tags: [gradio-custom-component, SimpleDropdown] title: gradio_testcomponent short_description: A gradio custom component colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: space.py

gradio_testcomponent

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_testcomponent

Usage

import gradio as gr
from gradio_testcomponent import TestComponent


example = TestComponent().example_value()

demo = gr.Interface(
    lambda x:x,
    TestComponent(),  # interactive version of your component
    TestComponent(),  # static version of your component
    # examples=[[example]],  # uncomment this line to view the "example version" of your component
)


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

TestComponent

Initialization

list[str | int | float | tuple[str, str | int | float]]
    | None

str | int | float | Callable | None

str | None

str | None

float | None

bool | None

int | None

int

bool | None

bool

str | None

list[str] | str | None

bool

Events

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 the value of the selected dropdown choice as a str | int | float.

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