handler.py

import torch
from typing import  Dict
from sentence_transformers import SentenceTransformer

class EndpointHandler:
    def __init__(self, path=""):
        self.model = SentenceTransformer("all-MiniLM-L6-v2")


    def __call__(self, data: Dict[str, List[str]]) -> Dict[str, List[List[float]]]:
        """
        Args:
            data (:obj:):
                includes the deserialized sentences as a list of strings
        Return:
            A :obj:`dict`: list of embeddings for each input sentence
        """
        # process input
        inputs = data.pop("inputs", data)

        # get embeddings
        embeddings = self.model.encode(inputs)

        # postprocess the embeddings
        return {"embeddings": embeddings.tolist()}