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app.py

@@ -58,18 +58,13 @@ def predict_total_sales_batch():
     file = request.files['file']
     input_data = pd.read_csv(file)
 
-    # Predict log sales using full pipeline
-    predicted_log_sales = model.predict(input_data).tolist()
-
-    # Convert log sales to actual sales
-    predicted_sales = np.exp(predicted_log_sales)
-    predicted_sales = [round(float(s), 2) for s in predicted_sales]
+    # Make predictions for the batch data and convert raw predictions into a readable format
+    predicted_sales = [x for x in model.predict(input_data.drop("Product_Id",axis=1)).tolist()]
 
     return jsonify({'Predicted Sales for Each Row': predicted_sales})
 
-    # 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(Product_Id, predicted_sales))  # Use actual sales total
+    product_id = input_data.Product_Id.values.tolist()
+    output_dict = dict(zip(product_id, predicted_sales))
 
     # Return the predictions dictionary as a JSON response
     return output_dict