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---
title: PII-Guard – Deep Learning Model for PII Classification
emoji: 🔒
colorFrom: red
colorTo: purple
sdk: streamlit
sdk_version: "1.40.0" # 👈 use the latest stable streamlit
app_file: app.py
pinned: false
license: mit
---
# PIIDetector 🔒
Detecting Personally Identifiable Information (PII) using BiLSTM-CRF model
## 🚀 Demo
[Watch Demo Video](./demo/demo.mp4)
## ✨ Features
- **PII Detection**: Identify various types of Personally Identifiable Information in text
- **BiLSTM-CRF Model**: Utilizes a powerful deep learning model for sequence labeling
- **Streamlit Web Interface**: User-friendly interface for easy interaction
- **Multiple PII Types**: Detects various PII entities including names, addresses, financial information, and more
## 📦 Installation
1. **Clone the repository**
```bash
git clone https://github.com/yourusername/PIIDetector.git
cd PIIDetector
```
2. **Create and activate a virtual environment**
```bash
# Create a virtual environment
python -m venv .venv
# Activate it
# On Linux/Mac:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
## 🚀 Usage
1. **Run the Streamlit app**
```bash
streamlit run app.py
```
2. **Enter text** in the text area and click "Analyze" to detect PII entities
3. **View results** in the table showing tokens and their predicted PII labels
## 🛠 Configuration
The application uses a pre-trained BiLSTM-CRF model located in the `models/` directory. The model supports the following PII entity types:
- Personal Information (names, age, gender, etc.)
- Contact Information (emails, phone numbers, addresses)
- Financial Information (credit cards, account numbers, IBAN, etc.)
- Identification Numbers (SSN, passport numbers, etc.)
- And many more...
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgements
- [Hugging Face Transformers](https://huggingface.co/transformers/)
- [PyTorch](https://pytorch.org/)
- [Streamlit](https://streamlit.io/)
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