kermitting soon

util/app.py

@@ -207,6 +207,8 @@ def show_stats(player_opgg_url):
 
 def predict_champion(player_opgg_url, *champions):
     """Make prediction based on selected champions"""
+
+    print("==================== Inside: predict_champion() ===================== \n")
     if not player_opgg_url or None in champions:
         return "Please fill in all fields"
     
@@ -219,6 +221,7 @@ def predict_champion(player_opgg_url, *champions):
 
         # Get and process the data
         training_df = get_user_training_df(player_opgg_url)
+        print("training_df retrieved: ", training_df, "\n")
         
         if isinstance(training_df, str):  # Error message
             return training_df
@@ -226,28 +229,36 @@ def predict_champion(player_opgg_url, *champions):
         # Apply necessary transformations
         training_df = convert_df(training_df)
         training_df = apply_feature_engineering(training_df)
+        print("training_df converted and feature engineered: ", training_df, "\n")
         
         # Get feature columns (excluding champion and region)
         feature_columns = [col for col in training_df.columns 
                          if col not in ['champion', 'region', 'stratify_label']]
         X = training_df[feature_columns]
+        print("Got feature columns X: ", X, "\n")
         
         # Handle categorical features
         categorical_columns = X.select_dtypes(include=['category']).columns
         X_processed = X.copy()
+        print("Handled categorical features, X_processed = ", X_processed, "\n")
         
         # Convert categorical columns to numeric
         for col in categorical_columns:
             X_processed[col] = X_processed[col].cat.codes
+        print("Converted categorical columns to numeric: ", categorical_columns, "\n")
             
         # Convert to float32
         X_processed = X_processed.astype('float32')
+        print("Converted X_processed to float32: ", X_processed, "\n")
         
         # Create DMatrix with categorical feature support
         dtest = DMatrix(X_processed, enable_categorical=True)
+        print("Converted to Dmatrix: ", dtest, "\n")
         
         # Make prediction
+        print("Starting model prediction...\n")
         predictions = model.predict(dtest)
+        print("Model prediction complete\n")
         
         # Get the highest probability prediction
         if len(predictions.shape) > 1:
@@ -257,8 +268,10 @@ def predict_champion(player_opgg_url, *champions):
         
         # Decode predictions using loaded label encoder
         decoded_preds = label_encoder.inverse_transform(pred_indices)
+        print("Decoded predictions: ", decoded_preds, "\n")
         
         # Return the first prediction
+        print("==================== Exiting: predict_champion()===================\n")
         return f"Predicted champion: {decoded_preds[0]}"
     
     except Exception as e: