Research Paper Context Builder

A small web app that helps you quickly understand research papers by turning a PDF into a concise, structured context summary.

The app extracts text from a PDF and asks an LLM to summarise it into clear sections like Key Findings, Evidence & Methodology, Limitations & Improvements, Future Work, and Practical Implications.

Features

• PDF upload: Drop in any research paper in PDF format.
• Automatic text extraction: Uses PyPDF2 to read the text from each page.
• Structured summary: Short bullet-point sections designed to be skimmable.
• User‑friendly UI: Built with Streamlit; runs locally in your browser.

1. Prerequisites

• Python 3.9+ installed
• An OpenAI API key (or compatible API) with access to the specified model

2. Installation

From the project folder (Context_builder):

python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt

3. Environment variables

Create a .env file in the project root with:

OPENAI_API_KEY=your_api_key_here
# Optional – override the default model:
OPENAI_MODEL=gpt-4.1-mini

You can also set these as normal environment variables instead of using a .env file.

4. Run the app

From the project root:

streamlit run app.py

Then open the URL shown in the terminal (usually http://localhost:8501) in your browser.

5. Usage

1. Upload a research paper PDF from the left sidebar.
2. (Optional) Toggle Show extracted text preview to inspect what was read from the PDF.
3. Click Generate Context Summary.
4. Read the structured summary on the right side of the page.

Each segment is intentionally short and concise, making it easy to build context quickly and compare multiple papers.