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import gradio as gr
import plotly.graph_objects as go
from datasets import load_dataset
# Load dataset once
dataset = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train")
df = dataset.to_pandas()
def filter_map(min_price, max_price, boroughs):
# Handle empty boroughs list
if not boroughs:
boroughs = ["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"]
# Filter dataframe
filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) &
(df['price'] > min_price) & (df['price'] < max_price)]
# Handle empty results
if filtered_df.empty:
# Return empty map
fig = go.Figure()
fig.update_layout(
title="No properties found with current filters",
mapbox_style="open-street-map",
mapbox=dict(
center=go.layout.mapbox.Center(lat=40.67, lon=-73.90),
zoom=9
),
)
return fig
# Prepare data for map
names = filtered_df["name"].tolist()
prices = filtered_df["price"].tolist()
text_list = [(names[i], prices[i]) for i in range(len(names))]
# Create map
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,
color='red',
opacity=0.7
),
hoverinfo="text",
hovertemplate='<b>Name</b>: %{customdata[0]}<br><b>Price</b>: $%{customdata[1]}<extra></extra>'
))
fig.update_layout(
title=f"Found {len(filtered_df)} properties",
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
),
height=600
)
return fig
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
min_price = gr.Number(value=250, 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:")
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)
demo.launch()
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