"""gr.Number() component.""" from __future__ import annotations import json from collections.abc import Callable, Sequence from typing import TYPE_CHECKING, Any, Literal from gradio_client.documentation import document from gradio.components.base import Component, FormComponent from gradio.components.button import Button from gradio.events import Events from gradio.exceptions import Error from gradio.i18n import I18nData from gradio.utils import set_default_buttons if TYPE_CHECKING: from gradio.components import Timer from gradio.events import Dependency @document() class Number(FormComponent): """ Creates a numeric field for user to enter numbers as input or display numeric output. Demos: tax_calculator, blocks_simple_squares """ EVENTS = [Events.change, Events.input, Events.submit, Events.focus, Events.blur] def __init__( self, value: float | Callable | None = None, *, label: str | I18nData | None = None, placeholder: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal["hidden"] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", buttons: list[Button] | None = None, precision: int | None = None, minimum: float | None = None, maximum: float | None = None, step: float = 1, ): """ Parameters: value: default value. If None, the component will be empty and show the `placeholder` if is set. If no `placeholder` is set, the component will show 0. If a function is provided, the function will be called each time the app loads to set the initial value of this component. label: the label for this component, displayed above the component if `show_label` is `True` and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component corresponds to. placeholder: placeholder hint to provide behind number input. info: additional component description, appears below the label in smaller font. Supports markdown / HTML syntax. every: Continuously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer. inputs: Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change. show_label: if True, will display label. container: If True, will place the component in a container - providing some extra padding around the border. scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True. min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first. interactive: if True, will be editable; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output. visible: If False, component will be hidden. If "hidden", component will be visually hidden and not take up space in the layout but still exist in the DOM elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles. elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles. render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later. key: in a gr.render, Components with the same key across re-renders are treated as the same component, not a new component. Properties set in 'preserved_by_key' are not reset across a re-render. preserved_by_key: A list of parameters from this component's constructor. Inside a gr.render() function, if a component is re-rendered with the same key, these (and only these) parameters will be preserved in the UI (if they have been changed by the user or an event listener) instead of re-rendered based on the values provided during constructor. precision: Precision to round input/output to. If set to 0, will round to nearest integer and convert type to int. If None, no rounding happens. minimum: Minimum value. Only applied when component is used as an input. If a user provides a smaller value, a gr.Error exception is raised by the backend. maximum: Maximum value. Only applied when component is used as an input. If a user provides a larger value, a gr.Error exception is raised by the backend. step: The interval between allowed numbers in the component. Can be used along with optional parameters `minimum` and `maximum` to create a range of legal values starting from `minimum` and incrementing according to this parameter. buttons: A list of gr.Button() instances to show in the top right corner of the component. Custom buttons will appear in the toolbar with their configured icon and/or label, and clicking them will trigger any .click() events registered on the button. """ self.precision = precision self.minimum = minimum self.maximum = maximum self.step = step self.placeholder = placeholder super().__init__( label=label, info=info, every=every, inputs=inputs, show_label=show_label, container=container, scale=scale, min_width=min_width, interactive=interactive, visible=visible, elem_id=elem_id, elem_classes=elem_classes, render=render, key=key, preserved_by_key=preserved_by_key, value=value, ) self.buttons = set_default_buttons(buttons, None) @staticmethod def round_to_precision(num: float | int, precision: int | None) -> float | int: """ Round to a given precision. If precision is None, no rounding happens. If 0, num is converted to int. Parameters: num: Number to round. precision: Precision to round to. Returns: rounded number or the original number if precision is None """ if precision is None: return num elif precision == 0: return int(round(num, precision)) else: return round(num, precision) @staticmethod def raise_if_out_of_bounds( num: float | int, minimum: float | int | None, maximum: float | int | None ) -> None: if minimum is not None and num < minimum: raise Error(f"Value {num} is less than minimum value {minimum}.") if maximum is not None and num > maximum: raise Error(f"Value {num} is greater than maximum value {maximum}.") def preprocess(self, payload: float | None) -> float | int | None: """ Parameters: payload: the field value. Returns: Passes field value as a `float` or `int` into the function, depending on `precision`. """ if payload is None: return None self.raise_if_out_of_bounds(payload, self.minimum, self.maximum) return self.round_to_precision(payload, self.precision) def postprocess(self, value: float | int | None) -> float | int | None: """ Parameters: value: Expects an `int` or `float` returned from the function and sets field value to it. Returns: The (optionally rounded) field value as a `float` or `int` depending on `precision`. """ if value is None: return None return self.round_to_precision(value, self.precision) def api_info(self) -> dict[str, str]: # type: ignore[override] if self.precision == 0: return {"type": "integer"} return {"type": "number"} def example_payload(self) -> Any: return self.round_to_precision( 3 if self.minimum is None else self.minimum, self.precision ) def example_value(self) -> Any: return self.round_to_precision( 3 if self.minimum is None else self.minimum, self.precision ) def