.1.6 mpmath-1.3.0 networkx-3.4.2 numpy-2.2.6 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.19.0.56 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.28.9 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvshmem-cu12-3.4.5 nvidia-nvtx-cu12-12.8.90 pillow-12.2.0 setuptools-70.2.0 sympy-1.14.0 torch-2.11.0+cu128 torchaudio-2.11.0+cu128 torchvision-0.26.0+cu128 triton-3.6.0 typing-extensions-4.15.0","is_error":false}]},"uuid":"5baebb69-b439-4308-ac97-410f9f3c38ee","timestamp":"2026-06-15T21:27:09.177Z","toolUseResult":{"stdout":"    Found existing installation: setuptools 82.0.1\n    Uninstalling setuptools-82.0.1:\n      Successfully uninstalled setuptools-82.0.1\n\nSuccessfully installed MarkupSafe-3.0.3 cuda-bindings-12.9.4 cuda-pathfinder-1.2.2 cuda-toolkit-12.8.1 filelock-3.29.0 fsspec-2026.4.0 jinja2-3.1.6 mpmath-1.3.0 networkx-3.4.2 numpy-2.2.6 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.19.0.56 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.28.9 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvshmem-cu12-3.4.5 nvidia-nvtx-cu12-12.8.90 pillow-12.2.0 setuptools-70.2.0 sympy-1.14.0 torch-2.11.0+cu128 torchaudio-2.11.0+cu128 torchvision-0.26.0+cu128 triton-3.6.0 typing-extensions-4.15.0","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"63abf752-2b16-405e-b834-6d9480432b92","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"4227c20a-2c91-4303-83be-12955cfc8613","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01RYWpK2mVDJYrNDRCqkQqUC","type":"message","role":"assistant","content":[{"type":"text","text":"Tout installé. Maintenant on télécharge les poids du modèle :"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1171,"cache_read_input_tokens":59509,"output_tokens":193,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1171,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":193,"cache_read_input_tokens":59509,"cache_creation_input_tokens":1171,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1171},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5khwBw3hELsysVcukyp","type":"assistant","uuid":"8472fb23-9492-466e-b477-a43ec24264a1","timestamp":"2026-06-15T21:27:36.857Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"8472fb23-9492-466e-b477-a43ec24264a1","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01RYWpK2mVDJYrNDRCqkQqUC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NXPJJemoraKnSUnawMVSbj","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/LAM_Audio2Expression && $HOME/miniconda3/bin/conda run -n lam_a2e huggingface-cli download 3DAIGC/LAM_audio2exp --local-dir ./ 2>&1 | tail -15","description":"Download LAM Audio2Expression model weights from HuggingFace","timeout":600000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1171,"cache_read_input_tokens":59509,"output_tokens":193,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1171,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":193,"cache_read_input_tokens":59509,"cache_creation_input_tokens":1171,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1171},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5khwBw3hELsysVcukyp","type":"assistant","uuid":"5a68e194-c6d2-4f07-a5f5-03e33ccbd0d8","timestamp":"2026-06-15T21:27:38.750Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"5a68e194-c6d2-4f07-a5f5-03e33ccbd0d8","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NXPJJemoraKnSUnawMVSbj","type":"tool_result","content":"Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]Downloading 'LAM_audio2exp_assets.tar' to '.cache/huggingface/download/xn1GHoL6lVREWtyO9EhaiJhnvqg=.f5759f080ec1352d454d88d5a32f09c0fc8aeaf50b9ac1fd9c34a4acd82acfa1.incomplete'\nDownloading 'LAM_audio2exp_streaming.tar' to '.cache/huggingface/download/o0S-c6zjkluMpxZRzHxvu4G9aD0=.0877f419b1b7b6856479b743b71625ad478f97434404e91288f9f0d6d516b97a.incomplete'\nDownloading 'README.md' to '.cache/huggingface/download/Xn7B-BWUGOee2Y6hCZtEhtFu4BE=.7b95401dc46245ac339fc25059d4a56d90b4cde5.incomplete'\nDownloading '.gitattributes' to '.cache/huggingface/download/wPaCkH-WbT7GsmxMKKrNZTV4nSM=.a6344aac8c09253b3b630fb776ae94478aa0275b.incomplete'\nDownload complete. Moving file to README.md\nDownload complete. Moving file to .gitattributes\n\nFetching 4 files:  25%|██▌       | 1/4 [00:00<00:01,  2.52it/s]Download complete. Moving file to LAM_audio2exp_assets.tar\n\nFetching 4 files:  50%|█████     | 2/4 [00:02<00:02,  1.18s/it]Download complete. Moving file to LAM_audio2exp_streaming.tar\n\nFetching 4 files:  75%|███████▌  | 3/4 [00:03<00:01,  1.28s/it]\nFetching 4 files: 100%|██████████| 4/4 [00:03<00:00,  1.13it/s]\n\u001b[33m⚠️  Warning: 'huggingface-cli download' is deprecated. Use 'hf download' instead.