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| title: AI Literature Review System | |
| emoji: 📚 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 5.9.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # 📚 AI Literature Review System | |
| An AI-powered literature review application that uses multiple specialized reviewers to analyze research papers. Built with Gradio and powered by OpenAI-compatible APIs. | |
| ## 🌟 Features | |
| - **Multi-Agent Review System**: Three specialized AI reviewers with different perspectives: | |
| - **Experimentalist**: Focuses on methodology and experimental rigor | |
| - **Impactist**: Evaluates potential impact and field significance | |
| - **Novelty Seeker**: Assesses originality and innovation | |
| - **Comprehensive Analysis**: Reviews papers across multiple dimensions: | |
| - Originality & Novelty | |
| - Technical Quality & Soundness | |
| - Clarity & Presentation | |
| - Significance & Impact | |
| - Contribution to the field | |
| - **PDF Upload**: Simply upload your research paper and get instant feedback | |
| - **MarkItDown Integration**: Advanced PDF text extraction for better accuracy | |
| - **Semantic Scholar Integration**: Find and compare related papers | |
| - **Detailed Feedback**: Receive strengths, weaknesses, questions, and actionable suggestions | |
| - **Academic-Standard Scoring**: Based on top-tier conference review standards (NeurIPS-style) | |
| ## 🚀 Quick Start | |
| ### Local Installation | |
| 1. Clone the repository: | |
| ```bash | |
| git clone https://huggingface.co/spaces/syaikhipin/PaperReview | |
| cd PaperReview | |
| ``` | |
| 2. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Set up environment variables: | |
| ```bash | |
| cp .env.example .env | |
| # Edit .env with your API credentials | |
| ``` | |
| 4. Run the application: | |
| ```bash | |
| python app.py | |
| ``` | |
| ### Hugging Face Spaces | |
| Simply visit the space and configure your API settings in the UI or set secrets in Space settings: | |
| - `OPENAI_API_KEY`: Your API key | |
| - `OPENAI_BASE_URL`: API endpoint (optional) | |
| - `MODEL_NAME`: Model identifier (optional) | |
| ## 📖 How to Use | |
| 1. **Configure API**: Enter your API credentials in the UI or use environment variables | |
| 2. **Upload Paper**: Upload your research paper in PDF format | |
| 3. **Enable Semantic Scholar** (optional): Search for related papers | |
| 4. **Review**: Click "Review Paper" and wait for sequential analysis (3-6 minutes) | |
| 5. **Analyze Results**: Review detailed feedback from all three reviewers | |
| ## 📊 Understanding the Scores | |
| The system provides scores on a 1-10 scale: | |
| - **9-10**: Award Quality / Strong Accept | |
| - **7-8**: Accept | |
| - **5-6**: Borderline | |
| - **3-4**: Borderline Reject | |
| - **1-2**: Reject | |
| Each reviewer evaluates: | |
| - **Soundness** (1-4): Technical quality | |
| - **Presentation** (1-4): Writing quality | |
| - **Contribution** (1-4): Overall contribution | |
| - **Originality** (1-4): Novelty of ideas | |
| - **Quality** (1-4): Research quality | |
| - **Clarity** (1-4): Clarity of presentation | |
| - **Significance** (1-4): Importance of results | |
| - **Confidence** (1-5): Reviewer confidence level | |
| - **Overall** (1-10): Overall assessment | |
| ## 🏗️ Project Structure | |
| ``` | |
| aireviewer/ | |
| ├── app.py # Main Gradio application | |
| ├── agents.py # Multi-agent review system | |
| ├── requirements.txt # Python dependencies | |
| ├── README.md # This file | |
| ├── .env.example # Environment variables template | |
| └── .gitignore # Git ignore rules | |
| ``` | |
| ## 🛠️ Technical Details | |
| ### Multi-Agent Architecture | |
| The system implements three specialized reviewer agents, each with a distinct persona: | |
| 1. **Experimentalist Reviewer** | |
| - Emphasizes experimental design and methodology | |
| - Looks for rigorous evaluation and clear insights | |
