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-# AI Writing Studio - Production Grade
-
-[![CI](https://github.com/yourusername/writing-studio/workflows/CI/badge.svg)](https://github.com/yourusername/writing-studio/actions)
-[![codecov](https://codecov.io/gh/yourusername/writing-studio/branch/main/graph/badge.svg)](https://codecov.io/gh/yourusername/writing-studio)
-[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-
-A production-grade AI-powered writing assistant designed for educational environments. Compare drafts, receive rubric-based feedback, and improve your writing with AI-generated revisions.
-
-## 🚀 One-Click Deploy
-
-### Deploy to HuggingFace Spaces
-
-[![Deploy on HF Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/deploy-to-spaces-lg.svg)](https://huggingface.co/spaces)
-
-**Quick Deploy:**
-1. Click "Deploy to Spaces" above (or [create a new Space](https://huggingface.co/new-space))
-2. Choose "Gradio" as the SDK
-3. Upload/clone this repository
-4. Your app will be live in minutes!
-
-**No configuration needed** - Works out of the box on HuggingFace Spaces with sensible defaults.
-
-## Features
-
-- **AI-Powered Revisions**: Generate text improvements using state-of-the-art language models
-- **Rubric-Based Scoring**: Automated analysis across multiple writing criteria:
-  - Clarity and sentence structure
-  - Conciseness and wordiness detection
-  - Organization and flow
-  - Evidence and support
-  - Grammar and mechanics
-- **Side-by-Side Comparison**: Visual diff highlighting changes between original and revised text
-- **Multiple Prompt Packs**: Specialized templates for different writing contexts:
-  - General writing
-  - Literature analysis
-  - Technical communication
-  - Academic writing
-  - Creative writing
-- **Production Ready**:
-  - Comprehensive error handling
-  - Structured logging
-  - Prometheus metrics
-  - Health checks
-  - Rate limiting
-  - Docker support
-  - CI/CD pipeline
-
-## Quick Start
-
-### HuggingFace Spaces (Easiest)
-
-The simplest way to use Writing Studio is on HuggingFace Spaces:
-
-1. **Fork/Clone to Spaces:**
-   - Go to [HuggingFace Spaces](https://huggingface.co/spaces)
-   - Create a new Space with Gradio SDK
-   - Upload this repository or connect via Git
-   - Done! The app works immediately
-
-2. **Local Run (for HF Spaces compatibility):**
-   ```bash
-   pip install -r requirements.txt
-   python app.py
-   ```
-
-### Using Docker (Self-Hosted)
-
-```bash
-# Clone the repository
-git clone https://github.com/yourusername/writing-studio.git
-cd writing-studio
-
-# Copy environment file
-cp .env.example .env
-
-# Start the application
-docker-compose up
-```
-
-Visit `http://localhost:7860` for the application and `http://localhost:8000` for metrics.
-
-### Local Development
-
-```bash
-# Create virtual environment
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-
-# Install dependencies
-make install-dev
-
-# Copy environment file
-cp .env.example .env
-
-# Run the application
-make run
-```
-
-## Configuration
-
-Configuration is managed through environment variables. See `.env.example` for all available options:
-
-```bash
-# Key configuration options
-ENVIRONMENT=production          # development, staging, production
-DEFAULT_MODEL=distilgpt2       # HuggingFace model ID
-HOST=0.0.0.0                   # Server host
-PORT=7860                      # Server port
-LOG_LEVEL=INFO                 # Logging level
-ENABLE_METRICS=true            # Enable Prometheus metrics
-RATE_LIMIT_PER_MINUTE=10       # Rate limiting
-```
-
-## Project Structure
-
-```
-writing-studio/
-├── src/
-│   └── writing_studio/
-│       ├── core/              # Core business logic
-│       │   ├── analyzer.py    # Main analysis orchestrator
-│       │   ├── config.py      # Configuration management
-│       │   └── exceptions.py  # Custom exceptions
-│       ├── services/          # Service layer
-│       │   ├── model_service.py   # Model management
-│       │   ├── rubric_service.py  # Rubric scoring
-│       │   ├── diff_service.py    # Text comparison
-│       │   └── prompt_service.py  # Prompt templates
-│       ├── utils/             # Utilities
-│       │   ├── logging.py     # Logging configuration
-│       │   ├── validation.py  # Input validation
-│       │   ├── metrics.py     # Prometheus metrics
-│       │   └── monitoring.py  # Health checks
-│       └── main.py            # Application entry point
-├── tests/                     # Test suite
-│   ├── unit/                  # Unit tests
-│   └── integration/           # Integration tests
-├── configs/                   # Configuration files
-├── docs/                      # Documentation
-├── Dockerfile                 # Docker image definition
-├── docker-compose.yml         # Docker Compose configuration
-├── pyproject.toml            # Python project configuration
-└── requirements.txt          # Python dependencies
-```
-
-## Development
-
-### Setup Development Environment
-
-```bash
-# Install development dependencies
-make install-dev
-
-# Install pre-commit hooks
-pre-commit install
-```
-
-### Running Tests
-
-```bash
-# Run all tests with coverage
-make test
-
-# Run specific test file
-pytest tests/unit/test_validation.py -v
-```
-
-### Code Quality
-
-```bash
-# Format code
-make format
-
-# Run linters
-make lint
-
-# Run all checks
-make format lint test
-```
-
-### Docker Development
-
-```bash
-# Build Docker image
-make docker-build
-
-# Run with Docker Compose
-make docker-run
-
-# View logs
-docker-compose logs -f app
-```
-
-## Monitoring
-
-The application exposes Prometheus metrics on port 8000 (configurable):
-
-- `writing_studio_requests_total` - Total number of requests
-- `writing_studio_request_duration_seconds` - Request duration
-- `writing_studio_generation_duration_seconds` - Text generation duration
-- `writing_studio_cache_hits_total` - Cache hit count
-- `writing_studio_errors_total` - Error count by type
-
-### Health Checks
-
-- **Liveness**: `GET /health/live` - Check if application is alive
-- **Readiness**: `GET /health/ready` - Check if ready to serve requests
-- **Health**: `GET /health` - Comprehensive health check
-
-## Deployment
-
-### Environment Variables for Production
-
-```bash
-ENVIRONMENT=production
-DEBUG=false
-LOG_LEVEL=INFO
-ENABLE_AUTH=true
-SECRET_KEY=your-secure-secret-key
-ALLOWED_ORIGINS=https://yourdomain.com
-```
-
-### Docker Deployment
-
-```bash
-# Build production image
-docker build -t writing-studio:latest .
