Update README.md

README.md

@@ -1,13 +1,81 @@
 ---
-title: Malicious Email And Url Detector V2
-emoji: 🦀
-colorFrom: pink
-colorTo: indigo
+title: Malicious Email & URL Detector v2
+emoji: 🛡️
+colorFrom: red
+colorTo: yellow
 sdk: streamlit
-sdk_version: 1.44.0
+sdk_version: 1.43.2
 app_file: app.py
 pinned: false
-short_description: A web app for detecting malicious Email and URL version 2
+short_description: A web app for detecting malicious emails and URLs
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Malicious Email & URL Detector v2
+
+A lightweight **Streamlit** web application that utilizes a fine-tuned deep learning model to detect malicious content in emails and URLs. The app helps individuals and organizations identify threats such as **phishing** and **malware** before any harm can occur.
+
+---
+
+## Key Features
+
+- **Real-Time Detection**  
+  Quickly classifies emails or URLs as **malicious** or **benign** using a fine-tuned transformer model.
+- **User-Friendly Interface**  
+  Paste the email text or URL, then click a button—no advanced knowledge required.
+- **Lightweight & Fast**  
+  Built on Streamlit for a snappy, interactive experience.
+
+---
+
+## How It Works
+
+1. **Model**  
+   A fine-tuned variant of [distilroberta-base](https://huggingface.co/distilroberta-base) trained on a curated dataset of phishing, malware, and legitimate examples.
+2. **Input**  
+   Users provide either an email’s textual content or a single URL. The app normalizes and processes the input.
+3. **Inference**  
+   The model returns a **label** (malicious/benign) and a **confidence score**, enabling quick decisions on blocking or flagging potential threats.
+
+---
+
+## Quickstart
+
+1. **Clone the Repository**  
+   ```bash
+   git clone https://huggingface.co/spaces/your-username/Malicious-Email-and-URL-Detector-v2
+   cd Malicious-Email-and-URL-Detector-v2
+
+2. **Install Dependencies**
+   pip install -r requirements.txt
+
+3. **Run the App**  
+   streamlit run app.py
+
+4. **Use It** 
+   Step 1: Paste the email content or URL into the input box.
+
+   Step 2: Click Analyze.
+
+   Step 3: View the output displaying the classification (malicious or benign) and the confidence score.
+
+6. **Example**
+
+   Input:
+
+   "Hello, your account has been locked. Please verify at http://suspicious-link.com"
+
+   Output:
+
+   Malicious (Confidence: 0.95)
+
+
+## Limitations
+Limitations
+False Positives/Negatives: No model is perfect. Always combine with other security measures.
+
+Dataset Bias: Performance depends on how well the training data represents real-world threats.
+
+Evolving Threats: Regular updates are recommended to keep pace with new phishing or malware tactics.
+
+## Contact
+Author: Eason Liu