📄 ResearchXpert

Illuminate Research Papers with Interactive Conversational AI

ResearchXpert is a web application that allows users to upload research papers (PDFs) and interact with them through AI-powered summarization and Q&A. The app extracts text from uploaded PDFs, generates embeddings, and uses a large language model (LLM) to provide detailed summaries and answer user queries.

🚀 Features

📑 PDF Summarization

• Upload research papers in PDF format.

• Generate concise, high-quality summaries of the paper.

• Supports long documents via chunking and embeddings.

💬 Chat n Seek (Q&A)

• Ask questions about the uploaded paper.

• AI provides contextual answers based on the content.

• Supports interactive exploration of research papers.

🧰 Additional Features

• View uploaded PDF directly in the browser.

• Reset/Refresh button to clear all uploads and chat history.

• Clean, modern UI with collapsible summary boxes and chat bubbles.

🎨 Frontend

• HTML5 for structure

• CSS3 with modern gradients, shadows, and hover effects

• Vanilla JavaScript (ES6) for:

  • File upload handling

  • Summarization requests

  • Q&A interaction

  • Reset/Refresh functionality

🖥️ Backend

• Python 3.13

• Flask for routing and serving frontend

• PyMuPDF (fitz) for PDF text extraction

• FAISS for storing and searching text embeddings

• Groq LLM API for summarization and Q&A

⚙️ Project Structure

smart-paper-explainer/
│
├── app.py                  # Flask backend with routes
├── embeddings_utils.py      # Text embedding and FAISS index functions
├── qa_utils.py              # Groq API integration for summarization & Q/A
├── text_utils.py            # Text cleaning and chunking
├── static/
│   ├── style.css            # Frontend CSS
│   └── script.js            # Frontend JS
├── templates/
│   └── index.html           # Frontend HTML
├── uploads/                 # Temporary storage for uploaded PDFs
├── requirements.txt         # Python dependencies
└── README.md                # Project documentation

`

💻 Setup Instructions

1. Clone the repository

   git clone https://github.com/Shivanggaryaa/smart-paper-explainer.git cd smart-paper-explainer `

2. Create a virtual environment

   python -m venv venv

   source venv/bin/activate # Linux/Mac
   venv\Scripts\activate # Windows `

3. Install dependencies

   pip install -r requirements.txt `

4. Configure environment variables

• Create a .env file in the root directory: GROQ_API_KEY=your_groq_api_key `

1. Run the Flask app

   python app.py `

🔧 Usage

1. Upload a research paper (PDF) via the Upload section.

2. Click Summarize to generate a concise summary.

3. Ask questions in the Chat n Seek section.

4. Use the Reset/Refresh button to clear all uploads and start fresh.

🛠️ Tech Stack

• Frontend: HTML5, CSS3, JavaScript (ES6)

• Backend: Python, Flask

• PDF Processing: PyMuPDF

• Embeddings & Search: FAISS

• LLM API: Groq API (LLaMA 3.1)

📌 Future Improvements

• Add user authentication for saving sessions.

• Persistent storage for uploaded PDFs and summaries.

• Enhanced UI/UX for mobile responsiveness.

• Better handling for very large PDFs.

• Multiple LLM backends for summarization and Q&A.

🔖 Live Demo : https://huggingface.co/spaces/ShivangArya/rshxpt