\u001b[0m\n/mnt/DATA/WORK/LAM_Audio2Expression\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"5cac883a-e6d4-4b66-927f-8320ddf839fc","timestamp":"2026-06-15T21:27:43.605Z","toolUseResult":{"stdout":"Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]Downloading 'LAM_audio2exp_assets.tar' to '.cache/huggingface/download/xn1GHoL6lVREWtyO9EhaiJhnvqg=.f5759f080ec1352d454d88d5a32f09c0fc8aeaf50b9ac1fd9c34a4acd82acfa1.incomplete'\nDownloading 'LAM_audio2exp_streaming.tar' to '.cache/huggingface/download/o0S-c6zjkluMpxZRzHxvu4G9aD0=.0877f419b1b7b6856479b743b71625ad478f97434404e91288f9f0d6d516b97a.incomplete'\nDownloading 'README.md' to '.cache/huggingface/download/Xn7B-BWUGOee2Y6hCZtEhtFu4BE=.7b95401dc46245ac339fc25059d4a56d90b4cde5.incomplete'\nDownloading '.gitattributes' to '.cache/huggingface/download/wPaCkH-WbT7GsmxMKKrNZTV4nSM=.a6344aac8c09253b3b630fb776ae94478aa0275b.incomplete'\nDownload complete. Moving file to README.md\nDownload complete. Moving file to .gitattributes\n\nFetching 4 files:  25%|██▌       | 1/4 [00:00<00:01,  2.52it/s]Download complete. Moving file to LAM_audio2exp_assets.tar\n\nFetching 4 files:  50%|█████     | 2/4 [00:02<00:02,  1.18s/it]Download complete. Moving file to LAM_audio2exp_streaming.tar\n\nFetching 4 files:  75%|███████▌  | 3/4 [00:03<00:01,  1.28s/it]\nFetching 4 files: 100%|██████████| 4/4 [00:03<00:00,  1.13it/s]\n\u001b[33m⚠️  Warning: 'huggingface-cli download' is deprecated. Use 'hf download' instead.\u001b[0m\n/mnt/DATA/WORK/LAM_Audio2Expression","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"5a68e194-c6d2-4f07-a5f5-03e33ccbd0d8","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"5cac883a-e6d4-4b66-927f-8320ddf839fc","isSidechain":false,"attachment":{"type":"task_reminder","content":[],"itemCount":0},"type":"attachment","uuid":"ba3f03aa-eca5-44aa-b490-d856652c197c","timestamp":"2026-06-15T21:27:43.608Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"ba3f03aa-eca5-44aa-b490-d856652c197c","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01Bmy2F7tAmoS4YbcNxHpRrH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KxojUfP6yY3JC32pkDD4wN","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/LAM_Audio2Expression && tar -xzvf LAM_audio2exp_assets.tar && rm -f LAM_audio2exp_assets.tar && tar -xzvf LAM_audio2exp_streaming.tar && rm -f LAM_audio2exp_streaming.tar && ls -la","description":"Extract model weights tarballs","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":942,"cache_read_input_tokens":60680,"output_tokens":171,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":942,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":171,"cache_read_input_tokens":60680,"cache_creation_input_tokens":942,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":942},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kigBp2syqiR2yVjkt4","type":"assistant","uuid":"fc255ab6-eabe-4706-a5fe-7be7192a9357","timestamp":"2026-06-15T21:27:47.974Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"type":"ai-title","aiTitle":"Intégrer LAM pour avatars parlants dans GUI agent IA","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7"}
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{"parentUuid":"749e7074-426e-465f-9178-18504c1b4ef1","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01WZHTtRjvx2wJKsmTsRd4FV","type":"message","role":"assistant","content":[{"type":"text","text":"Modèle extrait. On teste que ça tourne avec un audio sample :"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":553,"cache_read_input_tokens":63883,"output_tokens":167,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":553,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":167,"cache_read_input_tokens":63883,"cache_creation_input_tokens":553,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":553},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kkfr2rvR7WYGR4qPCT","type":"assistant","uuid":"5fbf3e34-6539-4fe4-a93a-022c4a14daaa","timestamp":"2026-06-15T21:28:13.269Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"5fbf3e34-6539-4fe4-a93a-022c4a14daaa","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01WZHTtRjvx2wJKsmTsRd4FV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011QjRjVfr8JnmbrqU7pANUH","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/LAM_Audio2Expression && $HOME/miniconda3/bin/conda run -n lam_a2e python inference_streaming_audio.py 2>&1 | head -40","description":"Test Audio2Expression inference with sample audio","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":553,"cache_read_input_tokens":63883,"output_tokens":167,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":553,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":167,"cache_read_input_tokens":63883,"cache_creation_input_tokens":553,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":553},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kkfr2rvR7WYGR4qPCT","type":"assistant","uuid":"540f17ed-3fce-4c52-9764-4b5b814c6836","timestamp":"2026-06-15T21:28:15.032Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"540f17ed-3fce-4c52-9764-4b5b814c6836","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011QjRjVfr8JnmbrqU7pANUH","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(\nThe cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n\n0it [00:00, ?it/s]\n0it [00:00, ?it/s]\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 23:28:17,098 INFO infer.py line 51 42410] => Loading config ...\n[2026-06-15 23:28:17,098 INFO infer.py line 58 42410] => Building model ...\n[2026-06-15 23:28:17,533 INFO infer.py line 66 42410] Num params: 97912596\n[2026-06-15 23:28:17,833 INFO infer.py line 73 42410] Loading weight at: pretrained_models/lam_audio2exp_streaming.tar\n[2026-06-15 23:28:17,996 INFO infer.py line 85 42410] => Loaded weight 'pretrained_models/lam_audio2exp_streaming.tar'\n/mnt/DATA/WORK/LAM_Audio2Expression/inference_streaming_audio.py:45: UserWarning: PySoundFile failed. Trying audioread instead.\n  audio, sample_rate = librosa.load(cfg.audio_input, sr=16000)\n/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/core/audio.py:184: FutureWarning: librosa.core.audio.__audioread_load\n\tDeprecated as of librosa version 0.10.0.\n\tIt will be removed in librosa version 1.0.\n  y, sr_native = __audioread_load(path, offset, duration, dtype)\nTraceback (most recent call last):\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/core/audio.py\", line 176, in load\n    y, sr_native = __soundfile_load(path, offset, duration, dtype)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/core/audio.py\", line 209, in __soundfile_load\n    context = sf.SoundFile(path)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/soundfile.py\", line 708, in __init__\n    self._file = self._open(file, mode_int, closefd)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/soundfile.py\", line 1296, in _open\n    raise LibsndfileError(err, prefix=f\"Error opening {self.name!r}: \")\nsoundfile.LibsndfileError: Error opening './assets/sample_audio/BarackObama.wav': System error.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/mnt/DATA/WORK/LAM_Audio2Expression/inference_streaming_audio.py\", line 45, in <module>\n    audio, sample_rate = librosa.load(cfg.audio_input, sr=16000)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/core/audio.py\", line 184, in load\n    y, sr_native = __audioread_load(path, offset, duration, dtype)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/decorator/__init__.py\", line 247, in fun\n    return caller(func, *(extras + args), **kw)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/util/decorators.py\", line 63, in __wrapper\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"bdd39860-c6dd-4fe4-aa2e-f2261ae716f7","timestamp":"2026-06-15T21:28:20.293Z","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(\nThe cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n\n0it [00:00, ?it/s]\n0it [00:00, ?it/s]\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 23:28:17,098 INFO infer.py line 51 42410] => Loading config ...\n[2026-06-15 23:28:17,098 INFO infer.py line 58 42410] => Building model ...\n[2026-06-15 23:28:17,533 INFO infer.py line 66 42410] Num params: 97912596\n[2026-06-15 23:28:17,833 INFO infer.py line 73 42410] Loading weight at: pretrained_models/lam_audio2exp_streaming.tar\n[2026-06-15 23:28:17,996 INFO infer.py line 85 42410] => Loaded weight 'pretrained_models/lam_audio2exp_streaming.tar'\n/mnt/DATA/WORK/LAM_Audio2Expression/inference_streaming_audio.py:45: UserWarning: PySoundFile failed. Trying audioread instead.