kermitting soon

util/app.py

@@ -134,10 +134,10 @@ def predict_champion(player_opgg_url, *champions):
 
         training_df = convert_df(training_df)
         #print("type(training_df): ", type(training_df), "\n")
-        print("check_datatypes(training_df) BEFORE feature eng. :\n", check_datatypes(training_df), "\n")
+        print("check_datatypes(training_df) BEFORE feature eng: \n", check_datatypes(training_df), "\n")
 
         training_df = apply_feature_engineering(training_df)
-        print("check_datatypes(training_df) AFTER feature eng. \n:", check_datatypes(training_df), "\n")
+        print("check_datatypes(training_df) AFTER feature eng: \n", check_datatypes(training_df), "\n")
         
         # Get feature columns
         feature_columns = [col for col in training_df.columns 
@@ -182,7 +182,7 @@ def predict_champion(player_opgg_url, *champions):
             
             print(f"Mapped to champion: {predicted_champion}")
             
-            return f"Predicted champion: {predicted_champion}"
+            return f"{predicted_champion}"
             
         except Exception as e:
             print(f"Error mapping champion ID: {e}")

util/app_training_df_getter.py

@@ -354,8 +354,8 @@ def create_champion_features_and_return_df(merged_player_stats=None, meta_stats=
 
 def create_app_user_training_df(url):
     try:
-        #meta_stats = get_meta_stats() 
-        #weekly_meta_stats = get_weekly_meta()
+        meta_stats = get_meta_stats() 
+        weekly_meta_stats = get_weekly_meta()
 
         # Input validation
         if not url or not isinstance(url, str):

util/helper.py

@@ -999,8 +999,8 @@ def calculate_champion_loyalty(df):
                              for i in range(1, 3))  # Only top 2
             confidence_score += min(0.1, recent_games / 100)
             
-            # print(f"loyalty_score, confidence score: [{loyalty_score}], [{confidence_score}]  \n")
-            # print("===================== exiting: get_loyalty_scores()===================")
+            print(f"loyalty_score, confidence score: [{loyalty_score}], [{confidence_score}]  \n")
+            print("===================== exiting: get_loyalty_scores()===================")
             return {
                 'loyalty_score': round(min(loyalty_score, 1.0), 3),
                 'confidence_score': round(min(confidence_score, 1.0), 3),