setup_hf_space.py

#!/usr/bin/env python3
"""
Setup script for Hugging Face Space deployment
Ensures GPQA benchmark can run successfully on HF
"""

import os
import sys
import subprocess
from pathlib import Path

def create_deployment_files():
    """Create necessary files for HF deployment"""
    
    print("🚀 Setting up Hugging Face Space deployment...")
    
    # 1. Update requirements.txt with HF dependencies
    requirements_path = Path("requirements.txt")
    existing_reqs = requirements_path.read_text() if requirements_path.exists() else ""
    
    hf_deps = [
        "huggingface_hub>=0.20.0",
        "gradio>=4.31.0",
        "python-dotenv>=0.19.0"
    ]
    
    for dep in hf_deps:
        if dep.split(">=")[0] not in existing_reqs:
            existing_reqs += f"\n{dep}"
    
    requirements_path.write_text(existing_reqs.strip() + "\n")
    print("✅ Updated requirements.txt")
    
    # 2. Create .env.example
    env_example = """# Hugging Face Space Configuration
# Copy this to .env or set in HF Secrets

# Required: Your Grok API key from x.ai
GROK_API_KEY=your_grok_api_key_here

# Required: Your Hugging Face token for GPQA dataset access
# Get it from: https://huggingface.co/settings/tokens
HF_TOKEN=your_hugging_face_token_here

# Optional: OpenAI and Anthropic keys for comparison
# OPENAI_API_KEY=your_openai_key_here
# ANTHROPIC_API_KEY=your_anthropic_key_here
"""
    
    with open(".env.example", "w") as f:
        f.write(env_example)
    print("✅ Created .env.example")
    
    # 3. Create HF-specific run script
    run_script = """#!/usr/bin/env python3
\"\"\"
Hugging Face Space entry point for GPQA evaluation
\"\"\"

import os
import sys
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Set HF token if available
hf_token = os.getenv('HF_TOKEN')
if hf_token:
    os.environ['HUGGING_FACE_HUB_TOKEN'] = hf_token
    print("✅ HF Token configured")

# Import and run the app
from app import create_ui, start_evaluation_safe, check_environment

if __name__ == "__main__":
    # Check environment
    issues = check_environment()
    
    if issues:
        print("\\n⚠️  Configuration issues:")
        for issue in issues:
            print(f"   - {issue}")
        print("\\nThe app will run in demo mode.")
        print("To enable GPQA evaluation, please set the required secrets in HF Space settings.")
    else:
        print("✅ All environment variables configured")
        # Start evaluation in background
        start_evaluation_safe()
    
    # Create and launch UI
    ui = create_ui()
    ui.launch()
"""
    
    with open("run_hf_space.py", "w") as f:
        f.write(run_script)
    os.chmod("run_hf_space.py", 0o755)
    print("✅ Created run_hf_space.py")
    
    # 4. Create README for HF Space
    readme_content = """---
title: Grok-4 GPQA Evaluation
emoji: 🧠
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: "4.31.0"
app_file: run_hf_space.py
pinned: false
---

# Grok-4 GPQA Evaluation Dashboard

Real-time evaluation of Grok-4 model on the GPQA (Graduate-Level Google-Proof Q&A) benchmark.

## 🔧 Configuration

This Space requires the following secrets to be set in your HF Space settings:

1. **GROK_API_KEY** (Required)
   - Get from: https://x.ai
   - Your Grok API key for running evaluations

2. **HF_TOKEN** (Required)
   - Get from: https://huggingface.co/settings/tokens
   - Required for accessing the GPQA dataset
   - Make sure you have requested access to: https://huggingface.co/datasets/Idavidrein/gpqa

## 📊 Features

- Real-time progress tracking
- Accuracy metrics and performance stats
- Detailed results export
- Support for full GPQA dataset (448 questions)

## 🚀 Quick Start

1. Fork this Space
2. Set the required secrets in your Space settings
3. The evaluation will start automatically
4. Monitor progress in the dashboard

## ⚠️ Known Issues

- GPQA dataset requires access approval (usually 1-2 days)
- Grok-4-0709 uses extensive reasoning tokens (~2500-3000 per question)
- Full evaluation takes ~3-4 hours due to model response times

## 📈 Expected Performance

Based on our testing:
- Accuracy: ~80-90% (excluding timeouts)
- Avg Response Time: ~50s per question
- Total Runtime: ~3-4 hours for full dataset
"""
    
    with open("README_HF.md", "w") as f:
        f.write(readme_content)
    print("✅ Created README_HF.md")
    
    # 5. Create pre-flight check script
    check_script = """#!/usr/bin/env python3
\"\"\"
Pre-deployment checklist for HF Space
\"\"\"

import os
import sys
from pathlib import Path

def check_deployment_ready():
    \"\"\"Check if everything is ready for HF deployment\"\"\"
    
    print("🔍 Pre-deployment checklist:\\n")
    
    checks = []
    
    # Check files exist
    required_files = [
        "app.py",
        "run_evaluation.py", 
        "requirements.txt",
        ".env.example",
        "run_hf_space.py",
        "official_config.yaml"
    ]
    
    for file in required_files:
        if Path(file).exists():
            checks.append((f"✅ {file} exists", True))
        else:
            checks.append((f"❌ {file} missing", False))
    
    # Check API directories
    if Path("apis").is_dir() and list(Path("apis").glob("*.py")):
        checks.append(("✅ APIs directory configured", True))
    else:
        checks.append(("❌ APIs directory missing or empty", False))
    
    # Check benchmarks directory  
    if Path("benchmarks").is_dir() and Path("benchmarks/gpqa_benchmark.py").exists():
        checks.append(("✅ GPQA benchmark implementation found", True))
    else:
        checks.append(("❌ GPQA benchmark missing", False))
    
    # Check for sensitive data
    if Path(".env").exists():
        checks.append(("⚠️  .env file exists - make sure it's in .gitignore!", None))
    
    # Print results
    for check, status in checks:
        print(check)
    
    all_good = all(status is not False for _, status in checks)
    
    if all_good:
        print("\\n✅ Ready for deployment!")
        print("\\nNext steps:")
        print("1. Set GROK_API_KEY and HF_TOKEN in HF Space secrets")
        print("2. Make sure you have GPQA dataset access")
        print("3. Push to Hugging Face")
    else:
        print("\\n❌ Issues found - please fix before deploying")
        
    return all_good

if __name__ == "__main__":
    check_deployment_ready()
"""
    
    with open("check_deployment.py", "w") as f:
        f.write(check_script)
    os.chmod("check_deployment.py", 0o755)
    print("✅ Created check_deployment.py")
    
    print("\n🎉 Deployment files created successfully!")
    print("\nNext steps:")
    print("1. Run: python check_deployment.py")
    print("2. Set your API keys in HF Space secrets")
    print("3. Push to Hugging Face")

if __name__ == "__main__":
    create_deployment_files()