Reverted commit 31626ba + Removed log statements

Commit 31626ba included changes related to vulnerability fixes, hence reverted them.

Had added some log statements related to scalability fixes for model loading.
Removed them and will add in separate PR.

emission/analysis/classification/inference/labels/inferrers.py

@@ -6,7 +6,6 @@
 import copy
 import time
 import arrow
-from uuid import UUID
 
 import emission.analysis.modelling.tour_model_first_only.load_predict as lp
 import emission.analysis.modelling.trip_model.run_model as eamur
@@ -172,19 +171,14 @@ def predict_cluster_confidence_discounting(trip_list, max_confidence=None, first
     user_id_list = []
     for trip in trip_list:
         user_id_list.append(trip['user_id'])
-    error_message = f"""
-            Multiple user_ids found for trip_list, expected unique user_id for all trips.
-            Unique user_ids count = {len(set(user_id_list))}
-            {set(user_id_list)}
-        """
-    assert user_id_list.count(user_id_list[0]) == len(user_id_list), error_message
+    assert user_id_list.count(user_id_list[0]) == len(user_id_list), "Multiple user_ids found for trip_list, expected unique user_id for all trips"
     # Assertion successful, use unique user_id
     user_id = user_id_list[0]
 
     # load model
     start_model_load_time = time.process_time()
     model = eamur._load_stored_trip_model(user_id, model_type, model_storage)
-    logging.debug(f"{arrow.now()} Inside predict_cluster_confidence_discounting: Model load time = {time.process_time() - start_model_load_time}")
+    print(f"{arrow.now()} Inside predict_labels_n: Model load time = {time.process_time() - start_model_load_time}")
 
     labels_n_list = eamur.predict_labels_with_n(trip_list, model)
     predictions_list = []

emission/analysis/modelling/trip_model/run_model.py

@@ -109,7 +109,7 @@ def predict_labels_with_n(
     """
 
     predictions_list = []
-    logging.debug(f"{arrow.now()} Inside predict_labels_n: Predicting...")
+    print(f"{arrow.now()} Inside predict_labels_n: Predicting...")
     start_predict_time = time.process_time()
     for trip in trip_list:
         if model is None:
@@ -118,7 +118,7 @@ def predict_labels_with_n(
         else:
             predictions, n = model.predict(trip)
             predictions_list.append((predictions, n))
-    logging.debug(f"{arrow.now()} Inside predict_labels_n: Predictions complete for trip_list in time = {time.process_time() - start_predict_time}")
+    print(f"{arrow.now()} Inside predict_labels_n: Predictions complete for trip_list in time = {time.process_time() - start_predict_time}")
     return predictions_list