`gradio_folium`

Display Interactive Maps Created with Folium

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]), height=400)
    data = gr.DataFrame(value=df, height=200)
    data.select(select, data, map)

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

`Folium`

Initialization

name	type	default	description
`value`	`Any`	`None`	None
`height`	`int \| None`	`None`	None
`label`	`str \| None`	`None`	None
`container`	`bool`	`True`	None
`scale`	`int \| None`	`None`	None
`min_width`	`int \| None`	`None`	None
`visible`	`bool`	`True`	None
`elem_id`	`str \| None`	`None`	None
`elem_classes`	`list[str] \| str \| None`	`None`	None
`render`	`bool`	`True`	None
`load_fn`	`Callable[Ellipsis, Any] \| None`	`None`	None
`every`	`float \| None`	`None`	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.

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