Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import plotly.graph_objects as go
+from sklearn.linear_model import LinearRegression
+from datetime import timedelta
+
+def plot_and_predict(zip, prediction_days):
+    # Read and process the real estate data from Zillow
+    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')
+    df = df[df['RegionName'] == int(zip)]
+    df = df.loc[:, '2000-01-31':]
+    df = df.T.reset_index()
+    df.columns = ['Date', 'Price']
+    df['Date'] = pd.to_datetime(df['Date'])
+
+    # Train linear regression model
+    df['Timestamp'] = (df['Date'] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1D')
+    X = df['Timestamp'].values.reshape(-1, 1)
+    y = df['Price'].values
+    model = LinearRegression()
+    model.fit(X, y)
+
+    # Predict future prices
+    last_timestamp = df['Timestamp'].iloc[-1]
+    future_timestamps = [last_timestamp + i for i in range(1, prediction_days + 1)]
+    predicted_prices = model.predict(pd.np.array(future_timestamps).reshape(-1, 1))
+
+    # Prepare data for plotting
+    historical_prices_trace = go.Scatter(
+        x=df['Date'],
+        y=df['Price'],
+        mode="lines",
+        name="Historical Prices"
+    )
+    future_dates = [df['Date'].iloc[-1] + timedelta(days=i) for i in range(1, prediction_days + 1)]
+    predicted_prices_trace = go.Scatter(
+        x=future_dates,
+        y=predicted_prices,
+        mode="lines",
+        name="Predicted Prices"
+    )
+
+    # Plot data
+    fig = go.Figure()
+    fig.add_trace(historical_prices_trace)
+    fig.add_trace(predicted_prices_trace)
+    fig.update_layout(
+        title=f"Real Estate Price Prediction for Zip Code {zip}",
+        xaxis_title="Date",
+        yaxis_title="Price",
+        legend_title_text="Data"
+    )
+
+    return fig
+
+# Gradio interface
+interface = gr.Interface(
+    fn=plot_and_predict,
+    inputs=[
+        gr.Textbox(label="ZIP Code"),
+        gr.Slider(minimum=1, maximum=365, step=1, label="Prediction Days"),
+    ],
+    outputs="plot"
+)
+
+# Launch the app
+interface.launch(debug=True)