A comprehensive system for receiving, storing, and visualizing data from IoT sensors in real-time.

• Real-time Data Collection: Receive sensor data via REST API or MQTT protocol
• Data Storage: Persist sensor readings in a SQLite database (configurable for PostgreSQL)
• Interactive Dashboard: Visualize sensor data with real-time charts and graphs
• Alerting System: Configure thresholds and receive alerts when values exceed normal ranges
• Data Export: Download historical sensor data in CSV format
• Anomaly Detection: Automatically identify unusual sensor readings

The system follows a modern architecture with separate backend and frontend components:

• Backend: FastAPI application that handles data collection, processing, and storage
• Frontend: Streamlit dashboard for visualization and user interaction
• Database: SQLite (default) or PostgreSQL for data persistence
• Communication: REST API and MQTT for receiving sensor data

• Python 3.8 or higher
• pip (Python package manager)
• Virtual environment (recommended)

1. Clone the repository:

   git clone [https://github.com/yourusername/iot-dashboard.git](https://github.com/Wisauw/PyDash.git)
   cd iot_dashboard

2. Create and activate a virtual environment:

   # On Windows
   python -m venv iot_dashboard_env
   iot_dashboard_env\Scripts\activate
   
   # On macOS/Linux
   python -m venv iot_dashboard_env
   source iot_dashboard_env/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

The application uses environment variables for configuration. You can set these in a .env file in the project root:

# Database settings
DATABASE_URL=sqlite:///./iot_data.db

# MQTT settings (optional)
MQTT_BROKER=localhost
MQTT_PORT=1883
MQTT_TOPIC=sensors/#

# Alert thresholds
TEMPERATURE_MIN=10.0
TEMPERATURE_MAX=30.0
HUMIDITY_MIN=20.0
HUMIDITY_MAX=80.0

# Notification settings (optional)
TELEGRAM_TOKEN=your_telegram_bot_token
TELEGRAM_CHAT_ID=your_chat_id
EMAIL_FROM=your_email@example.com
EMAIL_PASSWORD=your_email_password
EMAIL_TO=recipient@example.com
SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587

1. Start the backend server:

   uvicorn backend.main:app --reload

   The API will be available at http://localhost:8000 with API documentation at http://localhost:8000/docs

2. In a separate terminal, start the Streamlit dashboard:

   streamlit run frontend/dashboard.py

   The dashboard will be available at http://localhost:8501

3. To simulate sensor data for testing, run:

   python backend/sensor_simulator.py

The backend provides the following API endpoints:

• GET /api/sensors - Get a list of all sensors
• GET /api/sensors/{sensor_id}/readings - Get readings for a specific sensor
• POST /api/sensors/data - Submit new sensor data
• GET /api/alerts - Get recent alerts
• GET /api/status - Check API status

iot_dashboard/
├── backend/
│   ├── __init__.py
│   ├── config.py           # Configuration settings
│   ├── database.py         # Database models and connections
│   ├── mqtt_client.py      # MQTT subscriber
│   ├── main.py             # FastAPI application
│   ├── data_processor.py   # Process and store sensor data
│   ├── alert_system.py     # Alerting mechanisms
│   └── sensor_simulator.py # Test data generator
│
├── frontend/
│   ├── __init__.py
│   ├── dashboard.py        # Streamlit dashboard app
│   ├── charts.py           # Chart components
│   └── utils.py            # Utility functions
│
├── requirements.txt        # Project dependencies
├── .env                    # Environment variables (not in git)
├── .gitignore              # Git ignore rules
└── README.md               # Project documentation

1. Define threshold values in config.py
2. Add alert conditions in alert_system.py
3. Update the simulator to generate test data

The dashboard is built with Streamlit and can be easily customized by editing the frontend/dashboard.py file. Chart components and utility functions are modular and can be extended.

For production deployments, PostgreSQL is recommended:

1. Install PostgreSQL and create a database
2. Update the DATABASE_URL in your configuration
3. Install the PostgreSQL driver: pip install psycopg2-binary

Contributions are welcome! Please feel free to submit a Pull Request.

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

• FastAPI for the efficient web framework
• Streamlit for the easy-to-use dashboard capabilities
• Plotly for interactive visualizations