read_from_flag(self, payload: str): """Numbers are stored as strings in the flagging file, so we need to parse them as json.""" return json.loads(payload) from typing import Callable, Literal, Sequence, Any, TYPE_CHECKING from gradio.blocks import Block if TYPE_CHECKING: from gradio.components import Timer from gradio.components.base import Component def change(self, fn: Callable[..., Any] | None = None, inputs: Block | Sequence[Block] | set[Block] | None = None, outputs: Block | Sequence[Block] | None = None, api_name: str | None = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", show_progress_on: Component | Sequence[Component] | None = None, queue: bool | None = None, batch: bool = False, max_batch_size: int = 4, preprocess: bool = True, postprocess: bool = True, cancels: dict[str, Any] | list[dict[str, Any]] | None = None, every: Timer | float | None = None, trigger_mode: Literal["once", "multiple", "always_last"] | None = None, js: str | Literal[True] | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, api_visibility: Literal["public", "private", "undocumented"] = "public", key: int | str | tuple[int | str, ...] | None = None, api_description: str | None | Literal[False] = None, validator: Callable[..., Any] | None = None, ) -> Dependency: """ Parameters: fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component. inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. api_name: defines how the endpoint appears in the API docs. Can be a string or None. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. scroll_to_output: if True, will scroll to output component on completion show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components. queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app. batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component. max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component). postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser. cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. every: continuously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer. trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete. js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components. concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default). concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit. api_visibility: controls the visibility and accessibility of this endpoint. Can be "public" (shown in API docs and callable by clients), "private" (hidden from API docs and not callable by the Gradio client libraries), or "undocumented" (hidden from API docs but callable by clients and via gr.load). If fn is None, api_visibility will automatically be set to "private". key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical. api_description: Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs. validator: Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function. """ ... def input(self, fn: Callable[..., Any] | None = None, inputs: Block | Sequence[Block] | set[Block] | None = None, outputs: Block | Sequence[Block] | None = None, api_name: str | None = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", show_progress_on: Component | Sequence[Component] | None = None, queue: bool | None = None, batch: bool = False, max_batch_size: int = 4, preprocess: bool = True, postprocess: bool = True, cancels: dict[str, Any] | list[dict[str, Any]] | None = None, every: Timer | float | None = None, trigger_mode: Literal["once", "multiple", "always_last"] | None = None, js: str | Literal[True] | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, api_visibility: Literal["public", "private", "undocumented"] = "public", key: int | str | tuple[int | str, ...] | None = None, api_description: str | None | Literal[False] = None, validator: Callable[..., Any] | None = None, ) -> Dependency: """ Parameters: fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component. inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. api_name: defines how the endpoint appears in the API docs. Can be a string or None. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. scroll_to_output: if True, will scroll to output component on completion show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components. queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app. batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component. max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component). postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser. cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. every: continuously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer. trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete. js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components. concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default). concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit. api_visibility: controls the visibility and accessibility of this endpoint. Can be "public" (shown in API docs and callable by clients), "private" (hidden from API docs and not callable by the Gradio client libraries), or "undocumented" (hidden from API docs but callable by clients and via gr.load). If fn is None, api_visibility will automatically be set to "private". key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical. api_description: Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs. validator: Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function. """ ... def submit(self, fn: Callable[..., Any] | None = None, inputs: Block | Sequence[Block] | set[Block] | None = None, outputs: Block | Sequence[Block] | None = None, api_name: str | None = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", show_progress_on: Component | Sequence[Component] | None = None, queue: bool | None = None, batch: bool = False, max_batch_size: int = 4, preprocess: bool = True, postprocess: bool = True, cancels: dict[str, Any] | list[dict[str, Any]] | None = None, every: Timer | float | None = None, trigger_mode: Literal["once", "multiple", "always_last"] | None = None, js: str | Literal[True] | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, api_visibility: Literal["public", "private", "undocumented"] = "public", key: int | str | tuple[int | str, ...] | None = None, api_description: str | None | Literal[False] = None, validator: Callable[..., Any] | None = None, ) -> Dependency: """ Parameters: fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component. inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. api_name: defines how the endpoint appears in the API docs. Can be a string or None. