"""gr.Textbox() component.""" from __future__ import annotations 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.events import Events if TYPE_CHECKING: from gradio.components import Timer from gradio.events import Dependency @document() class Textbox(FormComponent): """ Creates a textarea for user to enter string input or display string output. Demos: hello_world, diff_texts, sentence_builder Guides: creating-a-chatbot, real-time-speech-recognition """ EVENTS = [ Events.change, Events.input, Events.select, Events.submit, Events.focus, Events.blur, Events.stop, ] def __init__( self, value: str | Callable | None = None, *, lines: int = 1, max_lines: int = 20, placeholder: str | None = None, label: str | None = None, info: str | 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 = True, elem_id: str | None = None, autofocus: bool = False, autoscroll: bool = True, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | None = None, type: Literal["text", "password", "email"] = "text", text_align: Literal["left", "right"] | None = None, rtl: bool = False, show_copy_button: bool = False, max_length: int | None = None, submit_btn: str | bool | None = False, stop_btn: str | bool | None = False, ): """ Parameters: value: default text to provide in textarea. If callable, the function will be called whenever the app loads to set the initial value of the component. lines: minimum number of line rows to provide in textarea. max_lines: maximum number of line rows to provide in textarea. placeholder: placeholder hint to provide behind textarea. 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. info: additional component description, appears below the label in smaller font. Supports markdown / HTML syntax. every: Continously 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. If False, the copy button is hidden as well as well as the 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 rendered as an editable textbox; 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. autofocus: If True, will focus on the textbox when the page loads. Use this carefully, as it can cause usability issues for sighted and non-sighted users. 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: if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved. type: The type of textbox. One of: 'text', 'password', 'email', Default is 'text'. text_align: How to align the text in the textbox, can be: "left", "right", or None (default). If None, the alignment is left if `rtl` is False, or right if `rtl` is True. Can only be changed if `type` is "text". rtl: If True and `type` is "text", sets the direction of the text to right-to-left (cursor appears on the left of the text). Default is False, which renders cursor on the right. show_copy_button: If True, includes a copy button to copy the text in the textbox. Only applies if show_label is True. autoscroll: If True, will automatically scroll to the bottom of the textbox when the value changes, unless the user scrolls up. If False, will not scroll to the bottom of the textbox when the value changes. max_length: maximum number of characters (including newlines) allowed in the textbox. If None, there is no maximum length. submit_btn: If False, will not show a submit button. If True, will show a submit button with an icon. If a string, will use that string as the submit button text. When the submit button is shown, the border of the textbox will be removed, which is useful for creating a chat interface. """ if type not in ["text", "password", "email"]: raise ValueError('`type` must be one of "text", "password", or "email".') self.lines = lines if type == "text": self.max_lines = max(lines, max_lines) else: self.max_lines = 1 self.placeholder = placeholder self.show_copy_button = show_copy_button self.submit_btn = submit_btn self.stop_btn = stop_btn self.autofocus = autofocus self.autoscroll = autoscroll 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, value=value, ) self.type = type self.rtl = rtl self.text_align = text_align self.max_length = max_length def preprocess(self, payload: str | None) -> str | None: """ Parameters: payload: the text entered in the textarea. Returns: Passes text value as a {str} into the function. """ return None if payload is None else str(payload) def postprocess(self, value: str | None) -> str | None: """ Parameters: value: Expects a {str} returned from function and sets textarea value to it. Returns: The value to display in the textarea. """ return None if value is None else str(value) def api_info(self) -> dict[str, Any]: return {"type": "string"} def example_payload(self) -> Any: return "Hello!!" def example_value(self) -> Any: return "Hello!!" from typing import Callable, Literal, Sequence, Any, TYPE_CHECKING from gradio.blocks import Block if TYPE_CHECKING: from gradio.components import Timer 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 | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", 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 | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True, ) -> 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, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. 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 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: continously 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. show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False. """ ... 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 | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", 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 | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True, ) -> 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, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. 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 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: continously 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. show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False. """ ... def select(self, fn: Callable[..., Any] | None = None, inputs: Block | Sequence[Block] | set[Block] | None = None, outputs: Block | Sequence[Block] | None = None, api_name: str | None | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", 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 | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True, ) -> 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, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. 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 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: continously 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. show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False. """ ... 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 | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", 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 | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True, ) -> 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, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. 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 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: continously 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. show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False. """ ... 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 | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", 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 | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True, ) -> 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, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. 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 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: continously 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. show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False. """ ... 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 | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", 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 | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True, ) -> 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, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. 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 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: continously 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. show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False. """ ... def stop(self, fn: Callable[..., Any] | None = None, inputs: Block | Sequence[Block] | set[Block] | None = None, outputs: Block | Sequence[Block] | None = None, api_name: str | None | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", 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 | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True, ) -> 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, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. 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 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: continously 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. show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False. """ ...