Back to Portfolio

ForeCite Citation Parser

• Class: CSCI 496
• Grade: A
• Language(s): Python, HTML
• Source Code Repository: ForeCite
  (Please email me to request access.)

Project description

The Medical University of South Carolina (MUSC) conducts extensive research that relies heavily on accurate citations and literature review. Currently, researchers and students must manually extract and organize citations from research papers, a process that is both time-consuming, resource-intensive, and error-prone. Given the volume of publications produced and reviewed by MUSC, this inefficiency creates delays in research workflows and increases the risk of inaccuracies in research at MUSC. To address this issue, this project applies the Systems Development Life Cycle (SDLC) to design and implement an automated citation extraction system. This solution will leverage a local Large Language Model (LLM) to identify citations, capture their context, and generate summaries of their relevance, with results stored in a structured database. By streamlining citation management, MUSC researchers will benefit from reduced administrative workload, greater accuracy, and improved efficiency.

How to run the program

Included in Repository (See README and Requirements.txt)

cd ./frontend
python app.py

UI Design

Usage Flow Included In Documentation

Fig 1. The launch screen

Fig 2. Example output after file is processed.

Fig 3. Example of searching for documents

Fig 3. Example of document deletion

Back to Portfolio