{"id":24957025,"url":"https://github.com/hasanulmukit/spam-email-classifier","last_synced_at":"2026-05-11T03:15:42.422Z","repository":{"id":275366600,"uuid":"925877384","full_name":"hasanulmukit/spam-email-classifier","owner":"hasanulmukit","description":"This is a Spam Email Classifier built using Python and Streamlit. It uses a pre-trained model to predict whether an email is Spam or Not Spam. 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It offers an interactive interface for predicting whether an email is **Spam** or **Not Spam**, with detailed probability scores, text input, and file upload support. The project uses a pre-trained model for robust predictions and features a user-friendly design.\n\n## Features\n\n- **Interactive Interface**: A user-friendly Streamlit interface.\n- **Text Input**: Allows users to input email text directly.\n- **File Upload**: Accepts `.txt` files containing email content.\n- **Detailed Results**:\n  - Displays prediction as \"Spam\" or \"Not Spam.\"\n  - Shows probabilities for both categories.\n- **Custom Styling**: Enhanced aesthetics with styled headers, buttons, and footers.\n\n## Tech Stack\n\n- **Programming Language**: Python\n- **Framework**: Streamlit\n- **Libraries**: Scikit-learn, Joblib\n\n## Installation Guide\n\n### Prerequisites\n\n1. **Python**: Ensure Python (\u003e=3.8) is installed.\n   - [Download Python](https://www.python.org/downloads/)\n2. **Streamlit**: Install using pip.\n   ```bash\n   pip install streamlit\n   ```\n3. **Joblib**: Ensure joblib is installed.\n   ```bash\n   pip install joblib\n   ```\n\n### Steps to Run\n\n1. **Clone the Repository**:\n\n   ```bash\n   git clone https://github.com/your-username/SpamEmailClassifierApp.git\n   cd SpamEmailClassifierApp\n   ```\n\n2. **Add the Pre-trained Model**:\n\n   - Save the trained model (`spam_classifier_model.pkl`) and vectorizer (`count_vectorizer.pkl`) in the project directory.\n\n3. **Run the App**:\n\n   ```bash\n   streamlit run app.py\n   ```\n\n4. **Access the App**:\n\n   - Open the URL displayed in the terminal (default: [http://localhost:8501/](http://localhost:8501/)).\n\n## Usage\n\n### Input Methods\n\n1. **Direct Text Input**:\n   - Paste email content into the provided text box.\n2. **File Upload**:\n   - Upload a `.txt` file containing the email content.\n\n### Output\n\n- **Prediction**:\n  - Displays whether the email is \"Spam\" or \"Not Spam.\"\n- **Probabilities**:\n  - Shows the likelihood of the email belonging to each category.\n\n## Future Enhancements\n\n- **Deployment**:\n  - Deploy on platforms like Heroku or Streamlit Cloud.\n- **Enhanced Visualization**:\n  - Add graphical visualizations for probabilities.\n- **Additional Input Formats**:\n  - Support for PDF and DOCX file uploads.\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE).\n\n## Acknowledgments\n\n- Streamlit Documentation: [Streamlit](https://docs.streamlit.io/)\n- Scikit-learn: [Scikit-learn](https://scikit-learn.org/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhasanulmukit%2Fspam-email-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhasanulmukit%2Fspam-email-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhasanulmukit%2Fspam-email-classifier/lists"}