| - Questions reproducibility and statistical significance | |
| 2. **Impact-Focused Reviewer** | |
| - Evaluates potential field impact | |
| - Assesses practical applications | |
| - Considers broader implications | |
| 3. **Novelty-Focused Reviewer** | |
| - Seeks original contributions | |
| - Evaluates creative approaches | |
| - Identifies incremental vs. breakthrough work | |
| ### Review Process | |
| 1. **PDF Text Extraction**: Uses MarkItDown for high-quality text extraction | |
| 2. **Sequential Multi-Agent Review**: Each agent independently evaluates the paper one at a time | |
| - Rate limited to 1 request per second to avoid API concurrency issues | |
| - Sequential processing ensures consistent quality and respects API limits | |
| 3. **Scoring Aggregation**: Weighted scoring across multiple criteria | |
| 4. **Feedback Generation**: Structured feedback with JSON parsing | |
| 5. **Related Papers**: Semantic Scholar API integration with rate limiting (1 req/sec) | |
| ### API Compatibility | |
| The system uses OpenAI-compatible APIs, supporting: | |
| - OpenAI (GPT-4, GPT-3.5, etc.) | |
| - Azure OpenAI | |
| - Custom endpoints (LocalAI, vLLM, LiteLLM, etc.) | |
| - Any OpenAI-compatible inference server | |
| ## 🔒 Privacy & Security | |
| - API keys can be provided via UI or environment variables | |
| - Uploaded PDFs are processed in memory and not permanently stored | |
| - All processing happens on your infrastructure | |
| - Semantic Scholar searches are anonymous | |
| ## 🌐 Deployment | |
| ### Hugging Face Spaces | |
| This app is deployed on Hugging Face Spaces. To deploy your own: | |
| 1. Create a new Space on Hugging Face | |
| 2. Select "Gradio" as SDK | |
| 3. Upload all files from this repository | |
| 4. Configure secrets in Space settings: | |
| - `OPENAI_API_KEY` (required) | |
| - `OPENAI_BASE_URL` (optional, defaults to OpenAI) | |
| - `MODEL_NAME` (optional, defaults to gpt-3.5-turbo) | |
| - `SEMANTIC_SCHOLAR_API_KEY` (optional but recommended for better rate limits) | |
| ### Docker | |
| ```dockerfile | |
| FROM python:3.10-slim | |
| WORKDIR /app | |
| COPY requirements.txt . | |
| RUN pip install -r requirements.txt | |
| COPY . . | |
| CMD ["python", "app.py"] | |
| ``` | |
| ## 📝 Environment Variables | |
| ```bash | |
| # Required | |
| OPENAI_API_KEY=your-api-key-here | |
| # Optional | |
| OPENAI_BASE_URL=https://api.openai.com/v1 | |
| MODEL_NAME=gpt-4 | |
| SEMANTIC_SCHOLAR_API_KEY=your-semantic-scholar-key | |
| ``` | |
| **Note**: All API keys should be stored as **secrets** in Hugging Face Spaces settings or in a local `.env` file (never commit .env to git). | |
| ## ⚠️ Notes | |
| - Reviews are generated **sequentially** (one at a time) with 1-second delays between API calls | |
| - **Rate Limiting**: Both LLM and Semantic Scholar APIs are rate-limited to 1 request/second to avoid concurrency issues | |
| - Processing time: 3-6 minutes depending on paper length and API response time | |
| - Ensure your PDF contains extractable text (not scanned images) | |
| - Semantic Scholar API: Using an API key provides higher rate limits (recommended) | |
| - The system provides automated feedback; human review is still recommended | |
| ## 🤝 Contributing | |
| Contributions are welcome! Feel free to: | |
| - Report bugs | |
| - Suggest new features | |
| - Submit pull requests | |
| - Improve documentation | |
| ## 📄 License | |
| This project is open source and available under the MIT License. | |
| ## 🙏 Acknowledgments | |
| - Based on multi-agent research frameworks | |
| - Inspired by academic peer review processes | |
| - Uses MarkItDown for PDF processing | |
| - Integrates with Semantic Scholar API | |
| - Built with Gradio for easy deployment | |
| ## 📧 Contact | |
| For questions or feedback, please open an issue on the repository. | |
| --- | |
| **Live Demo**: https://huggingface.co/spaces/syaikhipin/PaperReview | |