Blocks Layout
Tab

New to Gradio? Start here: Getting Started

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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Tab

gradio.Tab(···)

Description

Tab (or its alias TabItem) is a layout element. Components defined within the Tab will be visible when this tab is selected tab.

Example Usage

with gr.Blocks() as demo:
    with gr.Tab("Lion"):
        gr.Image("lion.jpg")
        gr.Button("New Lion")
    with gr.Tab("Tiger"):
        gr.Image("tiger.jpg")
        gr.Button("New Tiger")

Initialization

label: str | None

default = None

The visual label for the tab

visible: bool

default = True

If False, Tab will be hidden.

interactive: bool

default = True

If False, Tab will not be clickable.

id: int | str | None

default = None

An optional identifier for the tab, required if you wish to control the selected tab from a predict function.

elem_id: str | None

default = None

An optional string that is assigned as the id of the <div> containing the contents of the Tab layout. The same string followed by "-button" is attached to the Tab button. Can be used for targeting CSS styles.

elem_classes: list[str] | str | None

default = None

An optional string or list of strings that are assigned as the class of this component in the HTML DOM. Can be used for targeting CSS styles.

render: bool

default = True

If False, this layout will not be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.

Methods

select

gradio.Tab.select(···)

Description

Event listener for when the user selects or deselects the Tab. Uses event data gradio.SelectData to carry `value` referring to the label of the Tab, and `selected` to refer to state of the Tab. See EventData documentation on how to use this event data

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

Guides

Controlling Layout

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