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""" |
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Upload trained model to Hugging Face Hub |
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""" |
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import argparse |
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import sys |
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import os |
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from huggingface_hub import HfApi, create_repo |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) |
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def upload_to_hub( |
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model_path: str, |
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repo_name: str, |
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organization: str = None, |
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private: bool = False |
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): |
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""" |
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Upload model to Hugging Face Hub. |
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Args: |
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model_path: Path to the trained model |
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repo_name: Name for the repository on Hugging Face Hub |
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organization: Organization name (optional) |
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private: Whether to make the repository private |
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""" |
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print("=" * 60) |
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print("Uploading Model to Hugging Face Hub") |
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print("=" * 60) |
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if organization: |
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repo_id = f"{organization}/{repo_name}" |
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else: |
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repo_id = repo_name |
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print(f"\nRepository: {repo_id}") |
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print(f"Private: {private}") |
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print("\n[1/3] Loading model...") |
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try: |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForSequenceClassification.from_pretrained(model_path) |
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print("✓ Model loaded successfully") |
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except Exception as e: |
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print(f"✗ Error loading model: {e}") |
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return |
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print("\n[2/3] Creating repository...") |
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try: |
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create_repo( |
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repo_id=repo_id, |
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repo_type="model", |
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exist_ok=True, |
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private=private |
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) |
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print(f"✓ Repository created/verified: {repo_id}") |
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except Exception as e: |
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print(f"✗ Error creating repository: {e}") |
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print("\nMake sure you're logged in:") |
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print(" huggingface-cli login") |
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return |
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print("\n[3/3] Uploading model and tokenizer...") |
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try: |
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model.push_to_hub(repo_id) |
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tokenizer.push_to_hub(repo_id) |
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print("✓ Upload complete!") |
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except Exception as e: |
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print(f"✗ Error uploading: {e}") |
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return |
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print("\n" + "=" * 60) |
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print("Success! 🎉") |
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print("=" * 60) |
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print(f"\nYour model is now available at:") |
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print(f"https://huggingface.co/{repo_id}") |
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print("\nTo use your model:") |
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print(f""" |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("{repo_id}") |
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model = AutoModelForSequenceClassification.from_pretrained("{repo_id}") |
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""") |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description="Upload model to Hugging Face Hub") |
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parser.add_argument( |
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"--model-path", |
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type=str, |
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default="./results/final_model", |
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help="Path to the trained model" |
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) |
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parser.add_argument( |
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"--repo-name", |
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type=str, |
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required=True, |
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help="Name for the repository on Hugging Face Hub" |
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) |
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parser.add_argument( |
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"--organization", |
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type=str, |
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default=None, |
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help="Organization name (optional)" |
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) |
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parser.add_argument( |
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"--private", |
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action="store_true", |
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help="Make the repository private" |
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) |
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args = parser.parse_args() |
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print("\nBefore uploading, make sure you:") |
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print("1. Have a Hugging Face account") |
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print("2. Are logged in: huggingface-cli login") |
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print("3. Have reviewed the model card (MODEL_CARD.md)") |
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response = input("\nProceed with upload? (yes/no): ") |
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if response.lower() in ['yes', 'y']: |
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upload_to_hub( |
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args.model_path, |
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args.repo_name, |
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args.organization, |
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args.private |
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) |
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else: |
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print("Upload cancelled.") |
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