- Model: qwen2.5-coder-7b-instruct (7 billion parameters)
- Purpose: Generate urban planning recommendations
- Location: localhost:1234
- Benefits:
- Complete privacy (no data leaves your machine)
- No internet required for AI processing
- Zero API costs
- Full control over AI responses
- Models: GPT-3.5/GPT-4 series
- Purpose: Fallback when LMStudio unavailable
- Use Cases:
- Hosted deployments
- Production scaling
- Demo environments without local AI
- Purpose: AI-powered code completion and assistance
- Integration: VS Code extension
- Usage: Throughout entire development process
- Benefits:
- Faster code writing
- Bug detection and fixes
- Code refactoring suggestions
- Best practices recommendations
- Geographic Context: Understands Indian cities and local conditions
- Cost Estimation: Municipal budgets in Indian Rupees (βΉ)
- Timeline Planning: Realistic implementation schedules
- Policy Generation: Municipal-ready action plans
- Data Interpretation: Converts NASA satellite data into actionable insights
- Flood Mitigation: Drainage systems, retention basins
- Urban Heat Reduction: Green infrastructure, cooling systems
- Population Protection: Early warning systems, evacuation plans
- Sustainable Development: Climate-resilient building codes
User Input β NASA Data Analysis β AI Processing (Local/Cloud) β
Context-Aware Analysis β Municipal Recommendations β Action Plans
- Development Speed: GitHub Copilot accelerates coding
- Privacy Protection: LMStudio keeps data local
- Production Flexibility: OpenAI enables cloud deployment
- Reliability: Multiple fallback options ensure system availability
3 AI Systems working together:
- LMStudio - Core recommendation engine
- OpenAI API - Cloud backup for hosting
- GitHub Copilot - Development acceleration
This multi-AI architecture ensures robust, private, and scalable urban planning intelligence! ππ