# Web Page LLM Classifier and Summarizer

## Overview
The Web Page LLM Classifier and Summarizer is a powerful tool designed to process and analyze text data from web pages using advanced natural language processing (NLP) techniques. This system consists of two main components: a classifier that categorizes text into predefined categories, and a summarizer that generates concise summaries of the content.

## Features
1. **Category Classification**:
   - Automatically identifies and categorizes text based on its structure, semantics, and context.
   - Supports categories such as news articles, academic papers, product reviews, and social media posts.

2. **Text Summarization**:
   - Generates brief summaries that capture the essential information from lengthy texts.
   - Utilizes techniques like topic modeling, sentence extraction, and sentiment analysis to create concise overviews.

3. **User Interface**:
   - A user-friendly web interface allows users to input their own text or URL directly into the system.
   - Real-time feedback on category classification and summary generation within seconds.

## Installation
The Web Page LLM Classifier and Summarizer is a web-based application and does not require installation. Simply access it through any web browser by navigating to [insert website URL].

## Usage
1. **Input Text or URL**:
   - Go to the web page.
   - Enter your text in the provided input field or paste a URL of the web page you want to analyze.

2. **Process Content**:
   - Click on the "Classify and Summarize" button.
   - The system will automatically classify the content and generate a summary.

3. **View Results**:
   - The category classification and summary will be displayed on the page.

## Example
Here's how you can use the tool:

1. Open [insert website URL].
2. Enter your text or paste a URL in the input field.
3. Click "Classify and Summarize".
4. Review the category classification and generated summary.

## Contributing
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please submit them through our [GitHub repository](insert GitHub link).

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contact
For any inquiries, reach out to us at [contact email address].

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