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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
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 |