DEPLOYMENT.md

🚀 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
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):

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!** 🚀