{"id":23119364,"url":"https://github.com/goldsharon/spamshield","last_synced_at":"2025-10-13T14:37:37.670Z","repository":{"id":243491687,"uuid":"812579994","full_name":"GoldSharon/SpamShield","owner":"GoldSharon","description":"SpamShield is a Flask-based web application that employs machine learning to swiftly identify and flag spam content in emails and text messages, offering users real-time protection against unwanted solicitations. 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It leverages Flask for the web framework and a Naive Bayes classifier for the spam detection model. The application provides an easy-to-use interface where users can input their messages and receive instant feedback on whether the message is spam or not.\n\n## Features\n- **User-Friendly Interface**: Clean and intuitive design for easy interaction.\n- **Real-Time Spam Detection**: Immediate results upon message submission.\n- **Advanced AI Technology**: Utilizes a Naive Bayes classifier for accurate spam detection.\n- **Privacy Assurance**: No data is stored or shared; user inputs are processed in real-time and discarded.\n\n## Project Structure\n\nSpamShield/\u003cbr\u003e\n│\u003cbr\u003e\n├── app.py # Main application file \u003cbr\u003e\n├── templates/\u003cbr\u003e\n│ ├── index.html # Home page template\u003cbr\u003e\n│ └── predict.html # Result page template\u003cbr\u003e\n├── models/\u003cbr\u003e\n│ ├── Spam_Model.joblib # Pre-trained model\u003cbr\u003e\n│ └── Vectorizer.joblib # Vectorizer for text transformation\u003cbr\u003e\n├── README.md # Project README file\u003cbr\u003e\n└── requirements.txt # Python dependencies\u003cbr\u003e\n\n\n## Installation\n1. **Clone the repository**:\n    ```sh\n    git clone https://github.com/GoldSharon/SpamShield.git\n    cd SpamShield\n    ```\n\n2. **Set up a virtual environment**:\n    ```sh\n    python -m venv venv\n    source venv/bin/activate  # On Windows, use `venv\\Scripts\\activate`\n    ```\n\n3. **Install dependencies**:\n    ```sh\n    pip install -r requirements.txt\n    ```\n\n4. **Download the pre-trained model and vectorizer**:\n    - Place `Spam_Model.joblib` and `Vectorizer.joblib` in the `models/` directory.\n\n## Usage\n1. **Run the application**:\n    ```sh\n    python app.py\n    ```\n\n2. **Access the web interface**:\n    Open your web browser and navigate to `http://127.0.0.1:5000/`.\n\n3. **Check for spam**:\n    - Enter the message you want to check in the provided text box.\n    - Click the \"Check\" button to get the result.\n\n## Dependencies\n- Flask\n- scikit-learn\n- joblib\n- numpy\n\nInstall dependencies using:\n```sh\npip install -r requirements.txt\n```\n## Contributing\nContributions are welcome! Please submit a pull request or open an issue to discuss improvements or suggestions.\n\n## License\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\n## Acknowledgements\nThe Naive Bayes model and TF-IDF vectorizer were trained using the scikit-learn library.\nFlask framework for the web application.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoldsharon%2Fspamshield","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgoldsharon%2Fspamshield","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoldsharon%2Fspamshield/lists"}