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README.md

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-# Phishing Email Detector
+## 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.
 
-## Description
+It serves as a practical, end-to-end example of building and deploying a machine learning application as an interactive web service.
 
-This project is a web application built with Flask that analyzes email text content to detect potential phishing attempts. It utilizes a pre-trained machine learning model from the Hugging Face Transformers library to classify emails. [cite: 1]
+## Key Features
+Simple Web Interface: An easy-to-use text area for pasting email content for analysis.
 
-## Features
+Real-Time Analysis: Utilizes a DistilBERT-based model to provide instant classification.
 
-* Provides a simple web interface to paste and analyze email text.
-* Uses the `cybersectony/phishing-email-detection-distilbert_v2.4.1` model for classification. [cite: 1]
-* Displays the most likely classification (e.g., "Likely Legitimate", "Suspicious / Phishing Link Likely"). [cite: 1]
-* Shows the confidence score for the top prediction. [cite: 1]
-* Provides detailed probabilities for all classification categories considered by the model. [cite: 1]
+Clear Predictions: Outputs a primary classification (e.g., "Phishing Link Detected", "Legitimate Email") along with a confidence score.
 
-## Dependencies
+Detailed Breakdown: Displays the model's confidence scores across all possible output labels for greater transparency.
 
-* Python 3.x
-* Flask [cite: 1]
-* Hugging Face Transformers (`transformers`) [cite: 1]
-* PyTorch (`torch`) [cite: 1]
+Containerized & Reproducible: Packaged with Docker, ensuring a consistent environment for both development and deployment.
 
-You can install the Python dependencies using pip:
-```bash
-pip install Flask transformers torch
+## 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: