Create model/label_encoders.pkl

model/label_encoders.pkl

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+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+import joblib
+import os
+
+# Sample dataset (replace with your actual data)
+data = {
+    'face_shape': ['Oval', 'Round', 'Square', 'Oval', 'Round', 'Square'] * 10,
+    'skin_tone': ['Fair', 'Medium', 'Dark', 'Medium', 'Dark', 'Fair'] * 10,
+    'face_size': ['Small', 'Medium', 'Large', 'Medium', 'Large', 'Small'] * 10,
+    'mask_style': ['Glitter', 'Animal', 'Floral', 'Animal', 'Glitter', 'Floral'] * 10
+}
+df = pd.DataFrame(data)
+
+# Initialize and fit LabelEncoders
+encoders = {
+    'face_shape': LabelEncoder().fit(df['face_shape'].unique()),
+    'skin_tone': LabelEncoder().fit(df['skin_tone'].unique()),
+    'face_size': LabelEncoder().fit(df['face_size'].unique()),
+    'mask_style': LabelEncoder().fit(df['mask_style'].unique())
+}
+
+# Create model directory if not exists
+os.makedirs('model', exist_ok=True)
+
+# Save encoders dictionary
+joblib.dump(encoders, 'model/label_encoders.pkl')
+
+# Verify encoding
+print("Label Encoders Saved Successfully!")
+print("Face Shape Classes:", encoders['face_shape'].classes_)
+print("Skin Tone Classes:", encoders['skin_tone'].classes_)
+print("Face Size Classes:", encoders['face_size'].classes_)
+print("Mask Style Classes:", encoders['mask_style'].classes_)