Update model/random_forest.pkl

model/random_forest.pkl

@@ -1,36 +1,40 @@
-# Re-run this corrected version of train_model.py
-import pandas as pd
+# train_model.py (corrected)
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import LabelEncoder
 import joblib
+import pandas as pd
 import os
 
-# Create fresh data
+# 1. Create sample data
 data = {
-    'face_shape': ['Oval', 'Round', 'Square'] * 50,
-    'skin_tone': ['Fair', 'Medium', 'Dark'] * 50,
-    'face_size': ['Small', 'Medium', 'Large'] * 50,
-    'mask_style': ['Glitter', 'Animal', 'Floral'] * 50
+    'face_shape': ['Oval', 'Round', 'Square'] * 100,
+    'skin_tone': ['Fair', 'Medium', 'Dark'] * 100,
+    'face_size': ['Small', 'Medium', 'Large'] * 100,
+    'mask_style': ['StyleA', 'StyleB', 'StyleC'] * 100
 }
 df = pd.DataFrame(data)
 
-# Create and save new encoders
-encoders = {col: LabelEncoder().fit(df[col].unique()) 
-           for col in ['face_shape', 'skin_tone', 'face_size', 'mask_style']}
+# 2. Create and fit encoders
+encoders = {
+    col: LabelEncoder().fit(df[col]) 
+    for col in ['face_shape', 'skin_tone', 'face_size', 'mask_style']
+}
 
-# Train new model
-model = RandomForestClassifier(n_estimators=100, random_state=42)
+# 3. Train model
+model = RandomForestClassifier(
+    n_estimators=50,
+    random_state=42,
+    max_depth=5
+)
 model.fit(
     pd.DataFrame({col: encoders[col].transform(df[col]) 
                 for col in ['face_shape', 'skin_tone', 'face_size']}),
     encoders['mask_style'].transform(df['mask_style'])
 )
 
-# Save properly
+# 4. Save with protocol=4 for compatibility
 os.makedirs('model', exist_ok=True)
-joblib.dump(model, 'model/random_forest.pkl')
-joblib.dump(encoders, 'model/label_encoders.pkl')
+joblib.dump(model, 'model/random_forest.pkl', protocol=4)
+joblib.dump(encoders, 'model/label_encoders.pkl', protocol=4)
 
-# Add this to your app.py before loading
-print("Current directory:", os.listdir())
-print("Model directory:", os.listdir('model'))
+print("Model saved successfully!")