-
-# Run with production settings
-docker run -d \
-  --name writing-studio \
-  -p 7860:7860 \
-  -p 8000:8000 \
-  -e ENVIRONMENT=production \
-  -v $(pwd)/logs:/app/logs \
-  -v $(pwd)/models:/app/models \
-  writing-studio:latest
-```
-
-### Monitoring Stack (Optional)
-
-Start the full monitoring stack with Prometheus and Grafana:
-
-```bash
-docker-compose --profile monitoring up
-```
-
-Access:
-- Application: http://localhost:7860
-- Metrics: http://localhost:8000
-- Prometheus: http://localhost:9090
-- Grafana: http://localhost:3000 (admin/admin)
-
-## API Usage
-
-While the primary interface is the Gradio web UI, you can also use the core components programmatically:
-
-```python
-from writing_studio.core.analyzer import WritingAnalyzer
-
-analyzer = WritingAnalyzer()
-
-original, revision, feedback, diff_html, metadata = analyzer.analyze_and_compare(
-    user_text="Your text here",
-    model_name="distilgpt2",
-    prompt_pack="General"
-)
-
-print(f"Feedback:\n{feedback}")
-print(f"Duration: {metadata['duration']:.2f}s")
-```
-
-## Performance Optimization
-
-- **Model Caching**: Models are cached after first load
-- **Generation Caching**: Results are cached based on input hash
-- **Lazy Loading**: Services initialized on first use
-- **Docker Layer Caching**: Multi-stage builds for faster rebuilds
-
-## Security Features
-
-- Input validation and sanitization
-- Rate limiting (configurable)
-- Path traversal protection
-- Non-root Docker user
-- Security scanning in CI/CD
-- CORS configuration
-- Secret management via environment variables
-
-## Troubleshooting
-
-### Common Issues
-
-**Model Loading Fails**
-```bash
-# Ensure sufficient disk space
-df -h
-
-# Check model cache directory permissions
-ls -la ./models/
-```
-
-**Port Already in Use**
-```bash
-# Change ports in .env or docker-compose.yml
-PORT=7861
-METRICS_PORT=8001
-```
-
-**Memory Issues**
-```bash
-# Use a smaller model
-DEFAULT_MODEL=distilgpt2
-
-# Disable caching if needed
-ENABLE_CACHE=false
-```
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch (`git checkout -b feature/amazing-feature`)
-3. Make your changes
-4. Run tests and linters (`make format lint test`)
-5. Commit your changes (`git commit -m 'Add amazing feature'`)
-6. Push to the branch (`git push origin feature/amazing-feature`)
-7. Open a Pull Request
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## Acknowledgments
-
-- Built with [Gradio](https://gradio.app/) for the web interface
-- Powered by [HuggingFace Transformers](https://huggingface.co/transformers/)
-- Monitoring with [Prometheus](https://prometheus.io/)
-
-## Documentation
-
-- **[User Guide](docs/USER_GUIDE.md)** - How to use the application
-- **[Architecture](docs/ARCHITECTURE.md)** - System design and structure
-- **[Deployment Guide](docs/DEPLOYMENT.md)** - Self-hosted deployment
-- **[HuggingFace Spaces Guide](docs/HUGGINGFACE_SPACES.md)** - Deploy to HF Spaces
-- **[Production Upgrade Summary](PRODUCTION_UPGRADE.md)** - What was improved
-
-## Support
-
-- Documentation: [docs/](docs/)
-- Issues: [GitHub Issues](https://github.com/yourusername/writing-studio/issues)
-- Discussions: [GitHub Discussions](https://github.com/yourusername/writing-studio/discussions)
-
-## Roadmap
-
-- [ ] User authentication and session management
-- [ ] Database integration for saving analyses
-- [ ] Support for additional language models
-- [ ] Export functionality (PDF, Word)
-- [ ] Collaborative features
-- [ ] Custom rubric creation
-- [ ] API endpoints for programmatic access