import os
import shutil
import logging
from transformers import GPT2LMHeadModel, GPT2TokenizerFast, GPT2Config
from huggingface_hub import snapshot_download
import torch
from dotenv import load_dotenv
load_dotenv()
REPO_ID = "can-org/AI-Content-Checker"
MODEL_DIR = "./models"
TOKENIZER_DIR = os.path.join(MODEL_DIR, "model")
WEIGHTS_PATH = os.path.join(MODEL_DIR, "model_weights.pth")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
_model, _tokenizer = None, None


def warmup():
    global _model, _tokenizer
    # Ensure punkt is available
    download_model_repo()
    _model, _tokenizer = load_model()
    logging.info("Its ready")


def download_model_repo():
    if os.path.exists(MODEL_DIR) and os.path.isdir(MODEL_DIR):
        logging.info("Model already exists, skipping download.")
        return
    snapshot_path = snapshot_download(repo_id=REPO_ID)
    os.makedirs(MODEL_DIR, exist_ok=True)
    shutil.copytree(snapshot_path, MODEL_DIR, dirs_exist_ok=True)


def load_model():
    tokenizer = GPT2TokenizerFast.from_pretrained(TOKENIZER_DIR)
    config = GPT2Config.from_pretrained(TOKENIZER_DIR)
    model = GPT2LMHeadModel(config)
    model.load_state_dict(torch.load(WEIGHTS_PATH, map_location=device))
    model.to(device)
    model.eval()
    return model, tokenizer


def get_model_tokenizer():
    global _model, _tokenizer
    if _model is None or _tokenizer is None:
        download_model_repo()
        _model, _tokenizer = load_model()
    return _model, _tokenizer