| 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 |
| |
| 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 |
|
|
