from typing import  Dict, List, Any

import spacy
import os

class EndpointHandler():
    def __init__(self, path=""):
        # load the optimized model
        os.system("python -m spacy download en_core_web_sm")
        self.pipeline = spacy.load("en_core_web_sm")

    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        """
        Args:
            data (:obj:):
                includes the input data and the parameters for the inference.
        Return:
            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
                - "label": A string representing what the label/class is. There can be multiple labels.
                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
        """
        inputs = data.pop("inputs", data)

        doc = self.pipeline(inputs)
        res = []
        for token in doc:
            res.append({"token": token.text, "pos": token.pos_, "dep": token.dep_})

        return res