Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import plotly.express as px
+
+def plot_real_estate(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_state = df[df['State'] == state.upper()]
+
+    # Extract unique ZIP codes for the state
+    zip_codes = df_state['RegionName'].unique()
+
+    # Prepare an empty figure for plotting
+    fig = px.Figure()
+
+    # Loop through each ZIP code and add a plot line
+    for zip_code in zip_codes:
+        df_zip = df_state[df_state['RegionName'] == zip_code]
+
+        # Select the columns with dates and transpose the data
+        df_zip = df_zip.loc[:, '2000-01-31':].T.reset_index()
+        df_zip.columns = ['Date', 'Price']
+
+        # Convert 'Date' to datetime
+        df_zip['Date'] = pd.to_datetime(df_zip['Date'])
+
+        # Add a line to the plot for this ZIP code
+        fig.add_scatter(x=df_zip['Date'], y=df_zip['Price'], mode='lines', name=str(zip_code))
+
+    # Update plot layout
+    fig.update_layout(title=f'Housing Prices in {state}',
+                      xaxis_title='Date',
+                      yaxis_title='Price')
+    return fig
+
+iface = gr.Interface(fn=plot_real_estate,
+                     inputs=[gr.components.Textbox(label="State (e.g., NJ for New Jersey)")],
+                     outputs=gr.Plot())
+
+iface.launch(share=False, debug=True)