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| title: MultiAgent XAI Demo | |
| emoji: 🤖 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: streamlit | |
| sdk_version: 1.41.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: A multi-agent conversational AI with explainability. | |
| # MultiAgent XAI Demo | |
| ## Overview | |
| The **Multi-Agent XAI Demo** is an advanced Streamlit-based web application designed to provide AI-powered technical solutions with built-in explainability. By integrating **Explainable AI (XAI)**, the system ensures transparency and interpretability, empowering users with actionable insights and a clear understanding of AI-generated recommendations. | |
| This project demonstrates a Multi-Agent system using the `microsoft/Phi-3-mini-4k-instruct` model to simulate collaboration between an **Engineer** and an **Analyst**, generating technical solutions and complementary data-driven recommendations for user queries. | |
| ## Key Features | |
| ### User-Friendly Query Submission | |
| - Intuitive interface for seamless query input | |
| - Efficient processing for rapid AI-generated responses | |
| ### AI-Powered Insights | |
| - **Engineer Agent:** Delivers precise technical solutions for complex challenges | |
| - **Analyst Agent:** Provides complementary data-driven insights to enhance analysis | |
| ### Explainable AI (XAI) | |
| - Every response includes a detailed explanation, offering clarity into the AI's reasoning and decision-making process | |
| ### Comprehensive Summarization | |
| - The system compiles responses into an actionable plan, ensuring well-structured insights for decision-making | |
| ## Applications | |
| ### Industry Applications | |
| - Predictive maintenance for manufacturing and industrial processes | |
| - Process automation to optimize workflows | |
| - Resource allocation and operational efficiency improvements | |
| ### Business Solutions | |
| - Providing strategic recommendations for decision-makers | |
| - Enhancing data-driven decision processes with AI-powered insights | |
| ### Educational Use | |
| - Demonstrating AI and XAI capabilities in practical applications | |
| - Supporting curriculum development in AI and machine learning | |
| ### Research and Development | |
| - Advancing multi-agent explainable AI systems | |
| - Exploring new methodologies for AI transparency and trustworthiness | |
| ## Technical Breakdown | |
| ### Modern UI Design | |
| - Structured layout displaying user queries, responses, and explanations | |
| - Clearly defined sections for Engineer Response, Analyst Response, and XAI Explanations | |
| ### Cutting-Edge Architecture | |
| - **Built with Streamlit:** Ensuring quick deployment and interactive experiences | |
| - **NLP-Powered by Hugging Face Transformers:** Delivering state-of-the-art language understanding | |
| - **Optimized AI Model:** Utilizing `microsoft/Phi-3-mini-4k-instruct` for highly accurate and context-aware responses | |
| - **Efficient State Management:** Using `st.session_state` to track user interactions seamlessly | |
| - **Dynamic Response Optimization:** Customizable parameters for fine-tuned performance | |
| ### Performance Enhancements | |
| - Optimized for both CPU and GPU to maximize efficiency | |
| - Adaptive token length management to maintain response quality and resource efficiency | |
| ## Installation | |
| 1. Clone the repository: | |
| ```bash | |
| git clone <repository-url> | |
| cd <repository-directory> | |
| ``` | |
| 2. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Install Git LFS for handling large files: | |
| ```bash | |
| git lfs install | |
| ``` | |
| ## Usage | |
| 1. Run the Streamlit application: | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| ## Model Details | |
| The `microsoft/Phi-3-mini-4k-instruct` model is a lightweight instruction-tuned language model optimized for efficient and concise responses. It is used here to: | |
| - Generate technical solutions from the Engineer. | |
| - Provide data-driven insights from the Analyst. | |
| ## Troubleshooting | |
| - **Model Loading Issues:** Ensure all dependencies are installed and that your environment supports the `microsoft/Phi-3-mini-4k-instruct` model. | |
| - **Performance Issues:** Use a CUDA-enabled GPU for better performance. If unavailable, ensure sufficient CPU resources. | |
| ## Contribution | |
| Contributions are welcome! Feel free to open issues or submit pull requests to improve the project. | |
| ## License | |
| This project is licensed under the MIT License. See the `LICENSE` file for details. | |
| ## Why It Matters | |
| The Multi-Agent System with XAI Demo showcases the transformative power of AI in solving complex problems while maintaining transparency and user trust. By bridging technical precision with explainability, this system sets a new standard for intelligent automation across industries. | |