🧠 PaperMind

AI-Powered Research Intelligence Assistant

PaperMind is an AI-driven academic research assistant designed to automate the process of discovering, summarizing, and organizing scientific research papers. It leverages large language models and the arXiv API to significantly reduce the time spent on manual literature reviews while improving research productivity.

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🚀 Problem Statement

Academic researchers and students spend a substantial amount of time searching for relevant papers, reading abstracts, and organizing findings. This manual process is repetitive, time-consuming, and inefficient, especially when dealing with rapidly growing research domains.

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💡 Solution

PaperMind automates the academic literature review workflow by:

• Fetching relevant research papers from arXiv
• Generating concise, structured summaries using AI
• Organizing outputs into topic-based local folders
• Providing an intuitive browser-based interface for ease of use

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✨ Key Features

• 🔍 Automated Paper Retrieval from arXiv using RSS/XML parsing
• 🧠 AI-Powered Summarization optimized for scientific literature
• 📂 Structured Local Storage of summaries for offline access
• 🌐 Interactive Web Interface built with Gradio
• 🔐 Secure API Key Management using environment variables
• 🧩 Modular Codebase for scalability and maintainability

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🛠️ Tech Stack

• Programming Language: Python

• AI Model: OpenAI GPT-4

• APIs: arXiv API

• Frontend/UI: Gradio

• Libraries & Tools:

  • openai
  • requests
  • xml.etree.ElementTree
  • python-dotenv

• Version Control: Git & GitHub

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🏗️ System Architecture

User Input (Topic/Keyword)
        ↓
arXiv API (RSS/XML)
        ↓
Paper Metadata & Abstracts
        ↓
GPT-4 Summarization Engine
        ↓
Structured Text Summaries
        ↓
Local Topic-Based Folders + Gradio UI

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📂 Project Structure

PaperMind/
│
├── main.py        # Core agent logic (fetching, summarization, file handling)
├── app.py         # Gradio-based web interface
├── summaries/     # Generated research paper summaries
├── .env           # Environment variables (API keys)
├── .gitignore     # Excludes sensitive and unnecessary files
├── requirements.txt
└── README.md

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/ahmasmibhanu/PaperMind.git
cd PaperMind

2️⃣ Create Virtual Environment (Recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Configure Environment Variables

Create a .env file in the root directory:

OPENAI_API_KEY=your_openai_api_key_here

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▶️ Running the Application

Run the Gradio Web Interface

python app.py

Open your browser and navigate to:

http://localhost:7860

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📈 Impact & Results

• ⏱️ Reduced manual literature review time by up to 80%
• 📚 Improved organization and accessibility of research summaries
• 👥 Enabled non-technical users to interact with AI-based research tools
• 🔬 Demonstrated effective application of LLMs for scientific text summarization

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⚠️ Limitations

• Summaries are generated primarily from paper abstracts
• AI-generated summaries may occasionally lack context or nuance
• Currently limited to arXiv as the data source

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🔮 Future Enhancements

• Full-text PDF parsing and summarization
• Semantic search using embeddings and vector databases
• Integration with additional research databases (PubMed, Semantic Scholar)
• Citation tracking and relevance ranking
• Export support (Markdown, BibTeX, Notion, Obsidian)

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👨‍💻 Author

ahmasmibhanu ~B.Tech Computer Science & Engineering

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📜 License

This project is intended for academic and educational purposes.

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⭐ If you find this project useful, consider starring the repository!

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