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| import os | |
| # Redirect ALL caching and temp directories to the project drive to avoid C: disk pressure | |
| project_root = os.path.dirname(os.path.dirname(__file__)) | |
| _hf_cache = os.path.join(project_root, ".hf_cache") | |
| _tmp_dir = os.path.join(project_root, ".tmp") | |
| os.makedirs(_hf_cache, exist_ok=True) | |
| os.makedirs(_tmp_dir, exist_ok=True) | |
| os.environ["HF_HOME"] = _hf_cache | |
| os.environ["HF_HUB_CACHE"] = _hf_cache | |
| os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" | |
| os.environ["TMPDIR"] = _tmp_dir | |
| os.environ["TEMP"] = _tmp_dir | |
| os.environ["TMP"] = _tmp_dir | |
| from huggingface_hub import HfApi, create_repo, login | |
| import getpass | |
| def upload_model_to_hub(): | |
| # Replace with your actual repo ID | |
| repo_id = "pahariaryan121/NeuroVision-VQA" | |
| model_folder = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models", "sharded-model") | |
| if not os.path.exists(model_folder): | |
| print(f"Error: Model folder not found at {model_folder}") | |
| print("Please ensure the model training has completed and weights are saved locally.") | |
| return | |
| token = os.environ.get("HF_TOKEN") | |
| if not token: | |
| # Try reading from stored token files (set by huggingface-cli login) | |
| for token_path in [ | |
| os.path.join(_hf_cache, "token"), | |
| os.path.expanduser("~/.cache/huggingface/token"), | |
| os.path.join(os.environ.get("HF_HOME", ""), "token"), | |
| ]: | |
| if os.path.isfile(token_path): | |
| token = open(token_path).read().strip() | |
| print(f"🔑 Using stored token from {token_path}") | |
| break | |
| if not token: | |
| print("🔑 Please enter your Hugging Face Access Token with WRITE permissions (input will be hidden):") | |
| token = getpass.getpass("Token: ") | |
| print("\nAuthenticating...") | |
| try: | |
| login(token=token.strip(), add_to_git_credential=True) | |
| except Exception as e: | |
| print(f"❌ Login failed! Please check your token. Error: {e}") | |
| return | |
| print(f"Connecting to Hugging Face Hub to upload {model_folder} to {repo_id}...") | |
| api = HfApi() | |
| try: | |
| # Create repository if it doesn't exist | |
| create_repo(repo_id, exist_ok=True, private=False) | |
| print(f"Repository {repo_id} is ready.") | |
| except Exception as e: | |
| print(f"Warning/Error creating repo: {e}") | |
| try: | |
| # Upload files one-by-one to avoid loading everything into RAM at once (OOM on large .safetensors) | |
| files = [] | |
| for root, dirs, filenames in os.walk(model_folder): | |
| for fname in filenames: | |
| full = os.path.join(root, fname) | |
| rel = os.path.relpath(full, model_folder).replace("\\", "/") | |
| files.append((full, rel)) | |
| print(f"Found {len(files)} files to upload.") | |
| for i, (full_path, rel_path) in enumerate(files, 1): | |
| size_mb = os.path.getsize(full_path) / (1024 * 1024) | |
| print(f" [{i}/{len(files)}] Uploading {rel_path} ({size_mb:.1f} MB)...") | |
| api.upload_file( | |
| path_or_fileobj=full_path, | |
| path_in_repo=rel_path, | |
| repo_id=repo_id, | |
| repo_type="model", | |
| commit_message=f"Upload {rel_path}", | |
| ) | |
| print(" ✓ Done") | |
| print(f"\n✅ Successfully uploaded model to https://huggingface.co/{repo_id}") | |
| except Exception as e: | |
| print(f"❌ Failed to upload: {e}") | |
| print("\nNote: Make sure you are logged in using `huggingface-cli login` and have write access.") | |
| if __name__ == "__main__": | |
| upload_model_to_hub() | |