feat : edit readme

README.md

@@ -14,107 +14,3 @@ pinned: false
 short_description: Federated Learning Credit Scoring Demo with Privacy-Preserving Model Training
 license: mit
 ---
-
-# Federated Learning for Privacy-Preserving Financial Data Generation with RAG Integration
-
-This project implements a federated learning framework combined with a Retrieval-Augmented Generation (RAG) system to generate privacy-preserving synthetic financial data.
-
-## Features
-
-- Federated Learning using TensorFlow
-- Privacy-preserving data generation using VAE/GAN
-- RAG integration for enhanced data quality
-- Secure Multi-Party Computation (SMPC)
-- Differential Privacy implementation
-- Kubernetes-based deployment
-- Comprehensive monitoring and logging
-- **NEW: Interactive Web Demo** - Try it out without setup!
-
-## Quick Demo (No Installation Required)
-
-🚀 **Live Demo**: [Hugging Face Spaces](https://huggingface.co/spaces/ArchCoder/federated-credit-scoring)
-
-The web demo allows you to:
-- Enter customer features and get credit score predictions
-- See how federated learning works
-- Understand privacy-preserving ML concepts
-
-## Installation
-
-```bash
-# Create virtual environment
-python3 -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-
-# Install dependencies
-pip install -r requirements.txt
-```
-
-## Federated Credit Scoring Demo (with Web App)
-
-This project includes a demo where multiple banks (clients) collaboratively train a credit scoring model using federated learning. A Streamlit web app allows you to enter customer features and get a credit score prediction from the federated model.
-
-### Quick Start
-
-1. **Install dependencies**
-
-```bash
-pip install -r requirements.txt
-```
-
-2. **Start the Federated Server**
-
-```bash
-python -m src.main --mode server --config config/server_config.yaml
-```
-
-3. **Start at least two Clients (in separate terminals)**
-
-```bash
-python -m src.main --mode client --config config/client_config.yaml
-```
-
-4. **Run the Web App**
-
-```bash
-streamlit run webapp/streamlit_app.py
-```
-
-5. **Use the Web App**
-- Enter 32 features (dummy values are fine for demo)
-- Click "Predict Credit Score" to get a prediction from the federated model
-- View training progress in the app
-- Toggle between Demo Mode (no server required) and Real Mode (connects to server)
-
-*For best results, keep the server and at least two clients running in parallel.*
-
-## Project Structure
-
-```
-FinFedRAG-Financial-Federated-RAG/
-├── src/
-│   ├── api/           # REST API for server and client communication
-│   ├── client/        # Federated learning client implementation
-│   ├── server/        # Federated learning server and coordinator
-│   ├── rag/           # Retrieval-Augmented Generation components
-│   ├── models/        # VAE/GAN models for data generation
-│   └── utils/         # Privacy, metrics, and utility functions
-├── webapp/            # Streamlit web application
-├── config/            # Configuration files
-├── tests/             # Unit and integration tests
-├── docker/            # Docker configurations
-├── kubernetes/        # Kubernetes deployment files
-└── app.py             # Root app.py for Hugging Face Spaces deployment
-```
-
-## License
-
-MIT
-
-## Contributing
-
-Please read our contributing guidelines before submitting pull requests.
-
----
-
-**Demo URL**: https://huggingface.co/spaces/ArchCoder/federated-credit-scoring