dev(narugo): add docs for predict_fmt

-----------------------------------------

.. autoclass:: ClassifyModel

    :members: __init__, predict_score, predict, clear, make_ui, launch_demo

    :members: __init__, predict_score, predict, predict_fmt, clear, make_ui, launch_demo

classify_predict_fmt

-----------------------------------------

.. autofunction:: classify_predict_fmt

        return self._open_label(model_name)[label_group][max_id], output[max_id].item()

    def predict_fmt(self, image: ImageTyping, model_name: str, fmt='scores-top5'):

        """

        Predict the scores for each class with given format specification using the specified model.

        :param image: The input image to classify.

        :type image: ImageTyping

        :param model_name: The name of the model to use for prediction.

        :type model_name: str

        :param fmt: Format specification. Default is ``scores-top5``.

        :return: Prediction result formatted with parameter ``fmt``.

        :raises ValueError: If the model name is invalid.

        :raises RuntimeError: If there's an error during prediction.

        .. note::

            The following specifications are supported in parameter ``fmt``:

            - ``output``, raw prediction result, in np.ndarray format.

            - ``logits``, (not available in some models) logits result, in np.ndarray format.

            - ``embedding``, (not available in some models) embeddings result, in np.ndarray format.

            - ``scores``, prediction scores of all classes in dict format.

            - ``scores-topK``, prediction scores of top-K classes in dict format, e.g. ``scores-top10`` means top 10 scores.

            - ``scores-<label_group>``, prediction scores of all classes with label group ``<label_group>``, e.g. ``scores-descriptions`` means all scores with ``descriptions`` label group.

            - ``scores-topK-<label_group>``, prediction scores of top-K classes with label group ``<label_group>``.

        """

        d_data = {name: value[0] for name, value in self._raw_predict(image, model_name).items()}

        scores = d_data['output']

        d_labels = self._open_label(model_name)

            matching = re.fullmatch(r'^scores(-top(?P<topk>\d+))?(-(?P<label_group>[a-zA-Z\d_]+))?$', vname)

            if matching:

                topk = int(matching.group('topk')) if matching.group('topk') else None

                group_label = matching.group('label_group') if matching.group('label_group') else 'default'

                vname_to_spair[vname] = (topk, group_label)

                if (topk, group_label) not in d_scores:

                    d_scores[(topk, group_label)] = _labels_scores_to_topk(

                        labels=d_labels[group_label],

                label_group = matching.group('label_group') if matching.group('label_group') else 'default'

                vname_to_spair[vname] = (topk, label_group)

                if (topk, label_group) not in d_scores:

                    d_scores[(topk, label_group)] = _labels_scores_to_topk(

                        labels=d_labels[label_group],

                        scores=scores,

                        topk=topk,

                    )

def classify_predict_fmt(image: ImageTyping, repo_id: str, model_name: str, fmt='scores-top5',

                         hf_token: Optional[str] = None):

    """

    Predict the scores for each class with given format specification using the specified model.

    This function is a convenience wrapper around ClassifyModel's predict method.

    :param image: The input image to classify.

    :type image: ImageTyping

    :param repo_id: The repository ID containing the models.

    :type repo_id: str

    :param model_name: The name of the model to use for prediction.

    :type model_name: str

    :param fmt: Format specification. Default is ``scores-top5``.

    :param hf_token: Optional Hugging Face authentication token.

    :type hf_token: Optional[str]

    :return: Prediction result formatted with parameter ``fmt``.

    :raises ValueError: If the model name is invalid.

    :raises RuntimeError: If there's an error during prediction.

    .. note::

        The following specifications are supported in parameter ``fmt``:

        - ``output``, raw prediction result, in np.ndarray format.

        - ``logits``, (not available in some models) logits result, in np.ndarray format.

        - ``embedding``, (not available in some models) embeddings result, in np.ndarray format.

        - ``scores``, prediction scores of all classes in dict format.

        - ``scores-topK``, prediction scores of top-K classes in dict format, e.g. ``scores-top10`` means top 10 scores.

        - ``scores-<label_group>``, prediction scores of all classes with label group ``<label_group>``, e.g. ``scores-descriptions`` means all scores with ``descriptions`` label group.

        - ``scores-topK-<label_group>``, prediction scores of top-K classes with label group ``<label_group>``.

    """

    return _open_models_for_repo_id(repo_id, hf_token=hf_token).predict_fmt(

        image=image,

        model_name=model_name,