1. Helpers
  2. on

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Description

Sets up an event listener that triggers a function when the specified event(s) occur. This is especially useful when the same function should be triggered by multiple events. Only a single API endpoint is generated for all events in the triggers list.

Example Usage

import gradio as gr

with gr.Blocks() as demo:
    with gr.Row():
        input = gr.Textbox()
        button = gr.Button("Submit")
    output = gr.Textbox()
    gr.on(
        triggers=[button.click, input.submit],
        fn=lambda x: x,
        inputs=[input],
        outputs=[output]
    )

demo.launch()

Initialization

Parameters
🔗 
triggers: list[EventListenerCallable] | EventListenerCallable | None
default = None

List of triggers to listen to, e.g. [btn.click, number.change]. If None, will run on app load and changes to any inputs.

🔗 
fn: Callable[..., Any] | None | Literal['decorator']
default = "decorator"

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: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default = None

List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.

🔗 
outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default = None

List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.

🔗 
api_visibility: Literal['public', 'private', 'undocumented']
default = "public"

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 clients), 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".

🔗 
api_name: str | None
default = None

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.

🔗 
api_description: str | None | Literal[False]
default = None
🔗 
scroll_to_output: bool
default = False

If True, will scroll to output component on completion

🔗 
show_progress: Literal['full', 'minimal', 'hidden']
default = "full"

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 | list[Component] | None
default = None

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: bool
default = True

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: bool
default = False

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: int
default = 4

Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)

🔗 
preprocess: bool
default = True

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: bool
default = True

If False, will not run postprocessing of component data before returning 'fn' output to the browser.

🔗 
cancels: dict[str, Any] | list[dict[str, Any]] | None
default = None

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.

🔗 
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default = None

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: str | Literal[True] | None
default = None

Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs', return should be a list of values for output components.

🔗 
concurrency_limit: int | None | Literal['default']
default = "default"

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: str | None
default = None

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.

🔗 
time_limit: int | None
default = None

The time limit for the function to run. Parameter only used for the `.stream()` event.

🔗 
stream_every: float
default = 0.5

The latency (in seconds) at which stream chunks are sent to the backend. Defaults to 0.5 seconds. Parameter only used for the `.stream()` event.

🔗 
key: int | str | tuple[int | str, ...] | None
default = None
🔗 
validator: Callable | None
default = None

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 and should return a `gr.validate()` for each input value.

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