from typing import Any, Dict, List

from span_marker import SpanMarkerModel


class EndpointHandler:
    def __init__(self, model_id: str) -> None:
        self.model = SpanMarkerModel.from_pretrained(model_id)
        # Try to place it on CUDA, do nothing if it fails
        self.model.try_cuda()

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Args:
            data (Dict[str, Any]):
                a dictionary with the "inputs" key corresponding to a string containing some text
        Return:
            A List[Dict[str, Any]]:. The object returned should be like [{"entity_group": "XXX", "word": "some word", "start": 3, "end": 6, "score": 0.82}] containing :
                - "entity_group": A string representing what the entity is.
                - "word": A rubstring of the original string that was detected as an entity.
                - "start": the offset within `input` leading to `answer`. context[start:stop] == word
                - "end": the ending offset within `input` leading to `answer`. context[start:stop] === word
                - "score": A score between 0 and 1 describing how confident the model is for this entity.
        """
        return [
            {
                "entity_group": entity["label"],
                "word": entity["span"],
                "start": entity["char_start_index"],
                "end": entity["char_end_index"],
                "score": entity["score"],
            }
            for entity in self.model.predict(data["inputs"])