train_model.py

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import joblib
import os

# 1. Dataset Preparation
print("=== Preparing Dataset ===")
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,
    'mask_image': ['mask_images/glitter.png', 'mask_images/animal.png', 'mask_images/floral.png'] * 50
}
df = pd.DataFrame(data)
print(f"Dataset created with {len(df)} samples")

# 2. Initialize Encoders with Image Mappings
print("\n=== Initializing Encoders ===")
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()),
    'mask_images': {
        0: 'mask_images/glitter.png',
        1: 'mask_images/animal.png',
        2: 'mask_images/floral.png'
    }
}

# 3. Feature Engineering
print("\n=== Encoding Features ===")
X = pd.DataFrame({
    'face_shape': encoders['face_shape'].transform(df['face_shape']),
    'skin_tone': encoders['skin_tone'].transform(df['skin_tone']),
    'face_size': encoders['face_size'].transform(df['face_size'])
})
y = encoders['mask_style'].transform(df['mask_style'])

# 4. Train-Test Split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)
print(f"Train samples: {len(X_train)}, Test samples: {len(X_test)}")

# 5. Model Training
print("\n=== Training Model ===")
model = RandomForestClassifier(
    n_estimators=150,  # Increased for better performance
    max_depth=7,
    min_samples_split=5,
    class_weight='balanced',
    random_state=42
)
model.fit(X_train, y_train)

# 6. Enhanced Evaluation
print("\n=== Model Evaluation ===")
print(f"Training Accuracy: {model.score(X_train, y_train):.2f}")
print(f"Test Accuracy: {model.score(X_test, y_test):.2f}")

# Feature Importance
importances = model.feature_importances_
print("\nFeature Importances:")
for col, imp in zip(X.columns, importances):
    print(f"- {col}: {imp:.3f}")

# Classification Report
print("\nDetailed Classification Report:")
print(classification_report(y_test, model.predict(X_test)))

# 7. Saving with Verification
print("\n=== Saving Assets ===")
os.makedirs('model', exist_ok=True)

# Verify mask images exist
print("\nMask Image Verification:")
for class_idx, path in encoders['mask_images'].items():
    if os.path.exists(path):
        print(f"✓ {encoders['mask_style'].classes_[class_idx]}: {path}")
    else:
        print(f"✗ Missing: {path}")

# Save models
joblib.dump(model, 'model/random_forest.pkl')
joblib.dump(encoders, 'model/label_encoders.pkl')

print("\n=== Saved Assets ===")
print("Model saved: model/random_forest.pkl")
print("Encoders saved: model/label_encoders.pkl")
print("\nClass Mappings:")
print("- Face Shapes:", list(encoders['face_shape'].classes_))
print("- Mask Styles:", list(encoders['mask_style'].classes_))
print("- Mask Images:", encoders['mask_images'])

print("\nTraining complete! Ready for deployment.")