\n  audio, sample_rate = librosa.load(cfg.audio_input, sr=16000)\n/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/core/audio.py:184: FutureWarning: librosa.core.audio.__audioread_load\n\tDeprecated as of librosa version 0.10.0.\n\tIt will be removed in librosa version 1.0.\n  y, sr_native = __audioread_load(path, offset, duration, dtype)\nTraceback (most recent call last):\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/core/audio.py\", line 176, in load\n    y, sr_native = __soundfile_load(path, offset, duration, dtype)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/core/audio.py\", line 209, in __soundfile_load\n    context = sf.SoundFile(path)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/soundfile.py\", line 708, in __init__\n    self._file = self._open(file, mode_int, closefd)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/soundfile.py\", line 1296, in _open\n    raise LibsndfileError(err, prefix=f\"Error opening {self.name!r}: \")\nsoundfile.LibsndfileError: Error opening './assets/sample_audio/BarackObama.wav': System error.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/mnt/DATA/WORK/LAM_Audio2Expression/inference_streaming_audio.py\", line 45, in <module>\n    audio, sample_rate = librosa.load(cfg.audio_input, sr=16000)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/core/audio.py\", line 184, in load\n    y, sr_native = __audioread_load(path, offset, duration, dtype)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/decorator/__init__.py\", line 247, in fun\n    return caller(func, *(extras + args), **kw)\n  File \"/home/wildlama/miniconda3/envs/lam_a2e/lib/python3.10/site-packages/librosa/util/decorators.py\", line 63, in __wrapper","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"540f17ed-3fce-4c52-9764-4b5b814c6836","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"bdd39860-c6dd-4fe4-aa2e-f2261ae716f7","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01AvMAQ5WYX2WMt8d2g5ibAr","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":1460,"cache_read_input_tokens":64436,"output_tokens":290,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1460,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":290,"cache_read_input_tokens":64436,"cache_creation_input_tokens":1460,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1460},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kmP329GcRZHngmwXwj","type":"assistant","uuid":"00a640f8-03e1-4251-be39-ee3e8663127e","timestamp":"2026-06-15T21:28:24.602Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"00a640f8-03e1-4251-be39-ee3e8663127e","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01AvMAQ5WYX2WMt8d2g5ibAr","type":"message","role":"assistant","content":[{"type":"text","text":"**Le modèle se charge parfaitement** (97M paramètres, poids chargés). L'erreur est juste le nom du fichier audio dans le script. Je corrige :"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1460,"cache_read_input_tokens":64436,"output_tokens":290,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1460,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":290,"cache_read_input_tokens":64436,"cache_creation_input_tokens":1460,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1460},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kmP329GcRZHngmwXwj","type":"assistant","uuid":"1ef3edb6-9730-418c-a8ee-d76254358813","timestamp":"2026-06-15T21:28:25.746Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"1ef3edb6-9730-418c-a8ee-d76254358813","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01AvMAQ5WYX2WMt8d2g5ibAr","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018CvFxuJ6vc3tPKA7ehDwGt","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":1460,"cache_read_input_tokens":64436,"output_tokens":290,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1460,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":290,"cache_read_input_tokens":64436,"cache_creation_input_tokens":1460,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1460},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kmP329GcRZHngmwXwj","type":"assistant","uuid":"edc024aa-0b03-4397-8907-f01057156cf3","timestamp":"2026-06-15T21:28:25.966Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"edc024aa-0b03-4397-8907-f01057156cf3","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018CvFxuJ6vc3tPKA7ehDwGt","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":"93fcdbaa-26d6-4f4c-a294-1db7eb3a9828","timestamp":"2026-06-15T21:28:25.990Z","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":"edc024aa-0b03-4397-8907-f01057156cf3","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"type":"last-prompt","lastPrompt":"je n'en sais rien, comment je sais ca si le pip install s'est terminé ?","leafUuid":"93fcdbaa-26d6-4f4c-a294-1db7eb3a9828","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7"}
