Components
ParamViewer

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To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/ac9bf5ec9208b92579f36ee94a247ae3e676c02f/gradio-5.6.0-py3-none-any.whl

*Note: Setting share=True in launch() will not work.

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Number

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ParamViewer

gradio.ParamViewer(···)

import gradio as gr with gr.Blocks() as demo: gr.Markdown("The `round()` function in Python takes two parameters") gr.ParamViewer( { "number": { "type": "int | float", "description": "The number to round", "default": None }, "ndigits": { "type": "int", "description": "The number of digits to round to", "default": "0" } } ) demo.launch()

Description

Displays an interactive table of parameters and their descriptions and default values with syntax highlighting. For each parameter, the user should provide a type (e.g. a `str`), a human-readable description, and a default value. As this component does not accept user input, it is rarely used as an input component. Internally, this component is used to display the parameters of components in the Custom Component Gallery (https://www.gradio.app/custom-components/gallery).

Behavior

As input component: (Rarely used) passes value as a `dict[str, dict]`. The key in the outer dictionary is the parameter name, while the inner dictionary has keys "type", "description", and "default" for each parameter.

Your function should accept one of these types:

def predict(
	value: dict[str, Parameter]
)
	...

As output component: Expects value as a `dict[str, dict]`. The key in the outer dictionary is the parameter name, while the inner dictionary has keys "type", "description", and "default" for each parameter.

Your function should return one of these types:

def predict(···) -> dict[str, Parameter]
	...	
	return value

Initialization

value: dict[str, Parameter] | None

default = None

A dictionary of dictionaries. The key in the outer dictionary is the parameter name, while the inner dictionary has keys "type", "description", and "default" for each parameter.

language: Literal['python', 'typescript']

default = "python"

The language to display the code in. One of "python" or "typescript".

linkify: list[str] | None

default = None

A list of strings to linkify. If any of these strings is found in the description, it will be rendered as a link.

every: Timer | float | None

default = None

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: Component | list[Component] | set[Component] | None

default = None

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.

render: bool

default = True

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: int | str | None

default = None

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.

header: str | None

default = "Parameters"

The header to display above the table of parameters, also includes a toggle button that closes/opens all details at once. If None, no header will be displayed.

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.ParamViewer`	"paramviewer"	Uses default values

Event Listeners

Description

Event listeners allow you to respond to user interactions with the UI components you've defined in a Gradio Blocks app. When a user interacts with an element, such as changing a slider value or uploading an image, a function is called.

Supported Event Listeners

The ParamViewer component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.

Listener	Description
`ParamViewer.change(fn, ···)`	Triggered when the value of the ParamViewer changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
`ParamViewer.upload(fn, ···)`	This listener is triggered when the user uploads a file into the ParamViewer.

Event Parameters

fn: Callable | 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_name: str | None | Literal[False]

default = None

defines how the endpoint appears in the API docs. Can be a string, None, or False. 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. If False, the endpoint will not be exposed in the API docs and downstream apps (including those that `gr.load` this app) will not be able to use this event.

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

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 | None

default = None

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: 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.

show_api: bool

default = True

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.

time_limit: int | None

default = None

stream_every: float

default = 0.5

like_user_message: bool

default = False

←

Number

Plot

→

ParamViewer

Description

Behavior

As input component: (Rarely used) passes value as a dict[str, dict]. The key in the outer dictionary is the parameter name, while the inner dictionary has keys "type", "description", and "default" for each parameter.

Your function should accept one of these types:

As output component: Expects value as a dict[str, dict]. The key in the outer dictionary is the parameter name, while the inner dictionary has keys "type", "description", and "default" for each parameter.

Your function should return one of these types:

Initialization

Shortcuts

Event Listeners

Description

Supported Event Listeners

Event Parameters

As input component: (Rarely used) passes value as a `dict[str, dict]`. The key in the outer dictionary is the parameter name, while the inner dictionary has keys "type", "description", and "default" for each parameter.

As output component: Expects value as a `dict[str, dict]`. The key in the outer dictionary is the parameter name, while the inner dictionary has keys "type", "description", and "default" for each parameter.