DLOADIN/RoadGuard-Rwanda

# **Accident Detection System** 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. --- ## **Features** 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. --- ## **Technologies Used** - **Frontend**: Next.js - **Backend**: Flask - **Admin Dashboard**: Streamlit - **Machine Learning**: TensorFlow/Keras (for accident detection) - **Database**: MySQL - **Video Processing**: OpenCV --- ## **How to Run the Application** ### **1. Prerequisites** 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) --- ### **2. Clone the Repository** Clone the repository to your local machine: ```bash git clone github.com cd RoadGuard-Rwanda ``` --- ### **3. Set Up the Backend (Flask)** 1. Navigate to the `backend` folder: ```bash cd backend ``` 2. Install Python dependencies: ```bash 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: ```bash python app.py ``` The backend will be running at `localhost`. --- ### **4. Set Up the Fronten …