Spam Detector Frontend

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🌟 Overview

The Spam Detector Frontend is a React-based user interface designed to help users identify spam messages effortlessly. With its simple design and seamless integration with the backend API, this project ensures accurate spam detection while maintaining a user-friendly experience.

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🚀 Features

• 🔍 Real-time Spam Detection: Analyze messages to detect spam instantly.
• 🧠 AI-Powered Insights: Utilizes a machine learning model trained on diverse datasets for high accuracy.
• 📱 Responsive Design: Works seamlessly across desktops and mobile devices.
• 🎨 Modern UI: Built with React Native and styled with NativeWind for a sleek, responsive interface.

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🛠️ Tech Stack

• Frontend: React Native, NativeWind (Styling), Axios (API communication)
• Backend API: Flask (Python)
• Machine Learning: Scikit-learn, Pandas, NLTK, NumPy

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📂 Folder Structure

├── src/
│   ├── components/    # Reusable UI components
│   ├── pages/         # Screens and pages of the app
│   ├── services/      # API integration and data handling
│   └── styles/        # Styling files
├── public/            # Static assets and files
├── App.js             # Main application entry point
├── package.json       # Dependencies and scripts
└── README.md          # Project documentation

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🖥️ Installation and Setup

Follow these steps to run the project locally:

Prerequisites

• Node.js and npm installed
• React Native environment set up

Steps

1. Clone the repository:

   git clone https://github.com/swaraj-bose/Spam-Detector-Frontend.git
   cd Spam-Detector-Frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm start

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🧪 Usage

1. Launch the app on your preferred device or emulator.
2. Enter a message in the provided input box.
3. Click "Analyze" to check whether the message is spam or not.

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🛡️ Security and Privacy

This project does not store user data. All processing happens in real time to ensure user privacy.

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💡 Future Enhancements

• Add support for bulk message analysis.
• Introduce a dark mode for better user experience.
• Enhance AI model with additional training datasets.

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🤝 Contribution Guidelines

We welcome contributions!

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

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📝 License

This project is licensed under the MIT License.

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📧 Contact

For questions or suggestions, feel free to reach out:

• Author: Swaraj Bose
• Email: appypub@gmail.com
• GitHub: swaraj-bose

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