text: import data_management as dm
  import config
  
  
  def make_prediction(*, path_to_images) -> float:
      """Make a prediction using the saved model pipeline."""
  
      # Load data
      # create a dataframe with columns = ['image', 'target']
      # column "image" contains path to image
      # columns target can contain all zeros, it doesn't matter
  
      dataframe = path_to_images  # needs to load as above described
      pipe = dm.load_pipeline_keras()
      predictions = pipe.pipe.predict(dataframe)
      #response = {'predictions': predictions, 'version': _version}
  
      return predictions
  
  
  if __name__ == '__main__':
  
      from sklearn.externals import joblib
  
      images_df = dm.load_image_paths(config.DATA_FOLDER)
      X_train, X_test, y_train, y_test = dm.get_train_test_target(images_df)
  
      pipe = joblib.load(config.PIPELINE_PATH)
  
      predictions = pipe.predict(X_test)
      print(predictions)
  