Update README.md

README.md

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 short_description: A web app for detecting malicious Email and URL
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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.
+
+## How It Works
+
+- **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.
+
+- **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.
+
+- **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.
+
+## Getting Started
+
+To run the application locally or deploy it on Hugging Face Spaces, follow these steps:
+
+1. **Clone the Repository:**  
+   Clone this repository to your local machine.
+   ```bash
+   git clone https://huggingface.co/spaces/your-username/Malicious-URL-Detector
+   cd Malicious-URL-Detector