def METHOD_NAME(_):
  if not os.path.exists(FLAGS.model_path):
    raise ValueError(
        "Model file does not exist. Please check the .tflite model path.")
  if not os.path.exists(FLAGS.img_path):
    raise ValueError("Image file does not exist. Please check the image path.")
  tflm_interpreter = runtime.Interpreter.from_file(FLAGS.model_path)
  category_probabilities = predict_image(tflm_interpreter, FLAGS.img_path)
  predicted_category = np.argmax(category_probabilities)
  logging.info("Model predicts the image as %i with probability %.2f",
               predicted_category, category_probabilities[predicted_category])