| --- |
| title: PDF Analysis & Orchestrator |
| emoji: 📄 |
| colorFrom: blue |
| colorTo: purple |
| sdk: gradio |
| sdk_version: 4.44.0 |
| app_file: app.py |
| pinned: false |
| license: mit |
| short_description: AI-powered PDF analysis with advanced features |
| --- |
| |
| # 📄 PDF Analysis & Orchestrator |
|
|
| A powerful, intelligent PDF analysis tool that provides comprehensive document processing through AI-powered agents. This application offers advanced features including document chunking, caching, streaming responses, batch processing, and custom prompt management. |
|
|
| ## 🚀 Features |
|
|
| ### Core Analysis |
| - **AI-Powered Analysis**: GPT-4 powered document analysis with context-aware responses |
| - **Audience Adaptation**: Automatically adapts explanations for different audiences |
| - **Document Segmentation**: Identifies and segments documents by themes and topics |
| - **Multi-Agent Orchestration**: Specialized AI agents for different analysis aspects |
|
|
| ### Performance Optimizations |
| - **Document Chunking**: Smart processing of large documents (>15k chars) with sentence boundary detection |
| - **Caching System**: PDF text extraction caching for improved performance |
| - **Streaming Responses**: Real-time progress updates and status indicators |
| - **Configurable Parameters**: Adjustable chunk sizes and processing options |
|
|
| ### Enhanced Features |
| - **Batch Processing**: Handle multiple PDFs simultaneously with comprehensive reporting |
| - **Result Export**: Export analysis results in TXT, JSON, and PDF formats |
| - **Custom Prompts**: Save, manage, and reuse custom analysis prompts |
| - **Progress Indicators**: Real-time feedback during long-running analyses |
| - **Session Management**: Per-user session isolation with persistent storage |
|
|
| ## 🎯 Use Cases |
|
|
| - **Document Summarization**: Create concise summaries of complex documents |
| - **Technical Explanation**: Explain technical content for general audiences |
| - **Executive Summaries**: Generate high-level overviews for decision makers |
| - **Content Analysis**: Extract key findings and insights from documents |
| - **Batch Processing**: Analyze multiple documents with consistent instructions |
| - **Research Assistance**: Process and analyze research papers and reports |
|
|
| ## 🛠️ Setup |
|
|
| ### Prerequisites |
| - Python 3.10+ |
| - OpenAI API key |
|
|
| ### Installation |
|
|
| 1. **Clone the repository:** |
| ```bash |
| git clone https://huggingface.co/spaces/your-username/pdf-analysis-orchestrator |
| cd pdf-analysis-orchestrator |
| ``` |
|
|
| 2. **Install dependencies:** |
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| 3. **Set up environment variables:** |
| ```bash |
| export OPENAI_API_KEY="sk-your-api-key-here" |
| ``` |
|
|
| 4. **Run the application:** |
| ```bash |
| python app.py |
| ``` |
|
|
| ## 📖 Usage |
|
|
| ### Single Document Analysis |
| 1. Upload a PDF document |
| 2. Enter your analysis instructions |
| 3. Choose analysis options (streaming, chunk size) |
| 4. Click "Analyze & Orchestrate" |
| 5. View results and export if needed |
|
|
| ### Batch Processing |
| 1. Upload multiple PDF files |
| 2. Enter batch analysis instructions |
| 3. Click "Process Batch" |
| 4. Review comprehensive batch results |
|
|
| ### Custom Prompts |
| 1. Go to "Manage Prompts" tab |
| 2. Create custom prompt templates |
| 3. Organize by categories |
| 4. Reuse prompts across analyses |
|
|
| ## 🏗️ Architecture |
|
|
| ### Core Components |
| - **AnalysisAgent**: Primary analysis engine using GPT-4 |
| - **CollaborationAgent**: Provides reviewer-style feedback |
| - **ConversationAgent**: Handles user interaction |
| - **MasterOrchestrator**: Coordinates agent interactions |
|
|
| ### Key Files |
| - `app.py`: Main application with Gradio interface |
| - `agents.py`: AI agent implementations with streaming support |
| - `config.py`: Centralized configuration management |
| - `utils/`: Utility functions for PDF processing, caching, and export |
|
|
| ## 🔧 Configuration |
|
|
| ### Environment Variables |
| - `OPENAI_API_KEY`: Required OpenAI API key |
| - `OPENAI_MODEL`: Model to use (default: gpt-4) |
| - `CHUNK_SIZE`: Document chunk size (default: 15000) |
| - `CACHE_ENABLED`: Enable caching (default: true) |
| - `ANALYSIS_MAX_UPLOAD_MB`: Max upload size in MB (default: 50) |
|
|
| ### Model Configuration |
| - **Temperature**: 0.2 (consistent, focused responses) |
| - **Max tokens**: 1000 (concise but comprehensive) |
| - **System prompts**: Designed for high-quality output |
|
|
| ## 📊 Performance |
|
|
| - **Response Time**: Typically 2-5 seconds for analysis |
| - **File Size Limit**: 50MB (configurable) |
| - **Concurrent Users**: Supports multiple simultaneous sessions |
| - **Memory Usage**: Optimized for efficient processing |
| - **Caching**: Reduces processing time for repeated documents |
|
|
| ## 🔒 Security |
|
|
| - File size validation |
| - Session isolation |
| - Secure file handling |
| - No persistent storage of sensitive data |
| - Environment-based configuration |
|
|
| ## 🤝 Contributing |
|
|
| 1. Fork the repository |
| 2. Create a feature branch |
| 3. Make your changes |
| 4. Add tests if applicable |
| 5. Submit a pull request |
|
|
| ## 📝 License |
|
|
| This project is licensed under the MIT License - see the LICENSE file for details. |
|
|
| ## 🙏 Acknowledgments |
|
|
| - Built on the successful Analysis & Orchestrate feature from Sharmaji ka PDF Blaster V1 |
| - Powered by OpenAI's GPT-4 model |
| - UI framework: Gradio |
| - PDF processing: pdfplumber |
|
|
| ## 📞 Support |
|
|
| For issues and questions: |
| 1. Check the documentation |
| 2. Review existing issues |
| 3. Create a new issue with detailed information |
|
|
| --- |
|
|
| **Note**: This project focuses exclusively on the Analysis & Orchestrate functionality, providing the same high-quality results in a streamlined, focused package with enhanced performance and user experience. |
