handler.py

from typing import Dict, List, Any
from optimum.pipelines import pipeline
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSeq2SeqLM


class EndpointHandler():
    def __init__(self, path=""):
        tokenizer = AutoTokenizer.from_pretrained(path)
        model = ORTModelForSeq2SeqLM.from_pretrained(path)
        self.pipeline = pipeline("summarization",model=model, tokenizer=tokenizer)

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
       data args:
            inputs (:obj: `str`)
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        # get inputs
        inputs = data.pop("inputs", data)
        parameters = data.pop("parameters", None)

        # pass inputs with all kwargs in data
        if parameters is not None:
            prediction = self.pipeline(inputs, **parameters)
        else:
            prediction = self.pipeline(inputs)
        return prediction