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