import gradio as gr import plotly.graph_objects as go from datasets import load_dataset dataset = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train") df = dataset.to_pandas() def filter_map(min_price, max_price, boroughs): filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) & (df['price'] > min_price) & (df['price'] < max_price)] names = filtered_df["name"].tolist() prices = filtered_df["price"].tolist() text_list = [(names[i], prices[i]) for i in range(0, len(names))] fig = go.Figure(go.Scattermapbox( customdata=text_list, lat=filtered_df['latitude'].tolist(), lon=filtered_df['longitude'].tolist(), mode='markers', marker=go.scattermapbox.Marker( size=6 ), hoverinfo="text", hovertemplate='Name: %{customdata[0]}
Price: $%{customdata[1]}' )) fig.update_layout( mapbox_style="open-street-map", hovermode='closest', mapbox=dict( bearing=0, center=go.layout.mapbox.Center( lat=40.67, lon=-73.90 ), pitch=0, zoom=9 ), ) return fig with gr.Blocks() as demo: with gr.Column(): with gr.Row(): min_price = gr.Number(value=500, label="Minimum Price") max_price = gr.Number(value=1000, label="Maximum Price") boroughs = gr.CheckboxGroup(choices=["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"], value=["Queens", "Brooklyn"], label="Select Boroughs or Hoods (my edit):") btn = gr.Button(value="Update Filter") map = gr.Plot() demo.load(filter_map, [min_price, max_price, boroughs], map) btn.click(filter_map, [min_price, max_price, boroughs], map) if __name__ == "__main__": demo.launch()