How to use the questionary.Choice function in questionary

def _ask_if_quit(sender_id: Text, endpoint: EndpointConfig) -> bool:
    """Display the exit menu.

    Return `True` if the previous question should be retried."""

    answer = questionary.select(
        message="Do you want to stop?",
        choices=[Choice("Continue", "continue"),
                 Choice("Undo Last", "undo"),
                 Choice("Fork", "fork"),
                 Choice("Start Fresh", "restart"),
                 Choice("Export & Quit", "quit")]).ask()

    if not answer or answer == "quit":
        # this is also the default answer if the user presses Ctrl-C
        _write_data_to_file(sender_id, endpoint)
        sys.exit()
    elif answer == "continue":
        # in this case we will just return, and the original
        # question will get asked again
        return True
    elif answer == "undo":
        raise UndoLastStep()
    elif answer == "fork":
        raise ForkTracker()

def _ask_if_quit(sender_id: Text, endpoint: EndpointConfig) -> bool:
    """Display the exit menu.

    Return `True` if the previous question should be retried."""

    answer = questionary.select(
        message="Do you want to stop?",
        choices=[Choice("Continue", "continue"),
                 Choice("Undo Last", "undo"),
                 Choice("Fork", "fork"),
                 Choice("Start Fresh", "restart"),
                 Choice("Export & Quit", "quit")]).ask()

    if not answer or answer == "quit":
        # this is also the default answer if the user presses Ctrl-C
        _write_data_to_file(sender_id, endpoint)
        sys.exit()
    elif answer == "continue":
        # in this case we will just return, and the original
        # question will get asked again
        return True
    elif answer == "undo":
        raise UndoLastStep()
    elif answer == "fork":

def _ask_if_quit(sender_id: Text, endpoint: EndpointConfig) -> bool:
    """Display the exit menu.

    Return `True` if the previous question should be retried."""

    answer = questionary.select(
        message="Do you want to stop?",
        choices=[Choice("Continue", "continue"),
                 Choice("Undo Last", "undo"),
                 Choice("Fork", "fork"),
                 Choice("Start Fresh", "restart"),
                 Choice("Export & Quit", "quit")]).ask()

    if not answer or answer == "quit":
        # this is also the default answer if the user presses Ctrl-C
        story_path, nlu_path, domain_path = _request_export_info()

        tracker = retrieve_tracker(endpoint, sender_id)
        evts = tracker.get("events", [])

        _write_stories_to_file(story_path, evts)
        _write_nlu_to_file(nlu_path, evts)
        _write_domain_to_file(domain_path, evts, endpoint)

        logger.info("Successfully wrote stories and NLU data")
        sys.exit()
    elif answer == "continue":

def _ask_if_quit(sender_id: Text, endpoint: EndpointConfig) -> bool:
    """Display the exit menu.

    Return `True` if the previous question should be retried."""

    answer = questionary.select(
        message="Do you want to stop?",
        choices=[Choice("Continue", "continue"),
                 Choice("Undo Last", "undo"),
                 Choice("Fork", "fork"),
                 Choice("Start Fresh", "restart"),
                 Choice("Export & Quit", "quit")]).ask()

    if not answer or answer == "quit":
        # this is also the default answer if the user presses Ctrl-C
        _write_data_to_file(sender_id, endpoint)
        sys.exit()
    elif answer == "continue":
        # in this case we will just return, and the original
        # question will get asked again
        return True
    elif answer == "undo":
        raise UndoLastStep()
    elif answer == "fork":
        raise ForkTracker()
    elif answer == "restart":

This method provides various options for exploration of the different models.

        - ``See-Confusion-Matrix`` shows the confusion matrix on the test dataset.
        - ``See-Examples-of-Classification`` is to explore correct and mis-classifications. You can provide two class numbers as in, ``2 3`` and it shows examples in the test dataset where text that belong to class ``2`` is classified as class ``3``.
        - ``See-prf-table`` shows the precision recall and fmeasure per class.
        - ``See-text`` - Manually enter text and look at the classification results.
        """
        self.infer_obj.run_test()

        while True:
            choices = [
                Choice("See-Confusion-Matrix"),
                Choice("See-examples-of-Classifications"),
                Choice("See-prf-table"),
                Choice(title="Enter text ", value="enter_text"),
                Choice(
                    title="If this is ScienceIE chose this to generate results",
                    value="science-ie-official-results",
                ),
                Choice("exit"),
            ]

            interaction_choice = questionary.rawselect(
                "What would you like to do now", qmark="❓", choices=choices
            ).ask()

            if interaction_choice == "See-Confusion-Matrix":
                self.infer_obj.print_confusion_matrix()
            elif interaction_choice == "See-examples-of-Classifications":
                misclassification_choice = questionary.text(
                    "Enter Two Classes separated by a space. [Hint: 1 2]"

def ask_deletion() -> str:
        """ Since this is deletion, we want confirmation, just to be sure
        whether to keep the deleted folder locally or to remove it

        Returns
        -------
        str
            An yes or no answer to the question

        """
        deletion_question = questionary.rawselect(
            "Do you also want to delete the file locally. Caution! File will be removed locally",
            qmark="❓",
            choices=[Choice("yes"), Choice("no")],
        )
        deletion_answer = deletion_question.ask()
        return deletion_answer

def ask_pystyle(**kwargs):
    # create the question object
    question = questionary.select(
        "What do you want to do?",
        qmark="😃",
        choices=[
            "Order a pizza",
            "Make a reservation",
            Separator(),
            "Ask for opening hours",
            Choice("Contact support", disabled="Unavailable at this time"),
            "Talk to the receptionist",
        ],
        style=custom_style_dope,
        **kwargs,
    )

    # prompt the user for an answer
    return question.ask()

def ask_generate_report_or_interact():
        """ Ask the user to chose between interacting with the model or generate the report
        for all the experiments

        Returns
        -------
        str
            choce of the user as a string

        """
        choices = [
            Choice("Interact with model", "interact"),
            Choice("Generate report (for all experiments)", "gen-report"),
        ]

        generate_report_or_interact = questionary.rawselect(
            "What would you like to do ", qmark="❓", choices=choices
        )
        return generate_report_or_interact.ask()

def return_model_type_choices(self) -> List[Choice]:
        """ Returns a list of different model types availabel to the user

        Returns
        -------
        List[Choice]
            A list of different model types available to the user

        """
        choices = []
        for model_type in self.trained_model_types:
            choices.append(Choice(model_type))
        return choices