This project showcases key papers in the field of Large Language Models (LLMs) using a simple web app built with Streamlit. The web app allows users to search and filter papers by title, author, summary, and publication year.

View demo

• papers.csv: A CSV file containing the details of the foundational papers, including title, authors, summary, link, and year of publication.
• papers.py: The Streamlit application code to display and search through the papers.

1. Clone the repository:

   git clone https://github.com/amitabhadey/llm_papers.git
   cd foundational-papers-llm
   

2. Install the required dependencies:: Ensure you have Python installed. It's recommended to use a virtual environment.

   pip install streamlit pandas
   

3. Run the Streamlit app::

   streamlit run papers.py
   

4. View the app: The app will open in your default web browser. You can search and filter the papers by title, author, summary, and year.

You can use this code to create a similar web app for papers in your own domain topics by updating the papers.csv file.

1. Update the CSV file: Replace the content of papers.csv with your own data. Ensure the CSV file includes the following columns: Paper, Authors, Summary, Link, and Year.

2. Run the app: After updating the CSV file, run the Streamlit app again using the command:

   streamlit run papers.py