metadata

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

Clone the repository:

git clone <repository-url>
cd <repository-directory>

Install dependencies:
```
pip install -r requirements.txt
```
Install Git LFS for handling large files:
```
git lfs install
```

Usage

Run the Streamlit application:
```
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.