upload_to_hf.py

import os
from huggingface_hub import HfApi, create_repo

# Configuration
REPO_NAME = "Bird-vs-Drone-image-classification-using-Deep-Learning" 
USERNAME = "d-e-e-k-11" # Your Hugging Face username
REPO_ID = f"{USERNAME}/{REPO_NAME}"

api = HfApi()

def upload():
    print(f"Starting upload to https://huggingface.co/spaces/{REPO_ID}")
    
    try:
        # 1. Create the repo if it doesn't exist
        print(f"Checking if repo {REPO_ID} exists...")
        create_repo(
            repo_id=REPO_ID, 
            repo_type="space", 
            space_sdk="docker", 
            exist_ok=True
        )
        
        # 2. Define files to ignore
        # We explicitly exclude the 41k+ images in Dataset/ to avoid hashing crashes
        ignore_patterns = [
            "Dataset/**/*",
            "Dataset/*", 
            "data/**/*",
            "data/*", 
            ".git/**/*",
            ".git/*",
            "__pycache__/**/*",
            "*.pyc",
            "training_history.png",
            "history.json"
        ]
        
        # 3. Use upload_folder (which handles multi-part uploads for large files internally)
        print("Uploading project files (skipping large datasets)...")
        api.upload_folder(
            folder_path=".",
            repo_id=REPO_ID,
            repo_type="space",
            ignore_patterns=ignore_patterns
        )
        
        print(f"\nSuccess! Your app is live at: https://huggingface.co/spaces/{REPO_ID}")
        print("The build process will start automatically.")
        
    except Exception as e:
        print(f"\nError: {e}")
        print("\nIf you are not logged in, run: huggingface-cli login")

if __name__ == "__main__":
    upload()