--- title: LitReviewAI emoji: 💻 colorFrom: green colorTo: pink sdk: streamlit sdk_version: 1.60.0 app_file: app.py pinned: false short_description: 👉 “Turn PDFs into Research Insights in Seconds." --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference --- ```markdown # 📚 LitReviewAI ## AI-Powered Research Literature Assistant

LitReviewAI is an intelligent research assistant designed to help researchers analyze, summarize, and organize scientific literature using Artificial Intelligence and Natural Language Processing. It automates the tedious parts of literature review by extracting insights from research papers, identifying research gaps, discovering topics, and enabling interactive conversations with scientific documents. --- # 🚀 Demo 🔗 Hugging Face Space: (Add your Space link here) --- # ✨ Features ## 📄 Automated Paper Analysis Upload multiple research papers in PDF format and automatically extract: - Paper title - Authors - Abstract - Research information --- ## 🧠 AI-Powered Summarization Using Large Language Models, LitReviewAI generates: - Concise paper summaries - Section-wise highlights - Research limitations - Potential research gaps --- ## 🔑 Keyword Extraction Extracts important scientific keywords using: - Sentence Transformers - KeyBERT - Semantic embeddings --- ## 📊 Topic Discovery Discover research themes across multiple papers using: - Latent Dirichlet Allocation (LDA) - NLP-based topic modeling --- ## 🌐 Research Collaboration Network Visualize: - Author relationships - Collaboration patterns - Research communities --- ## 💬 Chat With Research Papers Ask questions about uploaded papers and receive AI-generated answers based on extracted scientific content. --- ## 📚 Citation Management Export analyzed papers into: - BibTeX format - CSV reports - JSON summaries --- # 🏗️ System Architecture PDF Papers | | PyMuPDF Extraction | | Metadata + Abstract Extraction | | AI/NLP Pipeline | |------------------ | | Summarization Keyword Extraction (Groq LLM) (KeyBERT) | | Research Insights | | Interactive Streamlit Dashboard --- # 🛠️ Tech Stack ### Frontend - Streamlit ### AI / NLP - Groq LLM API - Sentence Transformers - KeyBERT - Gensim LDA ### Document Processing - PyMuPDF ### Visualization - Plotly - NetworkX - WordCloud ### Data Processing - Pandas - Numpy --- # 📂 Project Structure LitReviewAI/ │ ├── app.py ├── requirements.txt │ ├── src/ │ ├── embeddings.py │ ├── metadata_extractor.py │ ├── pdf_parser.py │ ├── ai_functions.py │ ├── topic_modeling.py │ ├── visualizations.py │ └── chat.py │ ├── components/ │ ├── upload.py │ ├── summaries.py │ ├── insights.py │ ├── assets/ │ └── logo.png │ └── data/sample_papers --- # ⚙️ Installation Clone repository: ```bash git clone https://github.com/Bano733-code/LitReviewAI.git Install dependencies: pip install -r requirements.txt Run: streamlit run app.py 🔐 Environment Variables Create Streamlit secrets: .streamlit/secrets.toml Add: GROQ_API_KEY="your_api_key" 📖 Research Applications LitReviewAI can support: Biomedical literature review Computational biology research AI-assisted scientific discovery Systematic review workflows Research hypothesis generation 🔬 Future Improvements Planned features: Vector database integration Semantic paper search RAG-based document retrieval Citation recommendation Automatic systematic review generation Multi-document knowledge graphs 👩‍💻 Author Bano Rani BS Bioinformatics Student Research Interests: AI for Bioinformatics Computational Biology Biomedical NLP Precision Medicine 📜 License MIT License --- This README will make LitReviewAI look like a **real AI research product**, not just a Streamlit assignment. It highlights the parts professors usually care about: - scientific motivation - AI methodology - architecture - reproducibility - future research potential - technical depth