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app.py

@@ -78,7 +78,7 @@ def predict_sales_batch():
     and returns the predicted  product sales prices as a dictionary in the JSON response.
     """
 
-    #print(">>> Batch endpoint invoked!", flush=True)
+    print(">>> Batch endpoint invoked!", flush=True)
     product_sales_predictor_api.logger.info('Batch endpoint invoked')
 
     # Get the uploaded CSV file from the request
@@ -99,7 +99,7 @@ def predict_sales_batch():
     ]
 
      # Drop only columns that exist in the CSV
-    input_data = df.drop(columns=[c for c in drop_cols if c in input_data.columns])
+    input_data = input_data.drop(columns=[c for c in drop_cols if c in input_data.columns])
 
     # Now df contains ONLY the features used for training
     predictions = model.predict(input_data)