import tensorflow as tf

print('loading best_model.h5...')
try:
    # Load without compiling to avoid deserializing legacy training configs/metrics
    m = tf.keras.models.load_model('best_model.h5', compile=False)
except Exception as e:
    print('Failed to load best_model.h5:', e)
    raise

# Try to export to the TF SavedModel format first
try:
    m.export('saved_model_age_regressor')
    print('Exported SavedModel to ./saved_model_age_regressor')
except Exception as e:
    print('Export to SavedModel failed:', e)
    # Fallback: save as Keras native single-file and HDF5 for compatibility
    try:
        m.save('saved_model_age_regressor.keras')
        print('Saved Keras model to ./saved_model_age_regressor.keras')
    except Exception as e2:
        print('Saving Keras native format failed:', e2)
    try:
        m.save('final_model.h5')
        print('Saved HDF5 model to ./final_model.h5')
    except Exception as e3:
        print('Saving HDF5 format failed:', e3)