Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import plotly.graph_objects as go
+import numpy as np
+
+def plot_real_estate_correlation(state):
+    # Read the CSV file
+    df = pd.read_csv('https://files.zillowstatic.com/research/public_csvs/zhvi/Zip_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv')
+
+    # Filter for the given state
+    df = df[df['State'] == state.upper()]
+
+    # Extract the list of ZIP codes and price data
+    zip_codes = df['RegionName'].unique()
+    price_data = df.iloc[:, 7:]  # Assuming price data starts from the 8th column
+
+    # Drop rows with missing data to avoid issues in correlation calculation
+    price_data = price_data.dropna(axis=1, how='all')
+
+    # Calculate the correlation matrix for ZIP codes
+    corr_matrix = price_data.T.corr()
+
+    # Prepare the grid data for 3D plot
+    x_data, y_data = np.meshgrid(zip_codes, zip_codes)
+    z_data = corr_matrix.values
+
+    # Create the 3D surface plot
+    fig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])
+
+    # Update plot layout
+    fig.update_layout(
+        title=f'3D Correlation Matrix of Housing Prices in {state}',
+        scene=dict(
+            xaxis_title='ZIP Code',
+            yaxis_title='ZIP Code',
+            zaxis_title='Correlation',
+        ),
+        autosize=True
+    )
+
+    return fig
+
+iface = gr.Interface(fn=plot_real_estate_correlation,
+                     inputs=[gr.components.Textbox(label="State (e.g., 'NJ' for New Jersey)")],
+                     outputs=gr.Plot())
+
+iface.launch(share=False, debug=True)