{"type":"ai-title","aiTitle":"Intégrer LAM pour avatars parlants dans GUI agent IA","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7"}
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{"parentUuid":"ce8789b2-a916-4ff5-8e20-96370223112a","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019NuUkTZcj1kXHzHxNsbjM2","type":"tool_result","content":"audio_input = './assets/sample_audio/BarackObama.wav'\nsave_json_path = 'bsData.json'","is_error":false}]},"uuid":"f3672b8d-0d3f-43a1-ba22-beb35edc44e7","timestamp":"2026-06-15T21:28:29.642Z","toolUseResult":{"stdout":"audio_input = './assets/sample_audio/BarackObama.wav'\nsave_json_path = 'bsData.json'","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"ce8789b2-a916-4ff5-8e20-96370223112a","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"b1cf54a6-3814-4f64-aa62-f62a8d2e5b6a","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01Lyx2e7DhuHdBXtekTyZvsX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AKW2uP818JVWp7KsreEHWe","name":"Bash","input":{"command":"sed -i \"s|BarackObama.wav|BarackObama_english.wav|\" /mnt/DATA/WORK/LAM_Audio2Expression/configs/lam_audio2exp_config_streaming.py","description":"Fix audio filename in config"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":261,"cache_read_input_tokens":66910,"output_tokens":127,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":261,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":127,"cache_read_input_tokens":66910,"cache_creation_input_tokens":261,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":261},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kn53pc5u4mBEPGze33","type":"assistant","uuid":"45713c12-2972-4f20-b2ae-be0b858e1c6b","timestamp":"2026-06-15T21:28:32.053Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"9a70e4fe-04d6-479e-95e3-e6b4bd5a220e","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01GbiFW7ScdDEaZUXzqDd8HK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MLw6nzLH3nHvRWKrPvUYro","name":"Bash","input":{"command":"cd /mnt/DATA/WORK/LAM_Audio2Expression && $HOME/miniconda3/bin/conda run -n lam_a2e python inference_streaming_audio.py 2>&1 | grep -v FutureWarning | grep -v \"deprecated\\|Deprecated\\|0it\"","description":"Test Audio2Expression inference with correct audio file","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":145,"cache_read_input_tokens":67171,"output_tokens":168,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":145,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":168,"cache_read_input_tokens":67171,"cache_creation_input_tokens":145,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":145},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5knNMd1JwkgLozjvF8S","type":"assistant","uuid":"b775e98f-3d05-4d28-a2f4-2d8d1a700723","timestamp":"2026-06-15T21:28:37.550Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"b775e98f-3d05-4d28-a2f4-2d8d1a700723","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MLw6nzLH3nHvRWKrPvUYro","type":"tool_result","content":"  _torch_pytree._register_pytree_node(\n  _torch_pytree._register_pytree_node(\n[2026-06-15 23:28:39,591 INFO infer.py line 51 42700] => Loading config ...\n[2026-06-15 23:28:39,591 INFO infer.py line 58 42700] => Building model ...\n[2026-06-15 23:28:40,025 INFO infer.py line 66 42700] Num params: 97912596\n[2026-06-15 23:28:40,321 INFO infer.py line 73 42700] Loading weight at: pretrained_models/lam_audio2exp_streaming.tar\n[2026-06-15 23:28:40,483 INFO infer.py line 85 42700] => Loaded weight 'pretrained_models/lam_audio2exp_streaming.tar'\n\n  0%|          | 0/8 [00:00<?, ?it/s]\n 12%|█▎        | 1/8 [00:00<00:06,  1.12it/s]\n100%|██████████| 8/8 [00:00<00:00,  8.71it/s]\nInference time 0.8898270130157471\nInference time 0.004354715347290039\nInference time 0.004106283187866211\nInference time 0.0041484832763671875\nInference time 0.003996849060058594\nInference time 0.00394749641418457\nInference time 0.003906965255737305\nInference time 0.003914356231689453\nShell cwd was reset to /home/wildlama","is_error":false}]},"uuid":"028f2581-f92f-439e-a8e0-a00150c7dd7f","timestamp":"2026-06-15T21:28:47.465Z","toolUseResult":{"stdout":"  _torch_pytree._register_pytree_node(\n  _torch_pytree._register_pytree_node(\n[2026-06-15 23:28:39,591 INFO infer.py line 51 42700] => Loading config ...