This is an Accident Detection System that uses machine learning to detect accidents in video footage. The system consists of a frontend built with Next.js, a backend built with Flask, and a Streamlit admin dashboard for managing and analyzing accident data.

1. Accident Detection:

   • Detects accidents in uploaded video footage.
   • Displays the severity score and accuracy in real-time.

2. Admin Dashboard:

   • Upload and process video footage.
   • View live video analysis with severity and accuracy overlays.
   • View accident statistics and data in interactive charts.

3. Database Integration:

   • Saves accident data (timestamp, location, severity, etc.) to a MySQL database.

4. Real-Time Notifications:

   • Simulates real-time accident notifications for testing purposes.

• Frontend: Next.js
• Backend: Flask
• Admin Dashboard: Streamlit
• Machine Learning: TensorFlow/Keras (for accident detection)
• Database: MySQL
• Video Processing: OpenCV

Before running the application, ensure you have the following installed:

• Python 3.8+
• Node.js (for Next.js frontend)
• MySQL (for the database)
• Git (optional, for cloning the repository)

Clone the repository to your local machine:

git clone https://github.com/DLOADIN/RoadGuard-Rwanda
cd RoadGuard-Rwanda

1. Navigate to the backend folder:

   cd backend

2. Install Python dependencies:

   pip install -r requirements.txt

3. Set up the MySQL database:

   • Create a database named accident_detection.
   • Update the database credentials in backend/config.py.

4. Run the Flask server:

   python app.py

   The backend will be running at http://localhost:5000.

1. Navigate to the frontend folder:

   cd frontend

2. Install Node.js dependencies:

   npm install

3. Run the Next.js development server:

   npm run dev

   The frontend will be running at http://localhost:3000.

1. Navigate to the admin folder:

   cd admin

2. Install Python dependencies:

   pip install -r requirements.txt

   The admin dashboard will be running at http://localhost:8501.

1. Ensure MySQL is running on your machine.

2. Create a database named accident_detection.

3. Run the following SQL query to create the accidents table:

   CREATE TABLE IF NOT EXISTS accidents (
       ID INT AUTO_INCREMENT PRIMARY KEY,
       timestamp DATETIME NOT NULL,
       location VARCHAR(255) NOT NULL,
       severity_level ENUM('low', 'medium', 'high') NOT NULL,
       severity_score FLOAT NOT NULL,
       video_path VARCHAR(255) NOT NULL,
       accuracy FLOAT NOT NULL
   );

4. Update the database credentials in backend/config.py and admin/admin.py.

1. Start the Flask backend:

   cd backend
   python app.py

2. Start the Next.js frontend:

   cd frontend
   npm run dev

3. Start the Streamlit admin dashboard:

   cd admin
   streamlit run admin.py

4. Open your browser and navigate to:

   • Frontend: http://localhost:3000
   • Admin Dashboard: http://localhost:8501

/RoadGuard-Rwanda/
.project
├── app
│   ├── Dashboard
│   │   ├── Analytics
│   ├── components
│   │   ├── dashboard-charts.tsx
│   │   ├── dashboard-content.tsx
│   │   ├── header.tsx
│   │   ├── maindatatable.tsx
│   │   ├── notifications-table.tsx
│   │   ├── overview-cards.tsx
│   │   ├── overview-section.tsx
│   │   ├── search.tsx
│   │   ├── trend-charts.tsx
│   │   ├── user-nav.tsx
│   │   ├── video-highlights.tsx
│   │   ├── video-upload.tsx
│   ├── highlights
│   ├── styles
│   ├── ui
│   ├── upload
│   ├── layout.tsx
│   ├── page.tsx
│   ├── sidebar.tsx
│   ├── styles.css
├── utils
│   ├── favicon.ico
│   ├── global.ts
│   ├── layout.tsx
├── components/ui
│   ├── aurora-background.tsx
│   ├── backgrounditem.tsx
│   ├── benjostick.tsx
│   ├── fact.tsx
│   ├── footer.tsx
│   ├── globalcoverage.tsx
│   ├── hero.tsx
│   ├── navbar.tsx
│   ├── roadadvisory.tsx
├── node_modules
├── public
├── .gitignore
├── AUTHORS
├── CI components.json
├── declarations.d.ts
├── depcheck.ts
├── get-commits.sh
├── next.config.ts
├── package.json
├── package-lock.json
├── backend/                # Flask backend
│   ├── app.py              # Flask application
│   ├── config.py           # Database configuration
│   └── requirements.txt    # Python dependencies
│
├── frontend/               # Next.js frontend
│   ├── pages/              # Next.js pages
│   ├── public/             # Static assets
│   ├── styles/             # CSS files
│   └── package.json        # Node.js dependencies
│
├── admin/                  # Streamlit admin dashboard
│   ├── camera.py           # Video processing script
│   └── requirements.txt    # Python dependencies
│
└── README.md               # Project documentation

1. Go to the Admin Dashboard (http://localhost:8501).
2. Navigate to the Upload Footage page.
3. Upload a video file (supported formats: .mp4, .avi, .mov).
4. The video will be processed, and the analysis will be displayed in real-time.

1. Go to the Admin Dashboard (http://localhost:8501).
2. Navigate to the Data page to view accident records from the database.

1. Go to the Admin Dashboard (http://localhost:8501).
2. Navigate to the Statistics page to view interactive charts and graphs.

1. Database Connection Issues:

   • Ensure MySQL is running and the database credentials are correct.
   • Check the logs for any errors related to the database connection.

2. Video Processing Issues:

   • Ensure OpenCV is installed correctly (pip install opencv-python).
   • Check the logs for any errors related to video processing.

3. Streamlit App Not Running:

   • Ensure all dependencies are installed (pip install -r requirements.txt).
   • Check the logs for any errors related to Streamlit.

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeatureName).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeatureName).
5. Open a pull request.

For any questions or issues, please contact:

• Your Name: [email protected]
• GitHub: DLOADIN

Enjoy using the RoadGuard Rwanda! 🚀