Update README.md

README.md

@@ -10,5 +10,38 @@ pinned: true
 tags: [agent-demo-track, python]
 short_description: Explainable & transparent AI credit card fraud detection
 ---
-# AI-Powered Credit Card Fraud Detection
-A gradio web app that provide explainable fruad assessments for credit card transactions. This is achieved by a combination of a tabular fraud classifier and a reasoning LLM.
+# AI-Powered Fraud Detection Agent
+
+`agent-demo-track`
+**Track:** 3. Agentic Demo Showcase  
+**Built For:** [Hugging Face Hackathon - Multimodal Challenge Playground (MCP)](https://huggingface.co/multimodal-challenge)  
+**Team:** Osama Elmahdy, Muhammed Hisham
+
+---
+
+## Overview
+
+This Gradio-based AI Agent helps **merchants, analysts, and fraud teams** assess transaction legitimacy by combining a **fraud detection model** with **reasoning capabilities** powered by Hugging Face’s [`Nemotron-Research-Reasoning-Qwen-1.5B-GGUF`](https://huggingface.co/Nemotron-Research/Reasoning-Qwen-1_5B-GGUF) model.
+
+Users input transaction details, and the system:
+1. **Classifies the transaction** as fraudulent or not.
+2. Uses LLM reasoning to **explain the decision** and provide **recommendations** for next steps (e.g., flag, approve, monitor).
+
+---
+
+## AI Stack
+
+### Core Idea
+
+> First, check if a transaction is likely fraudulent using a traditional ML model trained on real-world features. Then, invoke an open LLM agent (via GGUF + llama-cpp) to explain and suggest human-friendly actions.
+
+### Components Used
+
+- **Fraud Detection:**  
+  - Custom-trained classifier (scikit-learn + LIME explanations)
+- **Reasoning & Recommendations:**  
+  - Hugging Face’s `Nemotron-Research-Reasoning-Qwen-1.5B-GGUF` running locally via `llama-cpp-python`
+- **UI:**  
+  - Gradio 
+
+---