from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch

class EndpointHandler:
    def __init__(self, path=""):
        self.tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False)
        self.model = AutoModelForCausalLM.from_pretrained(
            path, torch_dtype=torch.float16, device_map="auto"
        )
        self.pipe = pipeline("text-generation", model=self.model, tokenizer=self.tokenizer)

    def __call__(self, data):
        messages = data.get("inputs", {}).get("messages", [])
        if not messages:
            messages = [{"role": "user", "content": str(data.get("inputs", ""))}]
        text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        out = self.pipe(text,
            max_new_tokens=data.get("parameters", {}).get("max_tokens", 2048),
            temperature=data.get("parameters", {}).get("temperature", 0.3),
            do_sample=True)
        return {"choices": [{"message": {"role": "assistant", "content": out[0]["generated_text"][len(text):]}}]}