kermitting soon

requirements.txt

@@ -7,3 +7,4 @@ numpy==1.26.0
 scikit-learn==1.3.1
 selenium==4.27.1
 webdriver-manager== 4.0.2
+joblib

util/app.py

@@ -8,6 +8,7 @@ import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 from helper import * 
+import joblib
 
 
 # Define champion list for dropdowns
@@ -43,6 +44,13 @@ except Exception as e:
     print(f"Error loading model: {e}")
     model = None
 
+# Load the saved LabelEncoder
+try:
+    label_encoder = joblib.load('label_encoder.joblib')
+except Exception as e:
+    print(f"Error loading label encoder: {e}")
+    label_encoder = None
+
 # Functions
 def get_user_training_df(player_opgg_url):
     try:
@@ -82,7 +90,7 @@ def prepare_training_df(df, target_column='champion', stratify_columns=['champio
     
     category_mappings = {}
     temp_encoded_df = df.copy()
-    
+    '''
     # Convert categorical columns to codes but keep original data
     for col in categorical_columns:
         if col in df.columns:
@@ -91,7 +99,7 @@ def prepare_training_df(df, target_column='champion', stratify_columns=['champio
                 'ordered': df[col].cat.ordered
             }
             temp_encoded_df[col] = df[col].cat.codes
-    
+    '''
     # Remove combinations with too few samples
     combo_counts = df['stratify_label'].value_counts()
     valid_combos = combo_counts[combo_counts >= min_samples_per_class].index
@@ -138,6 +146,7 @@ def prepare_training_df(df, target_column='champion', stratify_columns=['champio
 
     print("X_val: ", X_val, "\n X_val type: ", type(X_val), "\n y_val: ", y_val, "\n y_val type: ", type(y_val))
     
+    '''
     # Restore categorical dtypes
     for col in categorical_columns:
         if col in X_train.columns:
@@ -156,7 +165,7 @@ def prepare_training_df(df, target_column='champion', stratify_columns=['champio
                 categories=category_mappings[col]['categories'],
                 ordered=category_mappings[col]['ordered']
             )
-    
+    '''
     
     return X_train, X_val, X_test, y_train, y_val, y_test, label_encoder
 
@@ -207,6 +216,8 @@ def predict_champion(player_opgg_url, *champions):
     try:
         if model is None:
             return "Model not loaded properly"
+        if label_encoder is None:
+            return "Label encoder not loaded properly"
         
         print("============= Inside predict_champion(): Model loaded properly=================\n")
 
@@ -226,8 +237,8 @@ def predict_champion(player_opgg_url, *champions):
             target_column='champion',
             stratify_columns=['champion', 'region'],
             min_samples_per_class=5,
-            train_size=0.6,
-            val_size=0.2,
+            train_size=0,
+            val_size=1,
             random_state=42
         )
         print("type(X_test): ", type(X_test), "\n")
@@ -241,9 +252,13 @@ def predict_champion(player_opgg_url, *champions):
 
         prediction = model.predict(X_test)
 
-        print("prediction", prediction , "\n")
+        print("prediction: ", prediction , "\n")
+        
+        # Decode predictions (if using LabelEncoder)
+        decoded_preds = label_encoder.inverse_transform(prediction)
+        print("decoded_preds: ", decoded_preds, "\n")
 
-        return f"Predicted champion: {prediction}"
+        return f"Predicted champion: {decoded_preds}"
     except Exception as e:
         return f"Error making prediction: {e}"
 

util/label_encoder.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38ec04acda4d7987202f744bcc40b7d02d8374f55c17f5701840320e6b07ff29
+size 1541