\n[2026-06-15 23:28:39,591 INFO infer.py line 58 42700] => Building model ...\n[2026-06-15 23:28:40,025 INFO infer.py line 66 42700] Num params: 97912596\n[2026-06-15 23:28:40,321 INFO infer.py line 73 42700] Loading weight at: pretrained_models/lam_audio2exp_streaming.tar\n[2026-06-15 23:28:40,483 INFO infer.py line 85 42700] => Loaded weight 'pretrained_models/lam_audio2exp_streaming.tar'\n\n  0%|          | 0/8 [00:00<?, ?it/s]\n 12%|█▎        | 1/8 [00:00<00:06,  1.12it/s]\n100%|██████████| 8/8 [00:00<00:00,  8.71it/s]\nInference time 0.8898270130157471\nInference time 0.004354715347290039\nInference time 0.004106283187866211\nInference time 0.0041484832763671875\nInference time 0.003996849060058594\nInference time 0.00394749641418457\nInference time 0.003906965255737305\nInference time 0.003914356231689453","stderr":"\nShell cwd was reset to /home/wildlama","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"b775e98f-3d05-4d28-a2f4-2d8d1a700723","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"028f2581-f92f-439e-a8e0-a00150c7dd7f","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01VGmV3vrWv9HR5gKPVggciR","type":"message","role":"assistant","content":[{"type":"text","text":"**Audio2Expression fonctionne parfaitement** — après le premier chunk (0.89s de warm-up), chaque seconde d'audio se traite en **4ms**. C'est du vrai temps réel."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":614,"cache_read_input_tokens":67316,"output_tokens":267,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":614,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":267,"cache_read_input_tokens":67316,"cache_creation_input_tokens":614,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":614},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5koPEjbZhZEjaoDchZ6","type":"assistant","uuid":"d73817cf-9e80-479e-8be6-3827f2520d24","timestamp":"2026-06-15T21:28:52.483Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"d73817cf-9e80-479e-8be6-3827f2520d24","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01VGmV3vrWv9HR5gKPVggciR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01J12aAprAj1aEPC7mjdcz6N","name":"Bash","input":{"command":"ls /mnt/DATA/WORK/LAM_Audio2Expression/bsData.json && python3 -c \"import json; d=json.load(open('/mnt/DATA/WORK/LAM_Audio2Expression/bsData.json')); print('Frames:', len(d['blendshapes'])); print('Channels:', len(d['blendshapes'][0]) if d['blendshapes'] else 0); print('Sample frame keys:', list(d['blendshapes'][0].keys())[:5] if d['blendshapes'] else [])\"","description":"Check generated blendshape JSON output"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":614,"cache_read_input_tokens":67316,"output_tokens":267,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":614,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":267,"cache_read_input_tokens":67316,"cache_creation_input_tokens":614,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":614},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5koPEjbZhZEjaoDchZ6","type":"assistant","uuid":"bb99e037-f27b-4744-b7d2-9eb228818f90","timestamp":"2026-06-15T21:28:53.959Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"bb99e037-f27b-4744-b7d2-9eb228818f90","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n/mnt/DATA/WORK/LAM_Audio2Expression/bsData.json\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import json; d=json.load(open('/mnt/DATA/WORK/LAM_Audio2Expression/bsData.json')); print('Frames:', len(d['blendshapes'])); print('Channels:', len(d['blendshapes'][0]) if d['blendshapes'] else 0); print('Sample frame keys:', list(d['blendshapes'][0].keys())[:5] if d['blendshapes'] else [])\n                                                                                                            ~^^^^^^^^^^^^^^^\nKeyError: 'blendshapes'","is_error":true,"tool_use_id":"toolu_01J12aAprAj1aEPC7mjdcz6N"}]},"uuid":"bed43cd3-8d8d-4702-b9b4-382c70bcd18e","timestamp":"2026-06-15T21:28:56.945Z","toolUseResult":"Error: Exit code 1\n/mnt/DATA/WORK/LAM_Audio2Expression/bsData.json\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    import json; d=json.load(open('/mnt/DATA/WORK/LAM_Audio2Expression/bsData.json')); print('Frames:', len(d['blendshapes'])); print('Channels:', len(d['blendshapes'][0]) if d['blendshapes'] else 0); print('Sample frame keys:', list(d['blendshapes'][0].keys())[:5] if d['blendshapes'] else [])\n                                                                                                            ~^^^^^^^^^^^^^^^\nKeyError: 'blendshapes'","sourceToolAssistantUUID":"bb99e037-f27b-4744-b7d2-9eb228818f90","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"bed43cd3-8d8d-4702-b9b4-382c70bcd18e","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01VmtuTEEKNmKZTcvgc63m46","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011TmVup599UVPcFQDuwwXVX","name":"Bash","input":{"command":"python3 -c \"import json; d=json.load(open('/mnt/DATA/WORK/LAM_Audio2Expression/bsData.json')); print(type(d)); print(list(d.keys())[:5] if isinstance(d, dict) else len(d)); print(str(d)[:300])\"","description":"Inspect blendshape JSON structure"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":472,"cache_read_input_tokens":67930,"output_tokens":153,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":472,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":153,"cache_read_input_tokens":67930,"cache_creation_input_tokens":472,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":472},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kp6ZAWifJy8ppc9eMr","type":"assistant","uuid":"0929e387-6784-4f3a-843b-e41ca942b176","timestamp":"2026-06-15T21:29:01.191Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"0929e387-6784-4f3a-843b-e41ca942b176","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011TmVup599UVPcFQDuwwXVX","type":"tool_result","content":"<class 'dict'>\n['names', 'metadata', 'frames']\n{'names': ['browDownLeft', 'browDownRight', 'browInnerUp', 'browOuterUpLeft', 'browOuterUpRight', 'cheekPuff', 'cheekSquintLeft', 'cheekSquintRight', 'eyeBlinkLeft', 'eyeBlinkRight', 'eyeLookDownLeft', 'eyeLookDownRight', 'eyeLookInLeft', 'eyeLookInRight', 'eyeLookOutLeft', 'eyeLookOutRight', 'eyeLo","is_error":false}]},"uuid":"e8b6e158-8211-46a0-9cab-d550f0b2d1d4","timestamp":"2026-06-15T21:29:04.374Z","toolUseResult":{"stdout":"<class 'dict'>\n['names', 'metadata', 'frames']\n{'names': ['browDownLeft', 'browDownRight', 'browInnerUp', 'browOuterUpLeft', 'browOuterUpRight', 'cheekPuff', 'cheekSquintLeft', 'cheekSquintRight', 'eyeBlinkLeft', 'eyeBlinkRight', 'eyeLookDownLeft', 'eyeLookDownRight', 'eyeLookInLeft', 'eyeLookInRight', 'eyeLookOutLeft', 'eyeLookOutRight', 'eyeLo","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"0929e387-6784-4f3a-843b-e41ca942b176","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"e8b6e158-8211-46a0-9cab-d550f0b2d1d4","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01JVcGLTYnBBD5rYe1JwKiCx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LJkGHPjM9AsGHqDarDzKQF","name":"Bash","input":{"command":"python3 -c \"\nimport json\nd = json.load(open('/mnt/DATA/WORK/LAM_Audio2Expression/bsData.json'))\nprint('Channels:', len(d['names']))\nprint('Frames:', len(d['frames']))\nprint('FPS metadata:', d.get('metadata', {}))\nprint('Frame 0 sample:', dict(list(zip(d['names'][:5], d['frames'][0][:5]))  ))\n\"","description":"Show blendshape data structure"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":312,"cache_read_input_tokens":68402,"output_tokens":187,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":312,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":187,"cache_read_input_tokens":68402,"cache_creation_input_tokens":312,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":312},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kpdns8hJeVdxRZ17Cx","type":"assistant","uuid":"7be837b0-b3bf-4a7f-bd35-9e39705d4330","timestamp":"2026-06-15T21:29:08.135Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"7be837b0-b3bf-4a7f-bd35-9e39705d4330","isSidechain":false,"promptId":"02a81344-2b75-4954-a681-60a307bafecf","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n  File \"<string>\", line 7, in <module>\n    print('Frame 0 