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app.py

@@ -82,14 +82,22 @@ def predict_product_sale_price_batch():
 
     # Create a dictionary of predictions with product IDs as keys
     product_ids = input_data['Product_Id'].tolist()  # Assuming 'id' is the product ID column
-    output_dict = dict(zip(product_ids, predicted_prices))  # Use actual prices
 
     # Create a dictionary of predictions with Store IDs as keys
     store_ids = input_data['Store_Id'].tolist()
-    output_dict = dict(zip(store_ids, predicted_prices))  # Use actual prices
 
-    # Return the predictions dictionary as a JSON response
-    return output_dict
+    # Build predictions with both Product and Store IDs
+    output_list = []
+
+    for pid, sid, price in zip(product_ids, store_ids, predicted_prices):
+        output_list.append({
+            "Product_Id": pid,
+            "Store_Id": sid,
+            "Predicted_Revenue": round(float(price), 2)
+        })
+
+    # Return as JSON response
+    return jsonify({"predictions": output_list})
 
 # Run the Flask application in debug mode if this script is executed directly
 if __name__ == '__main__':