from __future__ import annotations from typing import Any, Callable from gradio.components.base import FormComponent from gradio.events import Events from gradio.events import Dependency class HighlightedTextbox(FormComponent): """ Creates a textarea for user to enter string input or display string output where some elements are highlighted. Preprocessing: passes a list of tuples as a {List[Tuple[str, float | str | None]]]} into the function. If no labels are provided, the text will be displayed as a single span. Postprocessing: expects a {List[Tuple[str, float | str]]]} consisting of spans of text and their associated labels, or a {Dict} with two keys: (1) "text" whose value is the complete text, and (2) "highlights", which is a list of dictionaries, each of which have the keys: "highlight_type" (consisting of the highlight label), "start" (the character index where the label starts), and "end" (the character index where the label ends). Highlights should not overlap. """ EVENTS = [ Events.change, Events.input, Events.select, Events.submit, Events.focus, Events.blur, ] data_model = HighlightedTextData def __init__( self, value: str | Callable | None = "", *, color_map: dict[str, str] | None = None, show_legend: bool = False, show_legend_label: bool = False, legend_label: str = "", combine_adjacent: bool = False, adjacent_separator: str = "", label: str | None = None, info: str | None = None, every: float | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, visible: bool = True, elem_id: str | None = None, autofocus: bool = False, autoscroll: bool = True, interactive: bool = True, elem_classes: list[str] | str | None = None, render: bool = True, show_copy_button: bool = False, ): """ Parameters: value: default text to provide in textbox. If callable, the function will be called whenever the app loads to set the initial value of the component. lines: number of lines to display in textbox. max_lines: maximum number of lines to display in textbox. color_map: dictionary mapping labels to colors. show_legend: if True, will display legend. show_legend_label: if True, will display legend label. legend_label: label to display above legend. combine_adjacent: if True, will combine adjacent spans with the same label. adjacent_separator: separator to use when combining adjacent spans. placeholder: placeholder hint to provide behind textbox. label: component name in interface. every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute. 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 width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer. 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. 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. 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. 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. """ self.color_map = color_map self.show_legend = show_legend self.show_legend_label = show_legend_label self.legend_label = legend_label self.combine_adjacent = combine_adjacent self.adjacent_separator = adjacent_separator self.show_copy_button = show_copy_button self.autofocus = autofocus self.autoscroll = autoscroll super().__init__( label=label, info=info, show_label=show_label, container=container, scale=scale, min_width=min_width, interactive=interactive, visible=visible, elem_id=elem_id, elem_classes=elem_classes, value=value, render=render, every=every, ) def preprocess(self, payload: str | None) -> str | None: return None if payload is None else str(payload) def postprocess( self, y: HighlightedTextData | dict | None ) -> HighlightedTextData | None: """ Parameters: y: List of (word, category) tuples, or a dictionary of two keys: "text", and "highlights", which itself is a list of dictionaries, each of which have the keys: "highlight_type", "start", and "end" Returns: List of (word, category) tuples """ if y is None: return None if isinstance(y, dict): try: text = y["text"] highlights = y["highlights"] except KeyError as ke: raise ValueError( "Expected a dictionary with keys 'text' and 'highlights' " "for the value of the HighlightedText component." ) from ke if len(highlights) == 0: y = [(text, None)] else: list_format = [] index = 0 entities = sorted(highlights, key=lambda x: x["start"]) for entity in entities: list_format.append((text[index : entity["start"]], None)) highlight_type = entity.get("highlight_type") list_format.append( (text[entity["start"] : entity["end"]], highlight_type) ) index = entity["end"] list_format.append((text[index:], None)) y = list_format if self.combine_adjacent: output = [] running_text, running_category = None, None for text, category in y: if running_text is None: running_text = text running_category = category elif category == running_category: running_text += self.adjacent_separator + text elif not text: # Skip fully empty item, these get added in processing # of dictionaries. pass else: output.append((running_text, running_category)) running_text = text running_category = category if running_text is not None: output.append((running_text, running_category)) return output else: return y def example_inputs(self) -> Any: return [("Hello", None), ("world", "highlight")] def change(self, fn: Callable | None, inputs: Component | Sequence[Component] | set[Component] | None = None, outputs: Component | Sequence[Component] | 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: 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: If True, will show progress animation while pending 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: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. 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()` event) 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 to use this event. If fn is None, show_api will automatically be set to False. """ ... def input(self, fn: Callable | None, inputs: Component | Sequence[Component] | set[Component] | None = None, outputs: Component | Sequence[Component] | 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: 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: If True, will show progress animation while pending 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: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. 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()` event) 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 to use this event. If fn is None, show_api will automatically be set to False. """ ... def select(self, fn: Callable | None, inputs: Component | Sequence[Component] | set[Component] | None = None, outputs: Component | Sequence[Component] | 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: 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: If True, will show progress animation while pending 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: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. 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()` event) 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 to use this event. If fn is None, show_api will automatically be set to False. """ ... def submit(self, fn: Callable | None, inputs: Component | Sequence[Component] | set[Component] | None = None, outputs: Component | Sequence[Component] | 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: 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: If True, will show progress animation while pending 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: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. 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()` event) 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 to use this event. If fn is None, show_api will automatically be set to False. """ ... def focus(self, fn: Callable | None, inputs: Component | Sequence[Component] | set[Component] | None = None, outputs: Component | Sequence[Component] | 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: 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: If True, will show progress animation while pending 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: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. 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()` event) 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 to use this event. If fn is None, show_api will automatically be set to False. """ ... def blur(self, fn: Callable | None, inputs: Component | Sequence[Component] | set[Component] | None = None, outputs: Component | Sequence[Component] | 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: 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: If True, will show progress animation while pending 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: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. 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()` event) 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 to use this event. If fn is None, show_api will automatically be set to False. """ ...