from huggingface_hub import HfApi
from pathlib import Path

# Define the parameters for uploading
repo_id = "DBMe/Llama-3.1-Nemotron-70B-Instruct-HF-4.6bpw-h6-exl2"  # Replace with your actual repo ID
folder_path = "/home/asusws-x570-ace/programs/tabbyAPI/models/Llama-3.1-Nemotron-70B-Instruct-HF_exl2_4.6bpw/"  # Replace with your folder path
repo_type = "model"  # Change to "model" or "space" if applicable
revision = "main"  # Optional: specify the branch or use "main"
private = False  # Set to True if the repository should be private
allow_patterns = None  # Optional: specify patterns of files to include
ignore_patterns = None  # Optional: specify patterns of files to exclude
num_workers = 1  # Set based on your system; lower if your internet is unstable
print_report = True  # Enable progress reporting
print_report_every = 60  # Report frequency in seconds

# Initialize the Hugging Face API client
api = HfApi()

# Function to upload the folder in a resumable manner
def upload_resumable():
    try:
        print("Starting upload process...")

        # Perform the upload with the provided parameters
        api.upload_large_folder(
            repo_id=repo_id,
            folder_path=Path(folder_path),
            repo_type=repo_type,
            revision=revision,
            private=private,
            allow_patterns=allow_patterns,
            ignore_patterns=ignore_patterns,
            num_workers=num_workers,
            print_report=print_report,
            print_report_every=print_report_every,
        )

        print("Upload completed successfully!")

    except Exception as e:
        print(f"Upload interrupted due to error: {e}")
        print("You can resume the upload by running the script again.")

# Call the function to start the upload
upload_resumable()