DEPLOYMENT_CHECKLIST.md

HuggingFace Spaces Deployment Checklist

Pre-Deployment Verification

1. Local Testing (Recommended)

# Install dependencies
pip install -r requirements.txt

# Quick sanity check
python3 test_flan_t5.py

# Full UI test
python3 app.py
# Open http://localhost:7860 and test manually

2. File Verification

• app.py - HF Spaces entry point ✅
• requirements.txt - All dependencies listed ✅
• README_HF_SPACES.md - HF Spaces README (copy as README.md) ✅
• src/writing_studio/ - All source code ✅
• LICENSE - MIT license file
• .gitignore - Ignore logs, cache, etc.

3. Configuration Check

• Default model: google/flan-t5-base ✅
• Max text length: 10,000 characters ✅
• Log format: text (easier to read on HF Spaces) ✅
• Metrics disabled: ENABLE_METRICS=false ✅
• No .env file required ✅

HuggingFace Spaces Setup

Step 1: Create Space

1. Go to https://huggingface.co/new-space
2. Choose a name (e.g., "ai-writing-studio")
3. License: MIT
4. SDK: Gradio
5. SDK version: 4.0.0 (must be quoted in YAML)
6. Hardware: CPU basic (free tier works!)
7. Visibility: Public or Private

Step 2: Upload Files

Option A: Git Push (Recommended)

# Initialize git if not already
git init
git add .
git commit -m "Initial commit: FLAN-T5 powered AI Writing Studio"

# Add HF Space as remote
git remote add hf https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
git push hf main

Option B: Web Upload

1. Click "Files" tab in your Space
2. Upload files one by one or drag-and-drop folders
3. Ensure app.py is in root directory

Step 3: Configure README

1. Copy README_HF_SPACES.md to README.md
2. Update GitHub URLs if you have a repo

3. Verify YAML frontmatter: ```yaml

   title: AI Writing Studio emoji: ✍️ colorFrom: blue colorTo: purple sdk: gradio sdk_version: "4.0.0" # MUST BE QUOTED! app_file: app.py suggested_hardware: cpu-basic

Step 4: Set Environment Variables (Optional)

In Space settings, add if needed:

• LOG_LEVEL=INFO
• ENVIRONMENT=production
• DEBUG=false

Default values work fine without setting these!

Post-Deployment Testing

Immediate Checks

• Space builds successfully (no errors in logs)
• Gradio UI loads
• All UI elements present (input box, model selector, prompt pack dropdown)
• No import errors in logs

First Analysis Test

• Paste test text (200-500 words)
• Select "General" revision mode
• Click "✨ Revise & Analyze"
• Wait ~60 seconds (first model load)
• Verify revision is generated
• Check revision differs from original
• Verify rubric scores appear
• Check diff highlighting works

Second Analysis Test

• Paste different text
• Try different revision mode (e.g., "Academic")
• Click analyze
• Should be MUCH faster (~5-10s) - model cached!
• Verify revision style matches selected mode

Common Deployment Issues

Issue 1: "Missing configuration" error

Cause: YAML frontmatter malformed Fix: Ensure sdk_version: "4.0.0" is quoted!

Issue 2: "Module not found" error

Cause: Missing dependency in requirements.txt Fix: Check all imports are listed in requirements.txt

Issue 3: Space crashes on first load

Cause: OOM during model download Fix:

• Refresh and try again (HF Spaces issue)
• Verify using flan-t5-base (not -large)
• Consider upgrading hardware tier

Issue 4: Slow response times

Cause: Model reloading on each request Fix:

• Check logs for "Loading model" messages
• Verify @lru_cache on get_model_service()
• Model should load once and persist

Issue 5: Revision quality is poor

Cause: FLAN-T5-base is smallest model Fix:

• Upgrade to CPU upgrade or T4 GPU
• Change model to google/flan-t5-large
• Set environment variable: DEFAULT_MODEL=google/flan-t5-large

Performance Expectations

Free Tier (CPU Basic)

• Model: google/flan-t5-base
• First load: ~60 seconds
• Subsequent: ~5-10 seconds
• Concurrent users: 1-2
• Cost: $0/month ✅

CPU Upgrade

• Model: google/flan-t5-large possible
• First load: ~2-3 minutes
• Subsequent: ~10-15 seconds
• Concurrent users: 3-5
• Cost: ~$0.03/hour when running

T4 GPU

• Model: google/flan-t5-xl possible
• First load: ~5 minutes
• Subsequent: ~3-5 seconds
• Concurrent users: 10+
• Cost: ~$0.60/hour when running

Monitoring

Check Space Health

1. Logs: Click "Logs" tab in Space

   • Look for "Model loaded successfully"
   • Check for any errors during startup
   • Monitor analysis request times

2. Usage: Check Space settings

   • See user count
   • Monitor resource usage
   • Check for crashes/restarts

3. Feedback: Enable Discussions

   • Users can report issues
   • Collect feedback on revision quality

Success Criteria

• Space builds without errors ✅
• UI loads and displays correctly ✅
• First analysis completes in ~60s ✅
• Subsequent analyses in ~5-10s ✅
• AI revisions are coherent and on-topic ✅
• Different prompt packs work differently ✅
• Rubric scores display correctly ✅
• Diff highlighting shows changes ✅
• No crashes or OOM errors ✅

Post-Launch

Week 1

• Monitor logs for errors
• Collect user feedback
• Note common issues
• Document workarounds

Month 1

• Analyze usage patterns
• Consider model upgrade if needed
• Optimize prompt packs based on feedback
• Add new revision modes if requested

Ongoing

• Keep dependencies updated
• Monitor HF Spaces announcements
• Update FLAN-T5 model if newer versions release
• Consider adding more features (export, history, etc.)

Support

If deployment issues occur:

1. Check HF Spaces status: https://status.huggingface.co/
2. Review Space logs for errors
3. Compare with working example Spaces
4. Ask in HF Spaces Discord or forums
5. Check this project's GitHub issues

Next Steps After Deployment

1. ✅ Share your Space URL!
2. ✅ Add to your portfolio/projects
3. ✅ Tweet about it with #HuggingFace #GradIO
4. ✅ Submit to Gradio showcase
5. ✅ Collect user feedback
6. ✅ Iterate based on usage
7. ✅ Consider adding more features

Good luck with deployment! 🚀