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from typing import Dict, List, Any
# from transformers import GPT2Tokenizer
# from model import GPT
import pipeline
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
# model = GPT.from_pretrained(path)
# tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
a = 1
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
inputs = data.pop("inputs", data)
pipeline.start = inputs
output = pipeline.infer()
# isinstance(output,str)
return {"Ans": output} |