NicoleMathias/grammer-error-correction

Multilingual Grammar Error Detection & Correction

A powerful web application for detecting and correcting grammar errors in multiple languages using state-of-the-art AI models.

🌍 Supported Languages

• —English - Error detection + Grammar correction
• —Czech - Error detection only
• —German - Error detection only
• —Italian - Error detection only
• —Swedish - Error detection only

✨ Features

• —🔍 Grammar Error Detection: Identifies grammar errors with confidence scores
• —✏️ Grammar Correction: Corrects errors (English only)
• —🌐 Multilingual Support: 5 languages with specialized models
• —🎨 Modern UI: Clean, responsive web interface
• —🔧 REST API: Full API for integration
• —📊 Detailed Analysis: Error statistics and confidence scores

🚀 Quick Start

Local Development

# Clone the repository
git clone <your-repo-url>
cd automatic_grammar_error_correction

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

# Open http://localhost:7860

Hugging Face Spaces Deployment

See README_HF_SPACES.md for detailed deployment instructions.

🔧 API Endpoints

Example API Usage

# Error Detection
curl -X POST http://localhost:7860/api/detect \
  -H "Content-Type: application/json" \
  -d '{"text": "I goes to the store", "language_code": "en"}'

# Grammar Correction
curl -X POST http://localhost:7860/api/correct \
  -H "Content-Type: application/json" \
  -d '{"text": "I goes to the store", "language_code": "en"}'

📊 Model Information

• —T5 Model: Grammar correction (English only)
• —BERT Models: Error detection for each language
• —English: FCE dataset
• —Czech: GECCC dataset
• —German: Falko-Merlin dataset
• —Italian: Merlin dataset
• —Swedish: SWELL dataset

🎯 Language Capabilities

🛠️ Technology Stack

• —Backend: Flask (Python)
• —AI Models: Transformers (BERT, T5)
• —Frontend: HTML, CSS, JavaScript
• —Deployment: Docker, Hugging Face Spaces
• —Dependencies: PyTorch, Transformers, NumPy

📁 Project Structure

automatic_grammar_error_correction/
├── app.py                 # Main Flask application
├── templates/
│   └── multilingual.html  # Web interface
├── models/                # BERT models for each language
├── t5_jfleg/             # T5 model for correction
├── requirements.txt       # Python dependencies
├── Dockerfile            # Docker configuration
└── README_HF_SPACES.md   # Deployment guide

🔍 Usage Examples

Web Interface

1. 1.Select your language from the dropdown
2. 2.Enter text to analyze
3. 3.Choose an action:
4. 4.Detect Errors: Find grammar errors
5. 5.Correct Grammar: Fix errors (English only)
6. 6.Full Analysis: Both detection and correction

API Response Examples

Error Detection:

{
  "accuracy": 0.806,
  "avg_confidence": 0.921,
  "dataset": "FCE",
  "tokens": [
    {"token": "I", "tag": "c", "confidence": 0.95},
    {"token": "goes", "tag": "i", "confidence": 0.86}
  ]
}

Grammar Correction:

{
  "success": true,
  "corrected_text": "I go to the store",
  "original_text": "I goes to the store"
}

🚨 Troubleshooting

Common Issues

1. 1.Model Loading: First run may take time to download models
2. 2.Memory: Models require significant RAM (~2GB)
3. 3.Port Conflicts: App uses port 7860 by default

Health Check

curl http://localhost:7860/api/health

📈 Performance

• —Error Detection: ~80-85% accuracy across languages
• —Grammar Correction: ~90% accuracy for English
• —Response Time: 1-3 seconds per request
• —Model Size: ~2GB total

🤝 Contributing

1. 1.Fork the repository
2. 2.Create a feature branch
3. 3.Make your changes
4. 4.Test thoroughly
5. 5.Submit a pull request

📄 License

MIT License - see LICENSE file for details

🙏 Acknowledgments

• —Hugging Face for the Transformers library
• —The research teams behind the fine-tuned models
• —The open-source community for various tools and libraries

Ready for deployment! 🚀

For deployment instructions, see README_HF_SPACES.md.