gradio-sandboxcomponent 0.0.10

Python library for easily interacting with trained machine learning models

gradio_sandboxcomponent

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_sandboxcomponent

Usage

import gradio as gr
from gradio_sandboxcomponent import SandboxComponent


example = SandboxComponent().example_value()

with gr.Blocks() as demo:
    with gr.Tab("Example"):
        with gr.Row():
            gr.Markdown("## Example")
        with gr.Row():
            SandboxComponent(
                label="Example",
                value=("https://www.baidu.com/", "Hello World"),
                show_label=True
            )


if __name__ == "__main__":
    demo.launch()

SandboxComponent

Initialization

name	type	default	description
value	tuple[str, str] | Callable | None	None	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.
label	str | None	None	the label for this component, displayed above the component if `show_label` is `True` 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 corresponds to.
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 | Sequence[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.
scale	int | None	None	relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
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, 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	bool	True	If False, component will be hidden.
elem_id	str | None	None	None
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.

Events

name	description
change	Triggered when the value of the SandboxComponent 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.
input	This listener is triggered when the user changes the value of the SandboxComponent.
submit	This listener is triggered when the user presses the Enter key while the SandboxComponent is focused.

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

• As output: Is passed, passes text value as a {str} into the function.
• As input: Should return, expects a {str} returned from function and sets textarea value to it.

def predict(
    value: str | None
) -> str | None:
    return value