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. scroll_to_output: if True, will scroll to output component on completion show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components. queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app. batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component. max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component). postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser. cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. every: continuously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer. trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete. js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components. concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default). concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit. api_visibility: controls the visibility and accessibility of this endpoint. Can be "public" (shown in API docs and callable by clients), "private" (hidden from API docs and not callable by the Gradio client libraries), or "undocumented" (hidden from API docs but callable by clients and via gr.load). If fn is None, api_visibility will automatically be set to "private". key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical. api_description: Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs. validator: Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function. """ ... def focus(self, fn: Callable[..., Any] | None = None, inputs: Block | Sequence[Block] | set[Block] | None = None, outputs: Block | Sequence[Block] | None = None, api_name: str | None = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", show_progress_on: Component | Sequence[Component] | None = None, queue: bool | None = None, batch: bool = False, max_batch_size: int = 4, preprocess: bool = True, postprocess: bool = True, cancels: dict[str, Any] | list[dict[str, Any]] | None = None, every: Timer | float | None = None, trigger_mode: Literal["once", "multiple", "always_last"] | None = None, js: str | Literal[True] | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, api_visibility: Literal["public", "private", "undocumented"] = "public", key: int | str | tuple[int | str, ...] | None = None, api_description: str | None | Literal[False] = None, validator: Callable[..., Any] | None = None, ) -> Dependency: """ Parameters: fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component. inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. api_name: defines how the endpoint appears in the API docs. Can be a string or None. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. scroll_to_output: if True, will scroll to output component on completion show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components. queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app. batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component. max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component). postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser. cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. every: continuously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer. trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete. js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components. concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default). concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit. api_visibility: controls the visibility and accessibility of this endpoint. Can be "public" (shown in API docs and callable by clients), "private" (hidden from API docs and not callable by the Gradio client libraries), or "undocumented" (hidden from API docs but callable by clients and via gr.load). If fn is None, api_visibility will automatically be set to "private". key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical. api_description: Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs. validator: Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function. """ ... def blur(self, fn: Callable[..., Any] | None = None, inputs: Block | Sequence[Block] | set[Block] | None = None, outputs: Block | Sequence[Block] | None = None, api_name: str | None = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", show_progress_on: Component | Sequence[Component] | None = None, queue: bool | None = None, batch: bool = False, max_batch_size: int = 4, preprocess: bool = True, postprocess: bool = True, cancels: dict[str, Any] | list[dict[str, Any]] | None = None, every: Timer | float | None = None, trigger_mode: Literal["once", "multiple", "always_last"] | None = None, js: str | Literal[True] | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, api_visibility: Literal["public", "private", "undocumented"] = "public", key: int | str | tuple[int | str, ...] | None = None, api_description: str | None | Literal[False] = None, validator: Callable[..., Any] | None = None, ) -> Dependency: """ Parameters: fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component. inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. api_name: defines how the endpoint appears in the API docs. Can be a string or None. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. scroll_to_output: if True, will scroll to output component on completion show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components. queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app. batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component. max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component). postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser. cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. every: continuously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer. trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete. js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components. concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default). concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit. api_visibility: controls the visibility and accessibility of this endpoint. Can be "public" (shown in API docs and callable by clients), "private" (hidden from API docs and not callable by the Gradio client libraries), or "undocumented" (hidden from API docs but callable by clients and via gr.load). If fn is None, api_visibility will automatically be set to "private". key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical. api_description: Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs. validator: Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function. """ ...