fix : correct deployment files

DEPLOYMENT.md

@@ -0,0 +1,120 @@
+# 🚀 Hugging Face Spaces Deployment Guide
+
+## Quick Deploy to HF Spaces (5 minutes)
+
+### Step 1: Prepare Your Repository
+
+Your repository should have these files in the root:
+- ✅ `app.py` - Streamlit application
+- ✅ `requirements.txt` - Minimal dependencies (streamlit, requests, numpy)
+- ✅ `README.md` - With HF Spaces config at the top
+
+### Step 2: Create HF Space
+
+1. Go to [huggingface.co/spaces](https://huggingface.co/spaces)
+2. Click "Create new Space"
+3. Fill in the details:
+   - **Owner**: `ArchCoder`
+   - **Space name**: `federated-credit-scoring`
+   - **Short description**: `Federated Learning Credit Scoring Demo with Privacy-Preserving Model Training`
+   - **License**: `MIT`
+   - **Space SDK**: `Streamlit` ⚠️ **NOT Docker**
+   - **Space hardware**: `Free`
+   - **Visibility**: `Public`
+
+### Step 3: Upload Files
+
+**Option A: Direct Upload**
+1. Click "Create Space"
+2. Upload these files:
+   - `app.py`
+   - `requirements.txt`
+
+**Option B: Connect GitHub (Recommended)**
+1. In Space Settings → "Repository"
+2. Connect your GitHub repo
+3. Enable "Auto-deploy on push"
+
+### Step 4: Wait for Build
+
+- HF Spaces will install dependencies
+- Build your Streamlit app
+- Takes 2-3 minutes
+
+### Step 5: Access Your App
+
+Your app will be live at:
+```
+https://huggingface.co/spaces/ArchCoder/federated-credit-scoring
+```
+
+## 🎯 What Users Will See
+
+- **Demo Mode**: Works immediately (no server needed)
+- **Interactive Interface**: Enter features, get predictions
+- **Educational Content**: Learn about federated learning
+- **Professional UI**: Clean, modern design
+
+## 🔧 Troubleshooting
+
+**"Missing app file" error:**
+- Ensure `app.py` is in the root directory
+- Check that SDK is set to `streamlit` (not docker)
+
+**Build fails:**
+- Check `requirements.txt` has minimal dependencies
+- Ensure no heavy packages (tensorflow, etc.) in requirements.txt
+
+**App doesn't load:**
+- Check logs in HF Spaces
+- Verify app.py has no syntax errors
+
+## 📁 Required Files
+
+**`app.py`** (root level):
+```python
+import streamlit as st
+import requests
+import numpy as np
+import time
+
+st.set_page_config(page_title="Federated Credit Scoring Demo", layout="centered")
+# ... rest of your app code
+```
+
+**`requirements.txt`** (root level):
+```
+streamlit
+requests
+numpy
+```
+
+**`README.md`** (with HF config at top):
+```yaml
+---
+title: Federated Credit Scoring
+emoji: 🚀
+colorFrom: red
+colorTo: red
+sdk: streamlit
+app_port: 8501
+tags:
+- streamlit
+- federated-learning
+- machine-learning
+- privacy
+pinned: false
+short_description: Federated Learning Credit Scoring Demo with Privacy-Preserving Model Training
+license: mit
+---
+```
+
+## 🎉 Success!
+
+After deployment, you'll have:
+- ✅ Live web app accessible to anyone
+- ✅ No server setup required
+- ✅ Professional presentation of your project
+- ✅ Educational value for visitors
+
+**Your federated learning demo will be live and working!** 🚀 

README.md

@@ -1,3 +1,20 @@
+---
+title: Federated Credit Scoring
+emoji: 🚀
+colorFrom: red
+colorTo: red
+sdk: streamlit
+app_port: 8501
+tags:
+- streamlit
+- federated-learning
+- machine-learning
+- privacy
+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.

requirements-full.txt

@@ -0,0 +1,41 @@
+# Core ML and Deep Learning
+tensorflow>=2.8.0
+numpy>=1.21.0
+pandas>=1.3.0
+scikit-learn>=1.0.0
+
+# Web Framework and API
+flask>=2.8.0
+requests>=2.25.0
+streamlit
+
+# Configuration and utilities
+pyyaml>=6.0
+pathlib2>=2.3.0
+
+# Development and testing
+pytest>=6.0.0
+pytest-cov>=2.0.0
+
+# Logging and monitoring
+python-json-logger>=2.0.0
+
+# Optional: For advanced features
+# tensorflow-federated>=0.20.0  # Uncomment if using TFF
+# torch>=1.10.0  # Uncomment if using PyTorch
+
+# RAG components
+elasticsearch
+faiss-cpu
+
+# Privacy and security
+tensorflow-privacy
+pysyft
+
+# API and web
+fastapi
+uvicorn
+
+# Documentation
+sphinx
+sphinx-rtd-theme 

requirements.txt

@@ -1,44 +1,3 @@
-# Core ML and Deep Learning
-tensorflow>=2.8.0
-numpy>=1.21.0
-pandas>=1.3.0
-scikit-learn>=1.0.0
-
-# Web Framework and API
-flask>=2.0.0
-requests>=2.25.0
 streamlit
-
-# Configuration and utilities
-pyyaml>=6.0
-pathlib2>=2.3.0
-
-# Development and testing
-pytest>=6.0.0
-pytest-cov>=2.0.0
-
-# Logging and monitoring
-python-json-logger>=2.0.0
-
-# Optional: For advanced features
-# tensorflow-federated>=0.20.0  # Uncomment if using TFF
-# torch>=1.10.0  # Uncomment if using PyTorch
-
-# RAG components
-elasticsearch
-faiss-cpu
-
-# Privacy and security
-tensorflow-privacy
-pysyft
-
-# API and web
-fastapi
-uvicorn
-
-# Documentation
-sphinx
-sphinx-rtd-theme
-
-# Additional requirements
-pyyaml
+requests
+numpy