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| title: Phishing Email Detector | |
| emoji: 🎣 | |
| sdk: docker | |
| app_port: 7860 | |
| --- | |
| ## Phishing Email Detector 🎣 | |
| This project is a web-based tool designed to help users identify potentially malicious phishing emails. By pasting the text content of an email, the application leverages a fine-tuned transformer model from the Hugging Face Hub to analyze the content and classify its likelihood of being a phishing attempt. | |
| It serves as a practical, end-to-end example of building and deploying a machine learning application as an interactive web service. | |
| ## Key Features | |
| Simple Web Interface: An easy-to-use text area for pasting email content for analysis. | |
| Real-Time Analysis: Utilizes a DistilBERT-based model to provide instant classification. | |
| Clear Predictions: Outputs a primary classification (e.g., "Phishing Link Detected", "Legitimate Email") along with a confidence score. | |
| Detailed Breakdown: Displays the model's confidence scores across all possible output labels for greater transparency. | |
| Containerized & Reproducible: Packaged with Docker, ensuring a consistent environment for both development and deployment. | |
| ## Tech Stack | |
| Backend: Python, Flask, Gunicorn | |
| Machine Learning: Hugging Face Transformers, PyTorch | |
| Frontend: HTML, CSS (via Jinja2 templates) | |
| Deployment: Docker, Hugging Face Spaces | |
| ## Live Demo | |
| 🚀 You can try the live application here: |