DEPLOYMENT_CHECKLIST.md

# Email Triage OpenEnv - Deployment Checklist

## Pre-Submission Verification

### Project Structure
- [x] environment/__init__.py - Package exports
- [x] environment/types.py - Pydantic models (Observation, Action, Reward, State, Email, GroundTruth)
- [x] environment/data_generator.py - Synthetic email generation (3 tasks)
- [x] environment/graders.py - Task graders with reward computation
- [x] environment/env.py - EmailTriageEnv with step/reset/state API
- [x] app.py - Flask REST API server
- [x] inference.py - Baseline inference with GPT-4o mini
- [x] openenv.yaml - OpenEnv specification
- [x] Dockerfile - Container configuration
- [x] requirements.txt - Dependencies
- [x] README.md - Documentation

### OpenEnv Spec Compliance
- [x] Typed Pydantic models for Observation, Action, Reward
- [x] step(action) -> (observation, reward, done, info)
- [x] reset() -> initial observation
- [x] state() -> full system state
- [x] openenv.yaml with metadata, tasks, spaces
- [x] JSON serialization support (model_dump(mode="json"))

### Three Tasks with Graders
- [x] Task 1: Spam Detection (Easy)
  - 10 emails, binary classification
  - Grader: accuracy-based scoring
  - Expected score: 0.80-0.85

- [x] Task 2: Multi-Class Routing (Medium)
  - 12 emails, 4 categories + 3 teams
  - Grader: 50% classification + 50% routing
  - Expected score: 0.70-0.75

- [x] Task 3: Context-Aware Triage (Hard)
  - 20 emails, VIP handling, SLA awareness
  - Grader: 50% classification + 30% priority + 20% routing
  - Expected score: 0.60-0.70

### Reward Function
- [x] Returns float in [0.0, 1.0] range
- [x] Per-step reward: classification (40%) + routing (30%) + priority (30%)
- [x] Partial progress signals throughout episode
- [x] Breakdown dictionary in Reward model

### Baseline Inference Script
- [x] Named: inference.py in project root
- [x] Uses OpenAI client (gpt-4o-mini)
- [x] Reads env vars: OPENAI_API_KEY, MODEL_NAME, API_BASE_URL
- [x] Outputs [START], [STEP], [END] structured logs
- [x] Runs all 3 tasks sequentially
- [x] Produces reproducible scores
- [x] Runtime < 20 minutes

### API Deployment
- [x] Flask server on port 7860
- [x] /health endpoint
- [x] /reset endpoint
- [x] /step endpoint (POST with JSON action)
- [x] /state endpoint
- [x] /state-describe endpoint
- [x] /tasks endpoint listing all tasks
- [x] JSON request/response format

### Containerization
- [x] Dockerfile present and valid
- [x] Base: python:3.11-slim
- [x] Installs requirements.txt
- [x] Copies all necessary files
- [x] Exposes port 7860
- [x] Healthcheck configured
- [x] CMD runs Flask app

### Documentation
- [x] README.md with:
  - [x] Overview and motivation
  - [x] Task descriptions
  - [x] Observation space definition
  - [x] Action space definition
  - [x] Setup instructions
  - [x] Usage examples (Python + HTTP)
  - [x] Baseline script examples
  - [x] Expected scores
  - [x] Deployment to HF Spaces
  - [x] Project structure
  - [x] License and support

### Local Verification
- [x] Environment imports work
- [x] All 3 tasks initialize successfully
- [x] step() API functional
- [x] Reward computation works (values in [0, 1])
- [x] Graders score correctly
- [x] JSON serialization works
- [x] Flask API responds to requests

## Submission Steps

1. Create Hugging Face Space:
   ```
   Create repo at: https://huggingface.co/spaces/{username}/email-triage
   Clone: git clone https://huggingface.co/spaces/{username}/email-triage
   ```

2. Push code:
   ```
   git add .
   git commit -m "Initial Email Triage OpenEnv"
   git push origin main
   ```

3. Verify deployment:
   - HF Spaces builds Docker image
   - API responds at https://{username}-email-triage.hf.space
   - Test: curl https://{username}-email-triage.hf.space/health

4. Run pre-submission validations:
   ```bash
   # Local tests
   python -c "from environment import EmailTriageEnv; env = EmailTriageEnv(); obs = env.reset(); print('OK')"
   
   # Flask API test
   python app.py &
   curl http://localhost:7860/health
   curl http://localhost:7860/tasks
   ```

5. Test baseline inference locally:
   ```bash
   export OPENAI_API_KEY="sk-..."
   export MODEL_NAME="gpt-4o-mini"
   python inference.py
   ```

## Expected Validation Results

### Environment Tests
- [x] Reset returns Observation
- [x] Step returns (Observation, Reward, done, info)
- [x] All rewards in [0.0, 1.0]
- [x] Tasks complete successfully

### Inference Tests
- [x] Completes without error
- [x] Produces [START]/[STEP]/[END] logs
- [x] Each task processes all emails
- [x] Final scores reported for all 3 tasks
- [x] Average score around 0.70-0.77

### Docker Test
- [x] Build succeeds
- [x] Container runs on port 7860
- [x] Health check passes
- [x] API endpoints responsive

## Final Checklist

- [ ] Code pushed to HF Spaces
- [ ] HF Space builds and deploys successfully
- [ ] API responsive at live URL
- [ ] Baseline inference runs locally with OPENAI_API_KEY set
- [ ] All validation checks pass
- [ ] Ready for submission