Firefly is a comprehensive system designed for real-time fire and smoke detection. Leveraging machine learning, a robust API, and a user-friendly frontend, Firefly provides timely alerts to ensure safety and prevent potential disasters.

• Real-time Video Analysis: Continuously monitors video streams for signs of fire and smoke.
• Advanced Machine Learning: Employs the VGG16 model for accurate and efficient detection.
• Scalable API: Built with FastAPI for high performance and easy integration.
• Persistent Data Storage: Utilizes MongoDB Atlas for secure and reliable data management.
• User Authentication: Secure user registration and login functionality.
• Email Alerts: Automated email notifications upon detection of fire or smoke.
• Intuitive Frontend: User-friendly interface for monitoring and managing the system.
• Alert Logging: Detailed logs of detection events for analysis and review.

Firefly/ (Root)
│
├── src/
│   ├── fire_detection_backend/ (Node.js Backend)
│   │   ├── server.js
│   │   ├── ... (other backend files)
│   │   ├── models/
│   │   │   ├── Session.js
│   │   │   ├── User.js
│   │   │   ├── video.js
│   │   ├── userRoutes.js
│   │   ├── fireAlarm.js (Optional)
│   │   ├── .env
│   │   └── package.json
│   │
│   ├── api/ (FastAPI Python API)
│   │   ├── api.py
│   │   ├── fs_model_v2.pkl (ML Model)
│   │   └── ... (other API files)
│   │
│   └── fire_detection_frontend/ (Frontend)
│       ├── index.html
│       ├── script.js
│       ├── style.css
│       └── ... (other frontend assets)
│
├── README.md
├── LICENSE
└── ... (other project files)

• Machine Learning: TensorFlow, Keras, VGG16, Pickle
• API: FastAPI (Python), Uvicorn, HTTPX
• Backend: Node.js, Express, Mongoose, JWT, Bcrypt, Nodemailer, Sharp
• Database: MongoDB Atlas
• Frontend: HTML, CSS, JavaScript
• Other: CORS, Dotenv, Axios

1. Clone the Repository:

   git clone [https://github.com/sgindeed/Firefly.git](https://github.com/sgindeed/Firefly.git)
   cd Firefly

2. Backend Setup (Node.js):

   • Navigate to the src/fire_detection_backend directory.

     cd src/fire_detection_backend

   • Install dependencies:

     npm install

   • Create a .env file and configure your environment variables (e.g., MONGO_URI, JWT_SECRET, EMAIL_USER, EMAIL_PASS).

   • Start the backend server:

     npm start

3. API Setup (Python):

   • Navigate to the src/api directory.

     cd ../api

   • (Optional but Recommended) Create a virtual environment:

     python3 -m venv venv
     source venv/bin/activate  # On Linux/macOS
     # venv\Scripts\activate   # On Windows

   • Install dependencies:

     pip install -r requirements.txt
     pip install fastapi uvicorn tensorflow keras pillow httpx

   • Run the API:

     uvicorn api:app --reload --port 8001

4. Frontend Setup:

   • Navigate to the src/fire_detection_frontend directory.

     cd ../fire_detection_frontend

   • Open index.html in your browser.

We welcome contributions to Firefly! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Submit a pull request.

A huge thank you to our dedicated team of developers:

• Supratim Ghosh: 🔥 Smoke and Fire Detection using ML (VGG16 Model)
• Suvojit Adak: 🐍 FastAPI Python API Development
• Mahak Gupta: 💾 MongoDB Atlas Database Integration & Node.js Backend
• Anushka Adak: 🎨 Frontend Development (HTML, CSS, JS) & 🔍 Project Research
• Aishee Guha Biswas: 💻 Frontend Development

This project is licensed under the MIT License.

• We extend our gratitude to the open-source community for providing the tools and libraries that made this project possible.
• Special thanks to the kaggle, TensorFlow, Keras, FastAPI, Node.js, and MongoDB communities.

For any inquiries or feedback, please feel free to reach out to the contributors through their GitHub usernames:

• Supratim Ghosh: sgindeed
• Suvojit Adak: SuojitAdak01
• Mahak Gupta: Mahak-27
• Anushka Adak: anushkaadak
• Aishee Guha Biswas: Aishee06