web-page-cls 0.0.8

Web page LLM classifier and summarizer

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 file for details.

Contact

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

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