📑 ResearchPaperAnalyzer

ResearchPaperAnalyzer is a Streamlit + Groq API powered application that helps users analyze academic research papers efficiently.

It allows you to upload one or more PDFs, generate structured summaries, evaluate topic relevance, extract key tags, and export everything into a downloadable PDF report — all through an intuitive web interface.

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🚀 Features

• 📂 Upload PDF
  Drag-and-drop one or multiple research papers.

• 📝 Summarization
  Generate Short, Medium, or Detailed summaries using structured prompts.

• 🎯 Relevance Scoring
  Enter a topic or domain and receive an alignment score (0–10) for each paper.

• 🔖 Tag Extraction
  Automatically extract important keywords and research themes.

• 📄 PDF Report Export
  Download a compiled report containing summaries, relevance scores, and tags.

• ⚡ Fast & Local Execution
  PDF processing runs locally; only AI inference uses the Groq API.

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🧠 How It Works

1. Upload one or more PDF research papers
2. Text is extracted and cached locally
3. Documents are chunked for long-context safety
4. Groq LLM is used with structured prompts to:
   • Generate summaries (Refine method)
   • Score relevance to a given topic
   • Extract key tags
5. Results are compiled into a final PDF report

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📁 Project Structure

ResearchPaperAnalyzer/
├─ .devcontainer/
│ └─ devcontainer.json
├─ config/
│ ├─ **pycache**/
│ │ ├─ **init**.cpython-313.pyc
│ │ ├─ export_utils.cpython-313.pyc
│ │ └─ setting.cpython-313.pyc
│ ├─ **init**.py
│ ├─ export_utils.py
│ └─ setting.py
├─ core/
│ ├─ **pycache**/
│ │ ├─ **init**.cpython-313.pyc
│ │ ├─ chunk_text.cpython-313.pyc
│ │ └─ extract_text.cpython-313.pyc
│ ├─ **init**.py
│ ├─ chunk_text.py
│ └─ extract_text.py
├─ pages/
│ ├─ 1_About.py
│ └─ 2_Contact_Me.py
├─ prompts/
│ ├─ **pycache**/
│ │ ├─ length_instruction.cpython-313.pyc
│ │ ├─ ratings.cpython-313.pyc
│ │ ├─ refine_method.cpython-313.pyc
│ │ └─ summarize.cpython-313.pyc
│ ├─ length_instruction.py
│ ├─ ratings.py
│ └─ refine_method.py
├─ ui/
│ ├─ **pycache**/
│ │ ├─ **init**.cpython-313.pyc
│ │ ├─ pdf_report.cpython-313.pyc
│ │ ├─ relevance.cpython-313.pyc
│ │ ├─ sidebar.cpython-313.pyc
│ │ └─ summarize.cpython-313.pyc
│ ├─ **init**.py
│ ├─ pdf_report.py
│ ├─ relevance.py
│ ├─ sidebar.py
│ └─ summarize.py
├─ .env
├─ .gitignore
├─ app.py
├─ README.md
└─ requirements.txt

🛠️ Tech Stack

• Python 3.10+
• Streamlit – Web UI
• Groq API – LLM inference
• PyMuPDF / pdfminer.six – PDF text extraction
• fpdf2 – PDF report generation

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📦 Installation

# Clone the repository
git clone https://github.com/<your-username>/ResearchPaperAnalyzer.git
cd ResearchPaperAnalyzer

# Create and activate virtual environment
python -m venv .venv

# Windows
.venv\Scripts\activate

# macOS / Linux
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

🔑 Environment Setup

Create a .env file for local development:

GROQ_API_KEY = "your_groq_api_key_here"

▶️ Usage

Run the app locally:

streamlit run app.py

Open in your browser:

http://localhost:8501

📌 Notes

1. Designed for researchers, students, and engineers
2. Uses Refine-based summarization for long documents
3. Optimized to reduce hallucinations and token usage
4. Built with scalability and clean architecture in mind

📊 Example Output

• Summary (short/medium/long)
• Tags: e.g., ["Neural Networks", "Graph Models", "Recommendation"]
• Relevance Score: 8.5 / 10
• Generated Report: Downloadable .pdf

🤝 Contributing

• Contributions are welcome!
• Open an issue for feature requests or bugs
• Submit a pull request for improvements
• Keep changes modular and documented