This repository provides a Python interface for reading and summarizing PDF research papers using the OpenAI GPT-3.5-turbo model. It's designed for researchers and power users who want to quickly extract key information and ask targeted questions about academic documents. The tool aims to streamline the paper review process by automating summarization and Q&A.

How It Works

The system processes PDF papers by splitting them into sections, generating summaries for each part while maintaining context from previous sections within token limits. Users can pre-define questions to guide the AI's focus during summarization. After processing, users can query the paper using a question() interface, receiving answers based on the aggregated summaries. This approach allows for efficient information retrieval and a structured understanding of complex documents.

Quick Start & Requirements

• Install via pip: pip install gradio
• Run the GUI: python gui.py
• Requires an OpenAI API key.
• Official documentation and demo are not explicitly linked, but usage examples are provided in the README.

Highlighted Details

• Splits papers by section titles for improved handling of longer articles.
• Supports asking follow-up questions about the paper's content.
• Includes a Gradio-based GUI for interactive use.
• Code refactored for better maintainability.

Maintenance & Community

No specific contributors, sponsorships, or community links (like Discord/Slack) are mentioned in the README. The project appears to be a personal or small-team effort.

Licensing & Compatibility

The README does not specify a license. This means it defaults to all rights reserved, and commercial use or linking with closed-source projects is likely not permitted without explicit permission.

Limitations & Caveats

Users may exceed token limits when asking questions, potentially leading to unstable results. The README also notes that summary accuracies may need improvement, indicating the system is still under active development or refinement.