Updated readme

README.md

@@ -8,44 +8,70 @@ sdk_version: 1.41.1
 app_file: app.py
 pinned: false
 license: mit
-short_description: A demo showcasing multi-agent conversational AI.
+short_description: A demo showcasing multi-agent conversational AI with explainability.
 ---
 
-## MultiAgent XAI Demo
+# MultiAgent XAI Demo
 
 ## Overview
-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. The system is designed to generate technical solutions and complementary data-driven recommendations for user queries.
-
-## Features
-- Uses the `microsoft/Phi-3-mini-4k-instruct` model for natural language understanding.
-- Simulates dialogue between two agents (Engineer and Analyst).
-- Provides a summarized actionable plan at the end of the interaction.
-- Built with Streamlit for an interactive user interface.
-
-## Requirements
-- Python 3.8 or higher
-- Streamlit
-- Transformers library
-- PyTorch
-- CUDA-enabled GPU for optimal performance (optional but recommended)
-
-### Features
-- **Engineer Role**: Provides concise, technical solutions.
-- **Analyst Role**: Offers data-driven recommendations to complement the Engineer's response.
-- **Natural Dialogue**: Facilitates a three-turn conversation between the roles.
-- **Actionable Summary**: Generates a final plan summarizing key insights.
-
-### How It Works
-1. The user enters a query.
-2. The Engineer and Analyst respond alternately, building on each other's inputs.
-3. A final summary is generated, integrating technical and analytical perspectives.
-
-### Technology Stack
-- **Streamlit**: Interactive web interface.
-- **Hugging Face Transformers**: Using `AutoTokenizer`and  `AutoModelForCausalLM` with the `microsoft/phi-3-mini-4k-instruct` model for text generation.
-- **Torch**
+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>
@@ -63,6 +89,7 @@ This project demonstrates a Multi-Agent system using the `microsoft/Phi-3-mini-4
    ```
 
 ## Usage
+
 1. Run the Streamlit application:
    ```bash
    streamlit run app.py
@@ -74,6 +101,7 @@ The `microsoft/Phi-3-mini-4k-instruct` model is a lightweight instruction-tuned
 - 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.
 
@@ -81,4 +109,7 @@ The `microsoft/Phi-3-mini-4k-instruct` model is a lightweight instruction-tuned
 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.
+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.