sample:', dict(list(zip(d['names'][:5], d['frames'][0][:5]))  ))\n                                                           ~~~~~~~~~~~~~~^^^^\nKeyError: slice(None, 5, None)\nChannels: 52\nFrames: 240\nFPS metadata: {'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":true,"tool_use_id":"toolu_01LJkGHPjM9AsGHqDarDzKQF"}]},"uuid":"9437b8d2-9336-4028-a402-0b0ddb3ec821","timestamp":"2026-06-15T21:29:12.130Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"<string>\", line 7, in <module>\n    print('Frame 0 sample:', dict(list(zip(d['names'][:5], d['frames'][0][:5]))  ))\n                                                           ~~~~~~~~~~~~~~^^^^\nKeyError: slice(None, 5, None)\nChannels: 52\nFrames: 240\nFPS metadata: {'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']}","sourceToolAssistantUUID":"7be837b0-b3bf-4a7f-bd35-9e39705d4330","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"9437b8d2-9336-4028-a402-0b0ddb3ec821","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_01G6iAQqq9X1FHYSBBzhxFE9","type":"message","role":"assistant","content":[{"type":"text","text":"Tout fonctionne parfaitement. Voici le résumé de ce qu'on a :\n\n---\n\n**Audio2Expression — opérationnel ✅**\n- **52 canaux ARKit** (browDownLeft, jawOpen, mouthSmileLeft, etc.)\n- **240 frames à 30 FPS** pour 8 secondes d'audio\n- **4ms par seconde d'audio** après warm-up — parfaitement temps réel\n\n---\n\n## La suite : intégration dans agent-gui\n\nIl reste 3 étapes :\n\n1. **Exporter ton avatar** depuis une photo via `app_lam.py` (nécessite Blender 4.0+)\n2. **Ajouter WebSocket + TTS + Audio2Expression** dans `agent_gui/app.py`\n3. **Remplacer le canvas pixel-art** par le renderer WebGL Gaussian Splatting\n\nTu as Blender d'installé ? (`blender --version` dans ton terminal). C'est nécessaire pour générer l'avatar 3D depuis ta photo."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":737,"cache_read_input_tokens":68714,"output_tokens":265,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":737,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":265,"cache_read_input_tokens":68714,"cache_creation_input_tokens":737,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":737},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kqCswXw2uFtaLuhM6v","type":"assistant","uuid":"e580e15b-4f62-4880-af6d-aeba58d58552","timestamp":"2026-06-15T21:29:20.732Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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{"parentUuid":"79153b8a-b2f2-4c20-a60e-2908f942bf71","isSidechain":false,"message":{"model":"claude-sonnet-4-6","id":"msg_014YVXR1hvPFBCJVZD9YnYWy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01P4pqhF1aamfSj2YyEKwFDU","name":"Write","input":{"file_path":"/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  type: project\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"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":316,"cache_read_input_tokens":69898,"output_tokens":1722,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":316,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":1722,"cache_read_input_tokens":69898,"cache_creation_input_tokens":316,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":316},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Cc5kzUbzpSLqg5UakjAJN","type":"assistant","uuid":"d52402c8-2b09-4495-9812-79fae945a870","timestamp":"2026-06-15T21:31:52.610Z","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
{"parentUuid":"d52402c8-2b09-4495-9812-79fae945a870","isSidechain":false,"promptId":"50c61cc0-c88c-47ae-85c0-70ad44defad7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01P4pqhF1aamfSj2YyEKwFDU","type":"tool_result","content":"File created successfully at: /home/wildlama/.claude/projects/-home-wildlama/memory/project_agent_gui_lam.md (file state is current in your context — no need to Read it back)"}]},"uuid":"3ef12a66-43b4-4fd3-81ec-679e54f20a61","timestamp":"2026-06-15T21:31:52.649Z","toolUseResult":{"type":"create","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","structuredPatch":[],"originalFile":null,"userModified":false},"sourceToolAssistantUUID":"d52402c8-2b09-4495-9812-79fae945a870","userType":"external","entrypoint":"cli","cwd":"/home/wildlama","sessionId":"7de9b929-9050-430b-9b36-f74849c1c0a7","version":"2.1.177","gitBranch":"HEAD","slug":"majestic-finding-fern"}
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