| from typing import Dict, List, Any | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| import torch | |
| class EndpointHandler: | |
| def __init__(self, path=""): | |
| # load model and processor from path | |
| self.model = AutoModelForSeq2SeqLM.from_pretrained(path, device_map="auto", load_in_8bit=True) | |
| self.tokenizer = AutoTokenizer.from_pretrained(path) | |
| def __call__(self, data: Dict[str, Any]) -> Dict[str, str]: | |
| """ | |
| Args: | |
| data (:obj:): | |
| includes the deserialized image file as PIL.Image | |
| """ | |
| # process input | |
| inputs = data.pop("inputs", data) | |
| parameters = data.pop("parameters", None) | |
| # preprocess | |
| input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids | |
| # pass inputs with all kwargs in data | |
| if parameters is not None: | |
| outputs = self.model.generate(input_ids, **parameters) | |
| else: | |
| outputs = self.model.generate(input_ids) | |
| # postprocess the prediction | |
| prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return [{"generated_text": prediction}] |