#!/usr/bin/env python3
"""
Script to deploy the Watermark Leaderboard to Hugging Face Spaces
"""

import os
import shutil
import json
from pathlib import Path

def copy_files_to_hf_directory():
    """Copy necessary files to the Hugging Face deployment directory"""
    
    # Files to copy from the main project
    source_dir = Path("../")
    hf_dir = Path(".")
    
    # Essential files for Hugging Face deployment
    files_to_copy = [
        "app.py",
        "requirements.txt", 
        "README.md",
        "leaderboard.json"
    ]
    
    # Copy Reproducibility folder if it exists
    reproducibility_source = source_dir / "Reproducibility"
    if reproducibility_source.exists():
        reproducibility_dest = hf_dir / "Reproducibility"
        if reproducibility_dest.exists():
            shutil.rmtree(reproducibility_dest)
        shutil.copytree(reproducibility_source, reproducibility_dest)
        print("✅ Copied Reproducibility folder")
    
    # Copy individual files
    for file_name in files_to_copy:
        source_file = source_dir / file_name
        dest_file = hf_dir / file_name
        
        if source_file.exists():
            shutil.copy2(source_file, dest_file)
            print(f"✅ Copied {file_name}")
        else:
            print(f"⚠️  {file_name} not found in source directory")
    
    print("\n🎉 Files copied successfully!")
    print("\nNext steps:")
    print("1. Create a new Hugging Face Space")
    print("2. Upload all files in this directory")
    print("3. Set the Space to use Gradio SDK")
    print("4. Your leaderboard will be live!")

def create_hf_readme():
    """Create a Hugging Face specific README"""
    readme_content = """---
title: Watermark Leaderboard
emoji: 🏆
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: "4.44.0"
app_file: app.py
pinned: false
license: mit
short_description: Interactive leaderboard for watermark performance evaluation
---

# Watermark Leaderboard 🏆

An interactive leaderboard for comparing watermark performance across different models and evaluation settings.

## Features

- **Interactive Scatter Plot**: Visualize watermark performance with Plotly charts
- **Performance Table**: Detailed metrics with sorting and filtering
- **Multiple Evaluation Settings**: Attack-free, Watermark Removal, and Stealing Attack
- **Model Support**: LLaMA3 and DeepSeek models
- **Dynamic Filtering**: Real-time updates based on model and metric selection
- **Flexible Submissions**: Submit data for any combination of attack types
- **Pending Approval System**: All submissions reviewed before appearing on leaderboard
- **Complete Field Visibility**: Administrators see all submission details for review
- **Professional UI**: Clean, modern interface with accordion sections
- **Reproducibility**: Access to all evaluation codes and guidelines

## How to Use

1. **Select Model**: Choose between LLaMA3 or DeepSeek
2. **Choose Setting**: Pick from Attack-free, Watermark Removal, or Stealing Attack
3. **View Results**: Explore the scatter plot and detailed table
4. **Submit Data**: Click "Add Your Data" to submit new results
   - Submit any combination of attack types (Attack-free, Watermark Removal, Stealing Attack)
   - All submissions go through approval process before appearing on leaderboard
5. **Administrator Review**: Administrators can review pending submissions with full field visibility

## Metrics Explained

- **Normalized Utility ↑**: Higher values indicate better text quality
- **Detection Rate (%) ↑**: Higher values indicate better watermark detection
- **Absolute Utility Degradation ↑**: Higher values indicate better resistance to removal attacks
- **Adversary BERT Score ↑**: Higher values indicate better performance under adversarial conditions

## Contributing

We encourage researchers to contribute their evaluation results. Please follow the guidelines in the "Guidelines" section for submission requirements.

## License

MIT License

---
*Last updated: December 2024*
"""
    
    with open("README.md", "w", encoding="utf-8") as f:
        f.write(readme_content)
    
    print("✅ Created Hugging Face README.md")

def main():
    """Main deployment function"""
    print("🚀 Preparing Watermark Leaderboard for Hugging Face deployment...")
    
    # Create HF README
    create_hf_readme()
    
    # Copy files
    copy_files_to_hf_directory()
    
    print("\n📋 Deployment Checklist:")
    print("✅ All files prepared")
    print("✅ Requirements.txt updated")
    print("✅ README.md created for Hugging Face")
    print("✅ Reproducibility codes included")
    
    print("\n🌐 Ready for Hugging Face Spaces deployment!")

if __name__ == "__main__":
    main()