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app.py

@@ -89,7 +89,7 @@ def predict_sales_batch():
     predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
 
     # Create a dictionary of predictions with property IDs as keys
-    property_ids = input_data['id'].tolist()  # Assuming 'id' is the property ID column
+    property_ids = input_data['Store_Id'].tolist()  # Assuming 'id' is the property ID column
     output_dict = dict(zip(property_ids, predicted_prices))  # Use actual prices
 
     # Return the predictions dictionary as a JSON response