Update app.py

app.py

@@ -2,9 +2,13 @@ import gradio as gr
 import pandas as pd
 import plotly.graph_objects as go
 import numpy as np
+from datetime import datetime
+
+# Fetch the latest data date from the CSV
+df_sample = pd.read_csv('https://files.zillowstatic.com/research/public_csvs/zhvi/Zip_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv', nrows=1)
+latest_data_date_str = df_sample.columns[-1]
 
 def plot_zip_code_correlation(zip_codes_str, start_date, end_date):
-    # Validate dates
     start_year = pd.to_datetime(start_date).year
     end_year = pd.to_datetime(end_date).year
     if start_year < 2000 or end_year < 2000:
@@ -12,87 +16,60 @@ def plot_zip_code_correlation(zip_codes_str, start_date, end_date):
     if start_year > end_year:
         raise ValueError("Start date must be before end date.")
 
-    # Process ZIP codes (ensure 5-digit format)
     zip_codes = [z.strip().zfill(5) for z in zip_codes_str.split(",")]
-
-    # Load data
     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')
-
-    # Ensure ZIP codes in dataframe are strings with leading zeros
     df['RegionName'] = df['RegionName'].astype(str).str.zfill(5)
     df = df[df['RegionName'].isin(zip_codes)]
-
     if df.empty:
         raise ValueError("No data found for the provided ZIP codes.")
 
-    # Extract date columns within the selected range
-    date_columns = []
-    for col in df.columns[7:]:
-        try:
-            date = pd.to_datetime(col)
-            if start_date <= str(date.date()) <= end_date:
-                date_columns.append(col)
-        except:
-            continue
-
+    date_columns = [col for col in df.columns[7:] if start_date <= col <= end_date]
     if not date_columns:
         raise ValueError("No data available within the selected date range.")
 
-    # Build price matrix
     price_matrix = []
     valid_zip_list = []
-
     for zip_code in zip_codes:
         df_zip = df[df['RegionName'] == zip_code]
         if not df_zip.empty:
-            prices = df_zip.loc[:, date_columns].values.flatten()
+            prices = df_zip[date_columns].values.flatten()
             if not np.isnan(prices).all():
                 price_matrix.append(prices)
                 valid_zip_list.append(zip_code)
 
     if len(price_matrix) < 2:
-        raise ValueError(f"Not enough data for correlation calculation. Ensure at least two valid ZIP codes with overlapping data between {start_date} and {end_date}.")
+        raise ValueError("Not enough data for correlation calculation.")
 
-    price_matrix_df = pd.DataFrame(price_matrix, index=valid_zip_list, columns=date_columns)
-    price_matrix_df = price_matrix_df.T.dropna()
-
-    # Calculate correlation matrix
+    price_matrix_df = pd.DataFrame(price_matrix, index=valid_zip_list, columns=date_columns).T.dropna()
     corr_matrix = price_matrix_df.corr()
-
-    # Prepare 3D plot
     z_data = corr_matrix.values
     x_data, y_data = np.meshgrid(valid_zip_list, valid_zip_list)
 
     fig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])
     fig.update_layout(
         title=f'3D Correlation Matrix of Housing Prices ({start_date} to {end_date})',
-        scene=dict(
-            xaxis_title='ZIP Code',
-            yaxis_title='ZIP Code',
-            zaxis_title='Correlation',
-        ),
+        scene=dict(xaxis_title='ZIP Code', yaxis_title='ZIP Code', zaxis_title='Correlation'),
         autosize=True
     )
-
     return fig
 
 iface = gr.Interface(
     fn=plot_zip_code_correlation,
-    gr.Markdown(
-        f"""
-        # 🇺🇸 US Real Estate Zip ZHVI Price Movement Correlation Matrix Gen
-        Track housing price correlations by ZIP code to make informed decisions as a property owner or buyer.  
-        **Data up to {latest_data_date_str}**. Enter a two-letter state abbreviation below (e.g., CA, NY, TX).
-        ### [Contact a real estate broker](https://micheled.com)
-        """
-    ),
     inputs=[
         gr.Textbox(label="Enter comma-separated ZIP codes (e.g., 07001,07002,07003)"),
         gr.Textbox(label="Start Date (YYYY-MM-DD) - No earlier than 2000"),
         gr.Textbox(label="End Date (YYYY-MM-DD) - No earlier than 2000")
     ],
     outputs=gr.Plot(),
-    title="3D ZIP Code Housing Price Correlation Matrix"
+    title="3D ZIP Code Housing Price Correlation Matrix",
+    description=f"""
+## US Real Estate Zip ZHVI Price Movement Correlation Matrix Gen
+
+Track housing price correlations by ZIP code to make informed decisions as a property owner or buyer.  
+**Data up to {latest_data_date_str}**. Enter ZIP codes below.
+
+[Contact a real estate broker](https://micheled.com)
+"""
 )
 
 iface.launch(share=False, debug=True)