Introducing Gradio Clients
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gradio.CheckboxGroup(···)
list[str | int | float]
or their indices as a list[int]
into the function, depending on type
.def predict(
value: list[str | int | float] | list[int | None]
)
...
list[str | int | float]
of values or a single str | int | float
value, the checkboxes with these values are checked.def predict(···) -> list[str | int | float] | str | int | float | None
...
return value
choices: list[str | int | float | tuple[str, str | int | float]] | None
= None
A list of string or numeric options to select from. An option can also be a tuple of the form (name, value), where name is the displayed name of the checkbox button and value is the value to be passed to the function, or returned by the function.
value: list[str | float | int] | str | float | int | Callable | None
= None
Default selected list of options. If a single choice is selected, it can be passed in as a string or numeric type. If callable, the function will be called whenever the app loads to set the initial value of the component.
type: Literal['value', 'index']
= "value"
Type of value to be returned by component. "value" returns the list of strings of the choices selected, "index" returns the list of indices of the choices selected.
label: str | None
= None
The label for this component. Appears above the component 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 is assigned to.
info: str | None
= None
Additional component description.
every: Timer | float | None
= 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
= 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.
show_label: bool | None
= None
If True, will display label.
container: bool
= True
If True, will place the component in a container - providing some extra padding around the border.
scale: int | None
= None
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: int
= 160
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: bool | None
= None
If True, choices in this checkbox group will be checkable; if False, checking will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
visible: bool
= True
If False, component will be hidden.
elem_id: str | None
= None
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: list[str] | str | None
= None
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: bool
= 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
= 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.
Class | Interface String Shortcut | Initialization |
---|---|---|
| "checkboxgroup" | Uses default values |
import gradio as gr
def sentence_builder(quantity, animal, countries, place, activity_list, morning):
return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
demo = gr.Interface(
sentence_builder,
[
gr.Slider(2, 20, value=4, label="Count", info="Choose between 2 and 20"),
gr.Dropdown(
["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
),
gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
gr.Dropdown(
["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
),
gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
],
"text",
examples=[
[2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
[4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
[10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
[8, "cat", ["Pakistan"], "zoo", ["ate"], True],
]
)
if __name__ == "__main__":
demo.launch()
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.
The CheckboxGroup component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.
Listener | Description |
---|---|
| Triggered when the value of the CheckboxGroup 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 |
| This listener is triggered when the user changes the value of the CheckboxGroup. |
| Event listener for when the user selects or deselects the CheckboxGroup. Uses event data gradio.SelectData to carry |
fn: Callable | None | Literal['decorator']
= "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
= 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
= 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]
= 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
= False
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
= "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
= 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
= 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
= 4
Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
preprocess: bool
= 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
= 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
= 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
= 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
= 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"
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
= 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
= 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
= None
stream_every: float
= 0.5
like_user_message: bool
= False