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app.py

@@ -76,6 +76,10 @@ def sales_price_batch():
     input_data = pd.read_csv(file)
     input_data['Store_Age'] = 2025 - input_data['Store_Establishment_Year']
 
+    product_ids = input_data['Product_Id'].tolist()  # Assuming 'Id' is the product ID column
+    store_ids = input_data['Store_Id'].tolist()  # Assuming 'Id' is the store ID column
+    input_data = input_data.drop(['Product_Id', 'Store_Id', 'Store_Establishment_Year'], axis=1)
+
     # Apply same transformations
     input_transformed = preprocessor.transform(input_data)
 
@@ -86,8 +90,6 @@ def sales_price_batch():
     predicted_sales = round(float(predicted_sales), 2)
 
     # Create a dictionary of predictions with property IDs as keys
-    product_ids = input_data['Product_Id'].tolist()  # Assuming 'Id' is the product ID column
-    store_ids = input_data['Store_Id'].tolist()  # Assuming 'Id' is the store ID column
     output_dict = dict(zip(product_ids, store_ids, predicted_sales))  # Use actual prices
 
     # Return the predictions dictionary as a JSON response