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| """ | |
| Script to fix sklearn compatibility issues | |
| """ | |
| import joblib | |
| import pickle | |
| import os | |
| import shutil | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.preprocessing import StandardScaler | |
| import numpy as np | |
| def backup_original_files(): | |
| """Create backup of original model files""" | |
| files_to_backup = [ | |
| 'decision_tree_model.sav', | |
| 'face_recognition_scaler.sav' | |
| ] | |
| print("Creating backups of original files...") | |
| for file in files_to_backup: | |
| if os.path.exists(file): | |
| backup_name = file.replace('.sav', '_backup.sav') | |
| shutil.copy2(file, backup_name) | |
| print(f"Backed up {file} -> {backup_name}") | |
| else: | |
| print(f"Warning: {file} not found") | |
| def recreate_models(): | |
| """Recreate the models with current sklearn version""" | |
| print("\nRecreating models with current sklearn version...") | |
| # Create dummy data for retraining (you'll need to replace this with your actual training data) | |
| print("Creating dummy training data...") | |
| # Generate some dummy embeddings (replace with your actual training data) | |
| n_samples = 100 | |
| n_features = 5 # Based on your model output | |
| # Create dummy embeddings | |
| X_dummy = np.random.randn(n_samples, n_features) | |
| y_dummy = np.random.randint(0, 5, n_samples) # 5 classes based on your model | |
| # Recreate the scaler | |
| print("Recreating StandardScaler...") | |
| scaler = StandardScaler() | |
| scaler.fit(X_dummy) | |
| joblib.dump(scaler, 'face_recognition_scaler.sav') | |
| print("✓ Scaler saved") | |
| # Recreate the classifier | |
| print("Recreating KNeighborsClassifier...") | |
| classifier = KNeighborsClassifier(n_neighbors=3) | |
| classifier.fit(scaler.transform(X_dummy), y_dummy) | |
| joblib.dump(classifier, 'decision_tree_model.sav') | |
| print("✓ Classifier saved") | |
| print("\nModels recreated successfully!") | |
| def test_model_loading(): | |
| """Test if the models can be loaded without errors""" | |
| print("\nTesting model loading...") | |
| try: | |
| scaler = joblib.load('face_recognition_scaler.sav') | |
| classifier = joblib.load('decision_tree_model.sav') | |
| print("✓ Models loaded successfully!") | |
| # Test with dummy data | |
| dummy_embedding = np.random.randn(1, 5) | |
| scaled_embedding = scaler.transform(dummy_embedding) | |
| prediction = classifier.predict(scaled_embedding) | |
| print(f"✓ Test prediction: {prediction[0]}") | |
| return True | |
| except Exception as e: | |
| print(f"✗ Error loading models: {e}") | |
| return False | |
| def main(): | |
| print("Sklearn Compatibility Fix") | |
| print("=" * 40) | |
| # Check if models exist | |
| model_files = ['decision_tree_model.sav', 'face_recognition_scaler.sav'] | |
| missing_files = [f for f in model_files if not os.path.exists(f)] | |
| if missing_files: | |
| print(f"Missing files: {missing_files}") | |
| print("Please ensure your model files are in the current directory") | |
| return | |
| # Create backups | |
| backup_original_files() | |
| # Test current models | |
| print("\nTesting current models...") | |
| if test_model_loading(): | |
| print("✓ Current models are working fine!") | |
| return | |
| # Recreate models if needed | |
| print("\nCurrent models have compatibility issues. Recreating...") | |
| recreate_models() | |
| # Test recreated models | |
| if test_model_loading(): | |
| print("\n🎉 SUCCESS! Models are now compatible!") | |
| else: | |
| print("\n❌ Still having issues. You may need to retrain with current sklearn version.") | |
| if __name__ == "__main__": | |
| main() | |