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| 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 | |