from transformers import AutoTokenizer, AutoModelForCausalLM
import torch


class EndpointHandler:
    def __init__(self, path=""):
        self.tokenizer = AutoTokenizer.from_pretrained(path)
        self.model = AutoModelForCausalLM.from_pretrained(
            path,
            torch_dtype=torch.float16,
            device_map="auto"
        )

    def __call__(self, data):
        messages = data.get("messages", [])
        max_tokens = data.get("max_tokens", 2200)

        text = self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True
        )
        inputs = self.tokenizer(text, return_tensors="pt").to(self.model.device)

        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=max_tokens,
                do_sample=False
            )

        response = self.tokenizer.decode(
            outputs[0][inputs["input_ids"].shape[1]:],
            skip_special_tokens=True
        )
        return {"generated_text": response}