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| title: Deep Dive Analysis with Sustainable AI | |
| emoji: 🌿 | |
| colorFrom: green | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.19.0 | |
| app_file: app/main.py | |
| pinned: false | |
| license: mit | |
| tags: | |
| - sustainability | |
| - multi-agent | |
| - nlp | |
| - computer-vision | |
| - langchain | |
| Deep Dive Analysis with Sustainable AI | |
| A multi-agent AI system for analyzing text and image content on a specific topic, with a focus on sustainability and energy efficiency. | |
| Overview | |
| This application allows users to upload text files and images related to a topic, and receive a comprehensive analysis and report. The system uses a combination of AI models for text analysis, image processing, and report generation, all while optimizing for energy efficiency and sustainability. | |
| Key features: | |
| Text analysis with semantic understanding | |
| Image captioning and relevance assessment | |
| Comprehensive report generation with confidence levels | |
| Sustainability metrics tracking | |
| Energy-efficient model selection | |
| Architecture | |
| The system is built with a multi-agent architecture: | |
| Text Analysis Agent: Processes text files to determine relevance and extract key information | |
| Image Processing Agent: Captions images and determines their relevance to the topic | |
| Report Generation Agent: Creates comprehensive reports based on the analyses | |
| Metrics Agent: Tracks sustainability metrics and resource usage | |
| Coordinator Agent: Orchestrates the workflow between agents | |
| These agents are supported by: | |
| Model managers for text, image, and summarization | |
| Utilities for token management, caching, and metrics calculation | |
| Communication and synchronization components | |
| Installation | |
| Clone the repository: | |
| git clone https://github.com/yourusername/deep-dive-analysis.git | |
| cd deep-dive-analysis | |
| Copy | |
| Create a virtual environment: | |
| python -m venv venv | |
| source venv/bin/activate # On Windows: venv\Scripts\activate | |
| Copy | |
| Install dependencies: | |
| pip install -r requirements.txt | |
| Copy | |
| Usage | |
| Running the Application | |
| python app/main.py | |
| Copy | |
| This will start the Gradio web interface, accessible at http://localhost:7860. | |
| Command Line Options | |
| python app/main.py --config path/to/config.yaml --log-level INFO --port 7860 --share | |
| Copy | |
| --config: Path to configuration file (default: config/config.yaml) | |
| --log-level: Logging level (default: INFO) | |
| --port: Port for the web interface (default: 7860) | |
| --share: Create a shareable link | |
| Using the Web Interface | |
| Enter a topic for deep dive analysis | |
| Upload text files related to the topic | |
| Upload images related to the topic | |
| Click "Start Analysis" | |
| View the results in the different tabs: | |
| Executive Summary | |
| Detailed Report | |
| Text Analysis | |
| Image Analysis | |
| Raw Data | |
| Sustainability Features | |
| The application includes several features to optimize energy usage: | |
| Token Optimization: Minimizes token usage for LLM operations | |
| Adaptive Model Selection: Uses smaller models when appropriate | |
| Caching: Avoids redundant computation | |
| Smart Routing: Directs tasks to the most efficient components | |
| Sustainability Metrics: Tracks energy usage and carbon footprint | |
| Configuration | |
| The application is configured through config/config.yaml. Key configuration sections include: | |
| app: General application settings | |
| token_manager: Token budget and energy coefficients | |
| cache_manager: Cache size and TTL settings | |
| metrics_calculator: Carbon intensity and PUE values | |
| models: Model selection for different tasks | |
| agents: Agent-specific parameters | |
| Contributing | |
| Contributions are welcome! Please feel free to submit a Pull Request. | |
| Fork the repository | |
| Create your feature branch (git checkout -b feature/amazing-feature) | |
| Commit your changes (git commit -m 'Add some amazing feature') | |
| Push to the branch (git push origin feature/amazing-feature) | |
| Open a Pull Request | |
| License | |
| This project is licensed under the MIT License - see the LICENSE file for details. | |
| Acknowledgments | |
| This project uses models from Hugging Face | |
| Built with LangChain, PyTorch, and Gradio | |
| Inspired by research on energy-efficient AI systems |