from huggingface_hub import HfApi

def upload_huge_dataset(dataset_root, repo_id):
    api = HfApi()
    
    print(f"🚀 Starting Resilient Upload for {repo_id}...")
    print("This will automatically split your 16k+ files into multiple commits.")
    
    try:
        api.upload_large_folder(
            folder_path=dataset_root,
            repo_id=repo_id,
            repo_type="dataset",
            # Optional: Ignore hidden files or cache
            ignore_patterns=[".git", ".cache", "**/.ipynb_checkpoints"],
            # num_workers=4 can speed it up, but be careful with bandwidth
            num_workers=4 
        )
        print("✅ Upload Complete! Check your repo.")
        
    except Exception as e:
        print(f"❌ Error: {e}")
        print("💡 If it failed halfway, just run this script again. It will resume where it left off!")

# --- CONFIG ---
# Point this to your "atc_dataset" folder where 'train_audio', 'test_audio' are.
local_folder = "." 
repo = "MrEzzat/atc-dataset"

upload_huge_dataset(local_folder, repo)