kermitting soon

util/app.py

@@ -67,7 +67,7 @@ def get_user_training_df(player_opgg_url):
         #return f"Error getting training data: {e}"
 
 def prepare_training_df(df, target_column='champion', stratify_columns=['champion', 'region'], 
-                         min_samples_per_class=6, train_size=0, val_size=1, random_state=42):
+                         min_samples_per_class=6, train_size=0.6, val_size=0.2, random_state=42):
     df = df.copy()
     original_dtypes = df.dtypes.to_dict()
 
@@ -234,19 +234,40 @@ def predict_champion(player_opgg_url, *champions):
         )
         print("type(X_test): ", type(X_test), "\n")
 
-        # Convert Pandas DataFrame to DMatrix
-        X_test = DMatrix(X_test)
+        # Handle categorical features
+        categorical_columns = X_val.select_dtypes(include=['category']).columns
+        X_val_processed = X_val.copy()
+        
+        # Convert categorical columns to numeric
+        for col in categorical_columns:
+            X_val_processed[col] = X_val_processed[col].cat.codes
+            
+        # Convert to float32
+        X_val_processed = X_val_processed.astype('float32')
+        
+        # Create DMatrix with categorical feature support from pandas dataframe.
+        dtest = DMatrix(X_val_processed, enable_categorical=True)
 
         print("type(X_test) after converting to DMatrix: ", type(X_test), "\n") 
 
         print("Starting model prediction... \n")
 
-        prediction = model.predict(X_test)
+        predictions = model.predict(dtest)
+        print("Previous line: predictions = model.predict(dtest). \n prediction: ", predictions , "\n")
+
+        '''
+        # Get the highest probability prediction
+        if len(predictions.shape) > 1:
+            pred_indices = predictions.argmax(axis=1)
+        else:
+            pred_indices = predictions.astype(int)
 
-        print("prediction: ", prediction , "\n")
+        # Decode predictions using loaded label encoder
+        decoded_preds = label_encoder.inverse_transform(pred_indices)
+        '''
         
         # Decode predictions (if using LabelEncoder)
-        decoded_preds = label_encoder.inverse_transform(prediction)
+        decoded_preds = label_encoder.inverse_transform(predictions)
         print("decoded_preds: ", decoded_preds, "\n")
 
         return f"Predicted champion: {decoded_preds}"