privacy-prompt-library 1.0.2

A privacy-preserving prompt transformation library that redacts disability mentions while maintaining functional context

Privacy-Preserving Prompt Library

A comprehensive Python library that transforms user prompts to protect disability privacy while maintaining functional context for AI interactions.

🎯 Purpose

This library helps users interact with AI models without disclosing specific medical conditions by:

• Detecting disability mentions in prompts
• Redacting medical/diagnostic terms
• Adding functional context for better AI responses
• Preserving user privacy and intent

🔄 How It Works

Input: "I'm paralyzed and need help finding accessible restaurants"
↓
Output: "I use mobility equipment and need help finding accessible restaurants. I need step-free access to buildings and accessible parking close to entrances."

🏗️ Architecture

• 14 Disability Categories with comprehensive subgroups
• Pattern Detection Engine for identifying disability mentions
• Redaction Engine for replacing medical terms
• Context Enrichment for adding functional needs
• Privacy Validation to ensure no medical data leaks

� Installation

pip install privacy-prompt-library

�🚀 Quick Start

Python API

from prompt_library import transform_prompt

# Transform a single prompt
result = transform_prompt("I'm blind and need coding help")
print(result['output'])
# "I use screen readers and need coding help. Please ensure any visual content includes text descriptions and is compatible with screen readers."

# Get library information
from prompt_library import get_library_info
info = get_library_info()
print(f"Library version: {info['version']}")

Command Line Interface

# Transform a prompt via CLI
privacy-prompt "I have ADHD and need focus strategies"

# Get library information
privacy-prompt --info

# Custom privacy level
privacy-prompt --privacy-level medium "Your prompt here"

Async Support

from prompt_library import PromptLibrary

library = PromptLibrary()
await library.initialize()
result = await library.transform_prompt("I'm autistic and need help with social situations")
print(result['output'])

📋 Categories Supported

1. Physical Disabilities
2. Visual Impairments
3. Hearing Impairments
4. Speech & Language
5. Intellectual Disabilities
6. Learning Disabilities
7. Autism Spectrum
8. Developmental Disabilities
9. Mental Health
10. Emotional & Behavioral
11. Invisible Disabilities
12. Multiple Disabilities
13. Neurological
14. Genetic & Rare Disorders

🔒 Privacy Guarantee

• ✅ No medical terms in output
• ✅ No diagnostic language
• ✅ Functional descriptions only
• ✅ Complete user anonymity

🐍 Python Package Features

This repository now includes a fully-featured Python package with:

• Modern Packaging: Uses pyproject.toml and is available on PyPI
• Async Support: Full async/await compatibility
• CLI Tool: Command-line interface for easy integration
• Type Hints: Complete typing for better development experience
• Testing: Comprehensive test suite with pytest

Python Installation & Usage

# Install from PyPI
pip install privacy-prompt-library

# Basic usage
python -c "from prompt_library import transform_prompt; print(transform_prompt('I have autism and need help'))"

# CLI usage
privacy-prompt --info
privacy-prompt "I'm deaf and need communication help"

Development Setup

# Clone repository
git clone https://github.com/git-markkuria/kanuni-layer-sdk.git
cd kanuni-layer-sdk

# Install in development mode
pip install -e .

# Run tests
pytest tests/

📁 Repository Structure

├── prompt_library/          # Python package
│   ├── core/               # Core processing engines
│   ├── engines/            # Context and redaction engines
│   ├── data/               # JSON data files
│   └── cli.py              # Command-line interface
├── src/                    # JavaScript/Node.js version
├── tests/                  # Python tests
├── pyproject.toml          # Python packaging config
└── package.json            # Node.js config

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.