Real-time capacity monitoring and prediction system for Zurich's Hallenbad City swimming pool. The app helps visitors plan their swim sessions by providing current occupancy data and AI-powered predictions of pool capacity.
- Live Capacity Monitoring: Real-time updates of current pool occupancy
- Smart Predictions: Machine learning-based forecasts for the next 5 days
- Time Period Analysis: Capacity predictions broken down into six daily periods:
- Early Morning (6:00-9:00)
- Late Morning (9:00-11:00)
- Lunch (11:00-13:00)
- Afternoon (13:00-16:00)
- After Work (16:00-19:00)
- Evening (19:00-22:00)
- Interactive Visualization: Color-coded occupancy levels and detailed hourly charts
- Offline Support: Local caching of predictions for faster access
- Flutter framework for cross-platform deployment
- Material Design 3 with custom theming
- FL Chart for data visualization
- Firebase Analytics for usage tracking
- Firebase Cloud Functions
- Cloud Firestore for data storage
- Python for data processing and ML predictions
- WebSocket integration with Badi monitoring system
- Flutter SDK
- Python 3.8+
- Firebase CLI
- Google Cloud account with Firebase project
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Clone the repository
git clone https://github.com/yourusername/badi_counts.git cd badi_counts -
Frontend Setup
flutter pub get
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Backend Setup
cd functions pip install -r requirements.txt -
Firebase Configuration
- Create a new Firebase project
- Enable Firestore and Cloud Functions
- Add your Firebase configuration to
lib/firebase_options.dart - Configure environment variables:
# In Firebase Functions environment DBOS_PREDICT_URL=your_prediction_service_url
-
Start the Flutter app
flutter run
-
Run Cloud Functions locally
cd functions firebase emulators:start
badi_counts/
├── lib/ # Flutter app source
│ ├── main.dart # App initialization and theme setup
│ ├── home_page.dart # Main dashboard view
│ └── widgets/ # Reusable UI components
├── functions/ # Python backend functions
│ ├── main.py # Core functionality and scheduling
│ └── requirements.txt
├── dbos_fct/ # ML prediction service
├── public/ # Web assets
└── android/ # Android-specific configs
- Data Collection: Automated collection of pool capacity data every 10 minutes (6:00-22:00)
- Predictions: ML model updates every 2 hours for accurate capacity forecasting
- Caching: Local storage of predictions with 30-minute validity
- Period Analysis: Smart aggregation of data into meaningful time periods
Contributions are welcome! Please feel free to submit a Pull Request.
MIT License - See the LICENSE file for details.