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

gradio_folium

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_folium

Usage

import gradio as gr
from gradio_folium import Folium
from folium import Map
import pandas as pd
import pathlib

df = pd.read_csv(pathlib.Path(__file__).parent / "cities.csv")

def select(df, data: gr.SelectData):
    row = df.iloc[data.index[0], :]
    return Map(location=[row['Latitude'], row['Longitude']])

with gr.Blocks() as demo:
    gr.Markdown(("# 🗺️ Explore World Capitals with Gradio and Folium\n"
                 "Install this custom component with `pip install gradio_folium`"))
    map = Folium(value=Map(location=[25.7617, -80.1918]))
    data = gr.DataFrame(value=df)
    data.select(select, data, map)

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

Folium

Initialization

typing.Any

int | None

str | None

bool

int | None

int | None

bool

str | None

list[str] | str | None

bool

typing.Optional[typing.Callable[..., typing.Any]][
    typing.Callable[..., typing.Any][Ellipsis, typing.Any],
    None,
]

float | None

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 input: Should return, the output data received by the component from the user's function in the backend.

def predict(
    value: Unknown
) -> folium.folium.Map:
    return value