# `gradio_folium` PyPI - Version Display Interactive Maps Created with Folium ## Installation ```bash pip install gradio_folium ``` ## Usage ```python 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 ```python Any ``` None None
height ```python int | None ``` None None
label ```python str | None ``` None None
container ```python bool ``` True None
scale ```python int | None ``` None None
min_width ```python int | None ``` None None
visible ```python bool ``` True None
elem_id ```python str | None ``` None None
elem_classes ```python list[str] | str | None ``` None None
render ```python bool ``` True None
load_fn ```python Callable[Ellipsis, Any] | None ``` None None
every ```python 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. ```python def predict( value: Unknown ) -> folium.folium.Map: return value ```