handler.py

from typing import  Dict, List, Any
from transformers import AutoModel, AutoTokenizer


class EndpointHandler():
    def __init__(self, path=""):
        self.tokenizer = AutoTokenizer.from_pretrained("Wellcome/WellcomeBertMesh")
        self.model = AutoModel.from_pretrained("Wellcome/WellcomeBertMesh", trust_remote_code=True)

    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.
        """
        text = data.pop("inputs", data)
        inputs = self.tokenizer(text, padding="max_length")
        preds = self.model(input_ids=[inputs["input_ids"]])

        id2label = self.model.config.id2label

        prediction = [
            {"label": id2label[label_id], "score": p}
            for label_id, p in enumerate(preds[0].tolist()) if p > 0.5
        ]
        return prediction