Update README.md

README.md

@@ -1,36 +1,80 @@
 ---
-title: Malicious Email & Url Detector
-emoji: 📊
+title: Malicious Email & URL Detector
+emoji: 🛡️
 colorFrom: red
 colorTo: yellow
 sdk: streamlit
 sdk_version: 1.43.2
 app_file: app.py
 pinned: false
-short_description: A web app for detecting malicious Email and URL
+short_description: A web app for detecting malicious emails and URLs
 ---
 
 # Malicious Email & URL Detector
 
-This is the first version of **Malicious-URL-Detector**, a web application built using Streamlit that leverages a fine-tuned deep learning model to detect malicious emails and URLs. The application analyzes input text—whether it’s the content of an email or a URL string—and classifies it as either malicious (e.g., phishing or malware) or benign.
+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.
 
-## How It Works
+---
+
+## Key Features
 
-- **Model Integration:**  
-  The app uses a model fine-tuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) for text classification. The model has been trained on a curated dataset comprising phishing, malware, and legitimate examples, enabling it to recognize suspicious patterns and linguistic cues.
+- **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.
+
+---
 
-- **User Interface:**  
-  Built with Streamlit, the web app offers a simple and intuitive interface where users can paste the content of an email or a URL. Upon submission, the model processes the input and returns a prediction indicating whether the text is malicious or benign, along with a confidence score.
+## How It Works
 
-- **Real-Time Detection:**  
-  Designed for real-time threat detection, the application helps organizations and individual users quickly identify potentially harmful links before they are accessed, thereby contributing to enhanced cybersecurity defenses.
+1. **Model**  
+   A fine-tuned variant of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) (or your chosen model) 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.
 
-## Getting Started
+---
 
-To run the application locally or deploy it on Hugging Face Spaces, follow these steps:
+## Quickstart
 
-1. **Clone the Repository:**  
-   Clone this repository to your local machine.
+1. **Clone the Repository**  
    ```bash
    git clone https://huggingface.co/spaces/your-username/Malicious-URL-Detector
    cd Malicious-URL-Detector
+
+2. **Install Dependencies**  
+pip install -r requirements.txt
+
+3.**Run the App**  
+streamlit run app.py
+
+4.**Use It** 
+Paste an email’s content or a URL into the text box.
+
+Click Analyze to see the classification results.
+
+5.**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
+
+Questions/Issues: Please open an issue or reach out via Hugging Face.
+
+Enjoy safer browsing and emailing!