from typing import List, Union, Tuple

import numpy as np
from PIL.Image import Image
from funcy import rcompose

from image_prediction.estimator.adapter.adapter import EstimatorAdapter
from image_prediction.label_mapper.mapper import LabelMapper
from image_prediction.utils import get_logger

logger = get_logger()


class Classifier:
    def __init__(self, estimator_adapter: EstimatorAdapter, label_mapper: LabelMapper):
        """Abstraction layer over different estimator backends (e.g. keras or scikit-learn). For each backend to be used
        an EstimatorAdapter must be implemented.

        Args:
            estimator_adapter: adapter for a given estimator backend
        """
        self.__estimator_adapter = estimator_adapter
        self.__label_mapper = label_mapper
        self.__pipe = rcompose(self.__estimator_adapter, self.__label_mapper)

    def predict(self, batch: Union[np.array, Tuple[Image]]) -> List[str]:
        if not isinstance(batch, tuple) and batch.shape[0] == 0:
            return []

        return list(self.__pipe(batch))

    def __call__(self, batch: np.array) -> List[str]:
        return self.predict(batch)