README_DEPLOYMENT.md

Deployment Options

🎯 Recommended: Hugging Face Spaces (FREE)

Perfect for ML apps! No size limits, designed for transformers.

Steps:

1. Create Account: https://huggingface.co/join

2. Create New Space:

   • Go to: https://huggingface.co/new-space
   • Name: nlp-analysis-api
   • License: MIT
   • SDK: Docker
   • Hardware: CPU (free)

3. Upload Files:

   • Clone the HF Space repo locally
   • Copy all files from backend/nlp-backend/ to the Space
   • Add Dockerfile (already created)
   • Push to HF Space

4. Get Live URL: https://huggingface.co/spaces/YourUsername/nlp-analysis-api

Benefits:

• ✅ FREE forever
• ✅ No size limits
• ✅ ML-optimized infrastructure
• ✅ Great for portfolio

---

Option 2: Render.com (FREE with limitations)

Pros:

• ✅ Free tier available
• ✅ Auto-deploys from GitHub
• ✅ No image size limit

Cons:

• ⚠️ 512 MB RAM (may need to optimize)
• ⚠️ Sleeps after 15 min inactivity

Steps:

1. Go to: https://render.com
2. Create account
3. New → Web Service
4. Connect GitHub repo
5. Root Directory: backend/nlp-backend
6. Build Command: pip install -r requirements.txt
7. Start Command: uvicorn main:app --host 0.0.0.0 --port $PORT
8. Select Free tier
9. Deploy!

---

Option 3: Fly.io (FREE tier)

Pros:

• ✅ Generous free tier
• ✅ Good for Docker apps
• ✅ Fast deployments

Steps:

1. Install flyctl: https://fly.io/docs/hands-on/install-flyctl/
2. Login: flyctl auth login
3. In backend/nlp-backend/: flyctl launch
4. Follow prompts
5. Deploy: flyctl deploy

---

Option 4: Railway.app (PAID - $5/month)

Only if you want to pay:

• Hobby plan: $5/month
• Removes image size limit
• Better for production

---

🎯 Recommendation

Use Hugging Face Spaces - it's free, unlimited, and perfect for ML apps!

The community loves seeing ML projects on HF Spaces, and it's great for your portfolio.