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| 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 | |
| <p align="center"> | |
| <img src="assets/logo.png" width="180"> | |
| </p> | |
| 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 |