from typing import Dict, List, Any
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

class EndpointHandler():
    def __init__(self, path=""):
        self.tokenizer = AutoTokenizer.from_pretrained(path)
        self.model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map="auto")
        self.model.generation_config = GenerationConfig.from_pretrained(path)
        self.model.generation_config.pad_token_id = self.model.generation_config.eos_token_id

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        inputs = data.pop('inputs', data)
        messages = [{"role": "user", "content": inputs}]

        # Mesajları modelin anlayacağı formata dönüştürme
        input_texts = [message["content"] for message in messages]
        input_text = self.tokenizer.eos_token.join(input_texts)
        input_ids = self.tokenizer.encode(input_text, return_tensors="pt")

        # Modelden yanıt üretme
        outputs = self.model.generate(input_ids.to(self.model.device), max_new_tokens=100)

        # Üretilen yanıtı çözme
        result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return [{"result": result}]