from typing import Dict, List, Any from optimum.intel.openvino import OVModelForSequenceClassification from transformers import pipeline, AutoTokenizer class EndpointHandler(): def __init__(self, path=""): # load the optimized model model = OVModelForSequenceClassification.from_pretrained(path) tokenizer = AutoTokenizer.from_pretrained(path) # create inference pipeline self.pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer) 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) 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) # postprocess the prediction return prediction