streamlit_src/README.md

Utrecht Air Quality Monitoring Application 🌍💨

Welcome to the Utrecht Air Quality Monitoring application! This interactive app, built with Streamlit, provides timely insights into the levels of key air pollutants—NO₂ and O₃—in Utrecht, helping users monitor and forecast air quality based on WHO standards.

---

🌟 Features

• Real-Time Monitoring: Stay updated on today’s air quality with data updated every 6 hours.
• 3-Day Forecasts: View predicted NO₂ and O₃ levels for the upcoming three days.
• User & Admin Dashboards: Easy-to-use interfaces for general users and detailed analytics for admins.
• Comprehensive MVC Structure: Organized codebase for easy navigation, maintenance, and scalability.

---

📂 Folder Structure

streamlit_src/
├── controllers/
│   ├── home_controller.py      # Manages logic for the main dashboard and user access
│   ├── user_controller.py      # Controls data display on the user dashboard
│   └── admin_controller.py     # Extends user_controller for admin-specific features
│
├── json_interactions/          # Handles JSON data interactions for configuration and storage
│   └── (JSON handling scripts)
│
├── models/
│   └── air_quality_model.py    # Contains model loading, preprocessing, and prediction logic
│
├── views/
│   ├── home_view.py            # Sets up the main interface and role selection
│   ├── user_view.py            # Displays air quality data and forecasts for users
│   └── admin_view.py           # Presents analytics and monitoring tools for admins
│
├── README.md                   # Documentation for the Streamlit application
├── __init__.py                 # Initializes the streamlit_src package
└── app.py                      # Main entry point to run the Streamlit application

---

⚙️ Design Pattern: MVC

To make the application easy to manage and extend, we’ve structured it using the Model-View-Controller (MVC) design pattern. Here’s how each part contributes:

1. Model (air_quality_model.py)

Our Model forms the backbone of the application, handling:

• Data loading and preprocessing to ensure consistency in predictions.
• Air quality prediction models for NO₂ and O₃, using data collected over past days.
• Prediction logic that leverages our trained models to provide accurate, actionable forecasts.

2. Views (home_view.py, user_view.py, admin_view.py)

The View components shape the look of the application, providing different displays for each user role:

• home_view.py: Sets up the main page layout, including a field for users to switch roles or log in as an admin.
• user_view.py: Manages the visual elements and display of air quality forecasts for NO₂ and O₃ on the user dashboard.
• admin_view.py: Handles all visual elements on the admin dashboard, including advanced metrics, monitoring tools, and model performance data.

3. Controllers (home_controller.py, user_controller.py, admin_controller.py)

Our Controller modules serve as intermediaries, connecting the model's data and logic with the views’ visual representation. Each controller is aligned with a specific view:

• home_controller.py: Manages the logic for the main dashboard, handling user access and controlling the display of either the user or admin dashboard.
• user_controller.py: Manages the logic for the user dashboard, making use of the user_view.py script to correctly format the user dashboard including.
• admin_controller.py: Manages the logic for the admin dashboard, extending the UserController to provide additional features tailored to admin needs.

---

🚀 How to Run the App

To launch the Utrecht Air Quality Monitoring application, follow these simple steps:

1. Navigate to the streamlit_src folder in your terminal where the app files are located.

2. Run the Streamlit application by entering the following command:

   streamlit run app.py
   > [!TIP]
   > **Alternative Path**: If you are not in the `streamlit_src` folder, provide the full path to `app.py`. For example, from the root directory:
   > - **Windows**: 
   >   ```bash
   

streamlit run .\streamlit_src\app.py

- **macOS/Linux**: 
```bash
streamlit run ./streamlit_src/app.py