InboxArmour is a real-time educational phishing email detection tool. It integrates with Gmail via OAuth authentication and uses Cohere's AI-powered language analysis to provide users with insights about potentially malicious emails.

• Real-time Email Analysis: Detects phishing attempts and provides immediate feedback.
• Educational Warnings: Explains why an email might be dangerous to help users recognize phishing attempts.
• Gmail Integration: Uses OAuth to securely analyze emails.
• Cohere AI-powered Detection: Leverages Cohere's NLP models for phishing analysis.
• Flask Backend & React Frontend: Modern web technology stack for a seamless user experience.

• Backend: Flask (Python) with OAuth authentication
• Frontend: React (TypeScript) with Vite and ESLint
• Database: SQLite/PostgreSQL (configurable)
• AI Integration: Cohere NLP API

• Python 3.x
• Node.js & npm
• Gmail API credentials (OAuth 2.0 Client ID)

1. Clone the repository:

   git clone https://github.com/IDontKnow64/InboxArmour.git
   cd InboxArmour/backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables (create a .env file and add required keys, including Gmail OAuth credentials).
5. Run the backend:

   flask run

1. Navigate to the frontend directory:

   cd ../frontend

2. Install dependencies:

   npm install

3. Start the React app:

   npm start

To maintain a clean and organized codebase, you can structure your project as follows:

InboxArmour/
│── backend/             # Flask backend
│   ├── app.py           # Main application file
│   ├── models/          # Database models
│   ├── routes/          # API route definitions
│   ├── services/        # Business logic & integrations
│   ├── static/          # Static files (if needed)
│   ├── templates/       # HTML templates (if applicable)
│   ├── .env             # Environment variables
│
│── frontend/            # React frontend (TypeScript + Vite)
│   ├── src/             # Source files
│   │   ├── components/  # Reusable UI components
│   │   ├── pages/       # Page components
│   │   ├── hooks/       # Custom React hooks
│   │   ├── utils/       # Helper functions
│   │   ├── assets/      # Images, icons, etc.
│   ├── public/          # Static assets
│   ├── index.tsx        # Entry point
│   ├── vite.config.ts   # Vite configuration
│
│── README.md            # Project documentation
│── LICENSE              # License information

For better TypeScript linting, update eslint.config.js:

export default tseslint.config({
  extends: [
    ...tseslint.configs.recommendedTypeChecked,
    ...tseslint.configs.strictTypeChecked,
    ...tseslint.configs.stylisticTypeChecked,
  ],
  languageOptions: {
    parserOptions: {
      project: ['./tsconfig.node.json', './tsconfig.app.json'],
      tsconfigRootDir: import.meta.dirname,
    },
  },
})

You can also install additional plugins for React-specific linting:

// eslint.config.js
import reactX from 'eslint-plugin-react-x'
import reactDom from 'eslint-plugin-react-dom'

export default tseslint.config({
  plugins: {
    'react-x': reactX,
    'react-dom': reactDom,
  },
  rules: {
    ...reactX.configs['recommended-typescript'].rules,
    ...reactDom.configs.recommended.rules,
  },
})

Once both the frontend and backend are running, navigate to http://localhost:5173/ in your browser. Log in with Gmail to authorize access, and InboxArmour will analyze your emails in real-time, flagging potential phishing attempts.

This project is structured based on the best practices from the Creating a Chrome Extension with React and Vite Boilerplate and modern React development standards using Vite and TypeScript.

• Localized and Specialized AI Model: Develop a proprietary AI model tailored specifically for email security to improve detection accuracy.
• Compare and Contrast AI Models: Implement a system to compare multiple AI models in real-time, selecting the best-performing one for enhanced security.
• Hidden Verification Exchange with Companies: Collaborate with authentic companies to create a hidden verification mechanism, improving email trustworthiness without exposing sensitive data.

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Make your changes and commit:

   git commit -m "Description of changes"

4. Push to your fork:

   git push origin feature/your-feature-name

5. Open a pull request!

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

For any inquiries or issues, feel free to open an issue on GitHub or contact the maintainers directly.