| 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} |