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app.py

@@ -73,27 +73,32 @@ def sales_price_batch():
     file = request.files['file']
 
     # Read the CSV file into a Pandas DataFrame
-    input_data = pd.read_csv(file)
-    input_data['Store_Age'] = 2025 - input_data['Store_Establishment_Year']
+    input_data_batch = pd.read_csv(file)
+    input_data_batch['Store_Age'] = 2025 - input_data_batch['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)
+    product_ids = input_data_batch['Product_Id'].tolist()  # Assuming 'Id' is the product ID column
+    store_ids = input_data_batch['Store_Id'].tolist()  # Assuming 'Id' is the store ID column
+    input_data_batch = input_data_batch.drop(['Product_Id', 'Store_Id', 'Store_Establishment_Year'], axis=1)
 
     # Apply same transformations
     #input_transformed = preprocessor.transform(input_data)
 
     # Make predictions for all properties in the DataFrame (get log_prices)
-    predicted_sales = model.predict(input_data).tolist()
-
-    # Calculate sales
-    predicted_sales = round(float(predicted_sales), 2)
-
-    # Create a dictionary of predictions with property IDs as keys
-    output_dict = dict(zip(product_ids, store_ids, predicted_sales))  # Use actual prices
-
-    # Return the predictions dictionary as a JSON response
-    return output_dict
+    predictions = model.predict(input_data_batch).tolist()
+
+     # Round predictions
+    predicted_sales = [round(float(x), 2) for x in predictions]
+
+    # Structure output as a list of dicts
+        output_dict = [
+            {
+                "Product_Id": pid,
+                "Store_Id": sid,
+                "Predicted_Sales": psale
+            }
+            for pid, sid, psale in zip(product_ids, store_ids, predicted_sales)
+        ]
+        return jsonify({"predictions": output_dict})
 
 # Run the Flask application in debug mode if this script is executed directly
 if __name__ == '__main__':