| from typing import Dict, List, Any |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| import torch |
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| self.tokenizer = AutoTokenizer.from_pretrained(path) |
| self.tokenizer.src_lang = "en" |
| self.tokenizer.tgt_lang = "ta" |
| self.model = AutoModelForSeq2SeqLM.from_pretrained(path) |
|
|
| def __call__(self, data: str) -> str: |
| inputs = data.pop("inputs", data) |
| parameters = data.pop("parameters", None) |
| inp = self.tokenizer(inputs, return_tensors="pt") |
| with torch.inference_mode(): |
| out= self.model.generate(**inp) |
| final_output = self.tokenizer.batch_decode(out,skip_special_tokens=True) |
| return {"translation": final_output[0]} |
