A newer version of the Streamlit SDK is available:
1.52.2
metadata
license: wtfpl
sdk: streamlit
sdk_version: 1.48.1
title: NZ Legislation Loophole Analyzer emoji: ⚖️ colorFrom: blue colorTo: purple sdk: streamlit sdk_version: "1.28.0" app_file: app.py pinned: false license: mit models: - DavidAU/Qwen3-Zero-Coder-Reasoning-0.8B-NEO-EX-GGUF tags: - ai - legal - legislation - analysis - new-zealand - llm - streamlit - law - loopholes - compliance - huggingface-spaces short_description: AI-powered analysis of New Zealand legislation to identify potential loopholes and ambiguities long_description: A powerful AI-powered web application for analyzing New Zealand legislation to identify potential loopholes, ambiguities, and unintended consequences using advanced language models and intelligent caching. Optimized for Hugging Face Spaces deployment with efficient memory management and streaming responses. privacy: public duplicate_from: "" secrets: {} storage: small
NZ Legislation Loophole Analyzer
A powerful AI-powered web application for analyzing New Zealand legislation to identify potential loopholes, ambiguities, and unintended consequences. Built with advanced caching and real-time performance monitoring.
🌟 Key Features
🤖 AI-Powered Legal Analysis
- Specialized NZ Legislation Analysis: Optimized for New Zealand legal texts with Treaty of Waitangi references
- Multiple Analysis Depths: Standard, Detailed, and Comprehensive analysis modes
- Intelligent Text Processing: Sentence-aware chunking with legal document structure preservation
🧠 Advanced Context Memory Cache
- Smart Caching System: Hash-based identification prevents re-processing identical content
- Memory-Efficient: Optimized for cloud environments with automatic cache management
- Performance Boost: Significant speed improvements for large document analysis
🎨 Modern Web Interface
- Streamlit-Powered: Clean, responsive interface that works on any device
- Real-Time Progress: Live progress bars and processing status updates
- Interactive Results: Expandable analysis results with confidence scoring
🚀 Quick Start
- Upload Legislation: Use the file uploader to select NZ legislation files (JSON lines, JSON arrays, or raw text)
- Configure Analysis: Adjust model parameters and analysis settings
- Process & Analyze: Click "Start Processing" to begin AI-powered analysis
- Review Results: Explore detailed findings with interactive visualizations
- Export Data: Download results in JSON, CSV, or Excel formats
📊 Analysis Capabilities
- Loophole Detection: Identify potential legal ambiguities and exploitable interpretations
- Risk Assessment: Evaluate legal risks and unintended consequences
- Circumvention Analysis: Explore potential methods for bypassing legal provisions
- Recommendations: Receive specific suggestions for legislative improvements
🛠️ Technical Features
- Memory Optimized: Designed for cloud deployment with efficient resource usage
- Session-Based Caching: Intelligent caching that works within Spaces limitations
- Performance Monitoring: Real-time metrics and performance recommendations
- Batch Processing: Handle multiple files simultaneously
- Quality Metrics: Confidence scoring and analysis validation
🔧 Configuration
Model Settings
- Local Models: Support for GGUF format models
- HuggingFace Integration: Direct model downloads from HuggingFace Hub
- Parameter Tuning: Adjustable temperature, context length, and sampling parameters
Processing Options
- Chunk Size: Configurable text chunk sizes (256-8192 characters)
- Analysis Depth: Three levels of analysis detail
- Cache Size: Memory-efficient caching system
📈 Performance & Monitoring
- Real-Time Metrics: Memory usage, CPU utilization, and processing speed
- Cache Statistics: Hit rates, evictions, and cache efficiency
- Performance Recommendations: Automated suggestions for optimization
🔍 Analysis Output
Each analysis provides:
- Text Meaning: Clear explanation of legal provision intent
- Key Assumptions: Identified assumptions that could be exploited
- Critical Findings: Specific loopholes and ambiguities
- Confidence Scores: AI confidence in analysis results
- Recommendations: Suggested improvements and clarifications
🆘 Limitations & Recommendations
Spaces-Specific Considerations
- Memory Limits: Optimized for 2-8GB RAM environments
- Session-Based: Cache persists only during active sessions
- Model Size: Choose appropriately sized models for Spaces constraints
Recommended Models
- Small Models: Qwen 0.8B variants for faster processing
- Medium Models: Qwen 1.5B-3B for balanced performance
- API Integration: Consider using external APIs for larger models
📚 Documentation
For detailed documentation, see:
🤝 Contributing
This is a demo application for Hugging Face Spaces. For improvements or modifications:
- Fork the repository
- Make your changes
- Test thoroughly
- Submit a pull request
📄 License
MIT License - see LICENSE file for details.
⚖️ Built with Streamlit & Llama.cpp | Optimized for Hugging Face Spaces