def get_file_and_folder_info()

in deepracer_offroad_ws/webserver_pkg/webserver_pkg/models.py [0:0]

• 36 lines of code
• 6 McCabe index (conditional complexity)

def get_file_and_folder_info(path):
    """Helper function to get the file and folder information for the model at the
       model path sent as parameter.

    Args:
        path (str): Model directory path.

    Returns:
        dict: Dictonary with the relevant details about the model.
    """
    training_algorithm_display_name = \
        constants.TRAINING_ALGORITHM_NAME_MAPPING[constants.INVALID_ENUM_VALUE]
    action_space_type_display_name = \
        constants.ACTION_SPACE_TYPE_NAME_MAPPING[constants.INVALID_ENUM_VALUE]
    model_metadata_sensors_display_names = \
        [constants.SENSOR_INPUT_NAME_MAPPING[constants.INVALID_ENUM_VALUE]]

    size = utility.execute(["du", "-sh", path])[1].split("\t")[0]
    err_code, err_msg, model_metadata_content = \
        read_model_metadata_file(os.path.join(path, "model_metadata.json"))
    if err_code == 0:
        err_code, err_msg, model_metadata_sensors = get_sensors(model_metadata_content)
        if err_code == 0:
            model_metadata_sensors_display_names = [constants.SENSOR_INPUT_NAME_MAPPING[
                                                    constants.SensorInputKeys(sensor)]
                                                    for sensor in model_metadata_sensors]
        err_code, err_msg, training_algorithm = get_training_algorithm(model_metadata_content)
        if err_code == 0:
            training_algorithm_display_name = \
                constants.TRAINING_ALGORITHM_NAME_MAPPING[
                    constants.TrainingAlgorithms(training_algorithm)]
        err_code, err_msg, action_space_type = get_action_space_type(model_metadata_content)
        if err_code == 0:
            action_space_type_display_name = \
                constants.ACTION_SPACE_TYPE_NAME_MAPPING[
                    constants.ActionSpaceTypes(action_space_type)]
    data = {
        "name": os.path.basename(path),
        "size": size,
        "creation_time": os.path.getmtime(path),
        "status": "Ready",
        "training_algorithm": training_algorithm_display_name,
        "action_space_type": action_space_type_display_name,
        "sensors": ", ".join(model_metadata_sensors_display_names)
    }
    return data