Skip to content

SETUP_COMPLETE.md

Latest commit

 

History

History
285 lines (205 loc) · 7.92 KB

SETUP_COMPLETE.md

File metadata and controls

285 lines (205 loc) · 7.92 KB

🎉 Production Backend Setup Complete!

✅ What's Been Created

Your AutoDataLab project now has a complete production-grade backend architecture:

📦 Backend Services (Docker Compose)

  • ✅ FastAPI Backend - REST API server (port 8000)
  • ✅ Celery Worker - Background job processor
  • ✅ PostgreSQL - Database (port 5432)
  • ✅ Redis - Cache and message broker (port 6379)
  • ✅ MinIO - S3-compatible storage (ports 9000, 9001)

🗂️ Project Structure

Major_project/
├── backend/
│   ├── app/
│   │   ├── api/              # API endpoints (upload, jobs)
│   │   ├── core/             # Configuration
│   │   ├── worker/           # Celery tasks
│   │   ├── main.py           # FastAPI application
│   │   └── storage.py        # Job storage
│   ├── Dockerfile            # Backend container
│   ├── requirements.txt      # Python dependencies
│   └── README.md            # Backend documentation
├── project-frontend/         # React frontend (already configured)
├── docker-compose.yml        # All services orchestration
├── .env                      # Environment variables
├── start-dev.bat            # Windows quick start
├── start-dev.sh             # Linux/Mac quick start
├── QUICKSTART.md            # Step-by-step setup guide
├── PRODUCTION_GUIDE.md      # Production deployment guide
└── README.md                # Project overview

🔌 API Endpoints

  • POST /api/v1/upload - Upload CSV and create job
  • GET /api/v1/jobs/{job_id}/status - Get job status and results
  • GET /docs - Interactive API documentation (Swagger)

🎨 Frontend Updates

  • ✅ Updated src/api/client.ts to connect to real backend
  • ✅ Can switch between MSW mocks and real API via environment variable
  • ✅ Ready to use real backend for uploads and job tracking

🚀 How to Run (2 Options)

Option 1: Quick Start (Recommended)

# Run the setup script
.\start-dev.bat

# In a new terminal, start frontend
cd project-frontend
npm install
npm run dev

Option 2: Manual Start

# Start backend services
docker-compose build
docker-compose up -d

# Start frontend
cd project-frontend
npm install
npm run dev

📊 Access Points

After starting services:

Service URL Notes
Frontend http://localhost:5173 React UI
Backend API http://localhost:8000 FastAPI
API Docs http://localhost:8000/docs Interactive Swagger UI
MinIO http://localhost:9001 Storage console (minioadmin/minioadmin)

🧪 Quick Test Flow

  1. Open http://localhost:5173
  2. Navigate to Upload page
  3. Upload a CSV file
  4. Watch real-time progress as the job runs
  5. View the analysis with:
    • Dataset summary
    • Data profiling report
    • Cleaned CSV download

📝 What Happens When You Upload

  1. Frontend sends file to POST /api/v1/upload
  2. Backend saves file and creates job
  3. Celery worker picks up the job:
    • Reads CSV with pandas
    • Drops duplicates
    • Generates profiling report with ydata-profiling
    • Uploads artifacts to MinIO
    • Updates job status
  4. Frontend polls GET /api/v1/jobs/{id}/status
  5. User sees progress and can view/download results

🔍 Monitoring & Debugging

View Logs

# All services
docker-compose logs -f

# Specific service
docker-compose logs -f backend
docker-compose logs -f worker

# Frontend (in npm terminal)
# Logs appear automatically

Check Service Health

# List all containers
docker-compose ps

# Should show all as "Up" or "running"

Test Backend Directly

# Health check
curl http://localhost:8000

# API docs (open in browser)
start http://localhost:8000/docs

🛠️ Development Workflow

Backend Changes

  1. Edit files in backend/app/
  2. Changes auto-reload (volume mounted)
  3. Check logs: docker-compose logs -f backend

Frontend Changes

  1. Edit files in project-frontend/src/
  2. Vite hot-reloads automatically
  3. Check browser console for errors

Adding New API Endpoint

  1. Create route in backend/app/api/your_file.py
  2. Add router to backend/app/main.py
  3. Update frontend to call new endpoint
  4. Test in Swagger UI first

Adding New Celery Task

  1. Add task function in backend/app/worker/tasks.py
  2. Call with .delay() from API endpoint
  3. Check worker logs to debug

📚 Documentation

🎯 Next Steps (Recommended Order)

Today

  1. ✅ Run the quick start
  2. ✅ Upload a test CSV file
  3. ✅ Verify job processing works
  4. ✅ Check MinIO console to see artifacts

This Week

  1. Customize data processing in backend/app/worker/tasks.py
  2. Add ML model training (scikit-learn)
  3. Enhance report generation
  4. Add more frontend features

Production (Next Month)

  1. Set up AWS account
  2. Follow PRODUCTION_GUIDE.md
  3. Deploy to staging environment
  4. Add authentication (Auth0/Cognito)
  5. Deploy to production

🐛 Common Issues & Solutions

"Docker is not running"

→ Start Docker Desktop and wait for it to fully initialize

"Port 8000 already in use"

→ Stop other services: netstat -ano | findstr :8000 → Or change port in docker-compose.yml

"Cannot connect to backend"

→ Check backend logs: docker-compose logs backend → Verify backend is up: http://localhost:8000

"Worker not processing jobs"

→ Check worker logs: docker-compose logs -f worker → Restart: docker-compose restart worker

"Frontend not loading"

→ Check npm install completed successfully → Verify .env exists in project-frontend/ → Check browser console for errors

🎓 Learning Resources

💡 Pro Tips

  1. Use API docs at http://localhost:8000/docs to test endpoints before frontend
  2. Check worker logs when jobs seem stuck
  3. MinIO console is useful to verify artifacts are uploaded
  4. Keep services running - no need to restart Docker often
  5. Git commit often - especially before major changes

✅ Verification Checklist

Run through this to verify everything works:

  • Docker Desktop is running
  • docker-compose ps shows all services as "Up"
  • http://localhost:8000 returns JSON response
  • http://localhost:8000/docs shows Swagger UI
  • http://localhost:5173 loads frontend
  • Can upload CSV file
  • Job status page shows progress
  • Worker processes job (check logs)
  • Can view analysis page with results
  • Can download cleaned CSV
  • MinIO console shows uploaded files

🚀 You're Ready!

Everything is set up and ready to go. The system is:

✅ Production-grade - Proper service separation, queue system, storage ✅ Scalable - Can add more workers, use real S3, deploy to cloud ✅ Developer-friendly - Hot reload, volume mounts, good logging ✅ Well-documented - Multiple guides for different needs

Start with: .\start-dev.bat

Questions? Check:

  1. QUICKSTART.md for detailed setup
  2. PRODUCTION_GUIDE.md for architecture
  3. Logs: docker-compose logs -f

Happy coding! 🎉

Last updated: November 2025