Most Upvoted Paper Summarizer

This script fetches, downloads, and summarizes the most upvoted paper from Hugging Face daily papers. It uses SmoLAgents to create a pipeline that:

1. Fetches the top paper from Hugging Face
2. Gets its arXiv ID
3. Downloads the paper
4. Reads and summarizes its content

Features

• Automated paper discovery from Hugging Face's daily papers
• ArXiv integration for paper downloads
• PDF processing with first 3 pages analysis
• LLM-powered summarization using Qwen2.5-Coder-32B
• Modular tool-based architecture using SmoLAgents

Components

• get_hugging_face_top_daily_paper(): Scrapes and retrieves the most upvoted paper from HuggingFace
• get_paper_id_by_title(): Finds the corresponding arXiv ID for a paper title
• download_paper_by_id(): Downloads the paper PDF from arXiv
• read_pdf_file(): Processes the PDF and extracts text from the first three pages

Requirements

• SmoLAgents
• Hugging Face API token
• Dependencies:
  • arxiv
  • requests
  • beautifulsoup4
  • huggingface_hub
  • pypdf

Setup

1. Install the required packages:

pip install -r requirements.txt

2. Set up your Hugging Face API token:
   • Replace 'HF_API_TOKEN' in the code with your actual token
   • Or set it as an environment variable

Usage

from most_upvoted_paper import main

# Run the paper summarization pipeline
main()

Output

The script will:

1. Print the total number of pages in the downloaded paper
2. Process the first three pages
3. Generate a summary using the Qwen2.5-Coder model

Note

This is an example implementation using the SmoLAgents framework. The script demonstrates how to create a complex pipeline by combining multiple tools and LLM capabilities.