AirSense Intelligence 🌍

A modern air quality monitoring dashboard with real-time data visualization, AI-powered insights, and smart forecasting capabilities.

✨ Features

• 📊 Real-time Monitoring - Track PM2.5, PM10, CO2, and other air quality parameters
• 🗺️ Interactive Maps - Visualize air quality data across different locations
• 🤖 AI Assistant - Get intelligent insights powered by Google Gemini
• 🧠 ML Forecasting - Custom Python ML models for accurate air quality predictions
• 📈 Smart Forecasting - 6-hour and 3-day air quality predictions with AQI calculations
• 🚨 Alert System - Email/SMS notifications for pollution levels
• 📱 Responsive Design - Works seamlessly on desktop and mobile

🚀 Quick Start

# Clone the repository
git clone https://github.com/PathanWasim/airsense.git
cd airsense

# Install dependencies
npm install

# Copy environment variables
cp .env.example .env

# Start development server
npm run dev

Visit http://localhost:5001 to see the application.

🛠️ Tech Stack

Frontend: React 18, TypeScript, TailwindCSS, shadcn/ui, Leaflet Maps
Backend: Express.js, PostgreSQL, Drizzle ORM, WebSocket
AI/ML: Custom Python ML models (Random Forest, Gradient Boosting), OpenAI, Google Gemini
Forecasting: Scikit-learn, TensorFlow, Flask API

📝 Environment Setup

Copy the example environment file and configure your API keys:

cp .env.example .env

Then update .env with your actual API keys:

# Database Configuration
DATABASE_URL=postgresql://username:password@localhost:5432/airsense

# AI API Keys (Required for AI features)
GOOGLE_AI_API_KEY=your_google_ai_api_key_here
OPENAI_API_KEY=your_openai_api_key_here

# Firebase Configuration (Required for real-time features)
VITE_FIREBASE_API_KEY=your_firebase_api_key_here
VITE_FIREBASE_PROJECT_ID=airsense-9c60e
# ... other Firebase config

⚠️ Security Note: Never commit API keys to version control. All sensitive keys should be stored in environment variables. See SECURITY.md for detailed security guidelines.

🧠 AI Forecasting Model

The project includes a custom Python-based machine learning system for air quality forecasting:

• Models: Random Forest & Gradient Boosting regressors for robust predictions
• Multi-pollutant Forecasting: Predicts PM2.5, PM10, CO2, NO2, SO2, and O3 levels
• Time Series Features: Incorporates lag features, rolling averages, and temporal patterns
• AQI Calculation: Converts pollutant concentrations to Air Quality Index
• REST API: Flask-based API for integration with the main application
• Synthetic Data Generation: Creates realistic training data with seasonal patterns

AI Model Features

• Weather Dependencies: Temperature, humidity, wind speed, pressure integration
• Temporal Patterns: Hour of day, day of week, seasonal variations
• Lag Features: Previous 1-hour and 24-hour values for trend analysis
• Rolling Averages: 3-hour and 24-hour moving averages
• Automatic Model Selection: Chooses best performing model per pollutant

Quick Start with AI

# Install Python dependencies
cd ai_model && pip install -r requirements.txt

# Train the models (generates synthetic training data)
python air_quality_forecaster.py

# Start the forecast API
python forecast_api.py

# Or use npm scripts
npm run ai:install && npm run ai:train && npm run dev:full

The AI service runs on http://localhost:5002 and provides forecasting endpoints.

API Endpoints

• GET /health - Health check endpoint
• POST /forecast - Generate air quality forecast with weather data
• POST /train - Retrain models with new data

📦 Available Scripts

npm run dev         # Start development server
npm run build       # Build for production
npm run start       # Start production server
npm run check       # TypeScript type checking

# AI Forecasting Model
npm run ai:install  # Install Python dependencies
npm run ai:train    # Train ML models
npm run ai:serve    # Start AI forecast API
npm run dev:full    # Start both Node.js and Python services

🏗️ Project Structure

├── client/          # React frontend
├── server/          # Express backend
├── shared/          # Shared types & schemas
├── ai_model/        # Python ML forecasting models
├── migrations/      # Database migrations
└── docker-compose.yml # Multi-service deployment

🤝 Contributing

1. Fork the project
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 file for details.

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Built with ❤️ for cleaner air and healthier communities