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app.py

@@ -83,8 +83,8 @@ def predict_store_total_sales_batch():
     predicted_log_total_sales = model.predict(input_data).tolist()
 
     # Calculate actual prices
-    predicted_store_total_sales = [round(float(np.exp(log_total_sales)), 2) for log_total_sales in predicted_log_total_sales]
-
+    #predicted_store_total_sales = [round(float(np.exp(log_total_sales)), 2) for log_total_sales in predicted_log_total_sales]
+    predicted_store_total_sales = predicted_log_total_sales
     # Create a dictionary of predictions with Product Id as Unique keys for each record
     product_ids = input_data['Product_Id'].tolist()  # Assuming 'id' is the Product ID column
     output_dict = dict(zip(product_ids, predicted_store_total_sales))  # Use actual prices