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app.py

@@ -76,14 +76,11 @@ def predict_store_sales_batch():
     input_data = pd.read_csv(file)
 
     # Make predictions for all properties in the DataFrame (get store_saless)
-    predicted_store_saless = model.predict(input_data).tolist()
-
-    # Calculate actual prices
-    predicted_saless = [round(float((store_sales)), 2) for store_sales in predicted_store_saless]
+    predicted_store_sales = model.predict(input_data.drop("Product_Id",axis=1)).tolist()
 
     # Create a dictionary of predictions with property IDs as keys
-    property_ids = input_data['id'].tolist()  # Assuming 'id' is the property ID column
-    output_dict = dict(zip(property_ids, predicted_saless))  # Use actual prices
+    property_ids = input_data['Product_Id'].tolist()  # Assuming 'id' is the property ID column
+    output_dict = dict(zip(property_ids, predicted_sales))  # Use actual prices
 
     # Return the predictions dictionary as a JSON response
     return output_dict