tags: [gradio-custom-component, Slider, leaderboards, forms, filtering, slider] title: gradio_rangeslider short_description: 🛝 Slider component for selecting a range of values colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: app.py

gradio_rangeslider

🛝 Slider component for selecting a range of values

Installation

pip install gradio_rangeslider

Usage

import gradio as gr
from gradio_rangeslider import RangeSlider
from pathlib import Path

text = "## The range is: {min} to {max}"

docs = Path(__file__).parent / "docs.md"

with gr.Blocks() as demo:
    with gr.Tabs():
        with gr.Tab("Demo"):
            gr.Markdown("""## 🛝 RangeSlider

            ## Drag either end and see the selected endpoints update in real-time.
            """) 
            range_slider = RangeSlider(minimum=0, maximum=100, value=(0, 100))
            range_ = gr.Markdown(value=text.format(min=0, max=100))
            range_slider.change(lambda s: text.format(min=s[0], max=s[1]), range_slider, range_,
                                show_progress="hide", trigger_mode="always_last")
            gr.Slider(label="Normal slider", minimum=0, maximum=100, value=50, interactive=True)
            gr.Examples([(20, 30), (40, 80)], inputs=[range_slider])
        with gr.Tab("Docs"):
            gr.Markdown(docs.read_text())


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

RangeSlider

Initialization

float

float

typing.Union[
    typing.Tuple[float, float], typing.Callable, NoneType
][typing.Tuple[float, float][float, float], Callable, None]

float | None

str | None

str | None

float | None

bool | None

bool

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 slider value as a {float} into the function.
• As input: Should return, expects an {int} or {float} returned from function and sets slider value to it as long as it is within range (otherwise, sets to minimum value).

def predict(
    value: typing.Tuple[float, float][float, float]
) -> typing.Optional[typing.Tuple[float, float]][
   typing.Tuple[float, float][float, float], None
]:
    return value