import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_NAME = "kasterkeqi/Lumo-8B-Fork-Sol-Copilot"

class EndpointHandler:
    def __init__(self, model_dir=None):
        """Initialize the model and tokenizer."""
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Loading model on {self.device}...")

        self.tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
        self.model = AutoModelForCausalLM.from_pretrained(
            MODEL_NAME,
            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
        ).to(self.device)

        print("Model loaded successfully.")

    def __call__(self, inputs):
        """Handle inference requests."""
        prompt = inputs.get("inputs", "")
        if not prompt:
            return {"error": "No input provided"}

        # Tokenize input
        input_tokens = self.tokenizer(prompt, return_tensors="pt").to(self.device)

        # Generate output
        with torch.no_grad():
            output_tokens = self.model.generate(**input_tokens, max_length=200)

        # Decode output
        response = self.tokenizer.decode(output_tokens[0], skip_special_tokens=True)
        return {"response": response}