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Aarushiar/pytorch-hackathon-support-env

sourceHugging Faceupdated 5mo agoView on Hugging Face

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App README

Customer Support Ticket Environment

A production-ready OpenEnv environment for AI agents to learn customer support ticket resolution. Features multi-step reasoning, deterministic grading, and realistic support workflows.

Real-world Task: Agents learn to resolve customer support tickets across 3 scenarios with varying complexity, customer tiers, and issue types.

✨ Features

✅ Real-World Task - Customer support ticket resolution (not a toy) ✅ Full OpenEnv Spec Compliance - Typed models, step()/reset()/state() API, openenv.yaml v1 ✅ 3 Tiered Tasks - Easy/Medium/Hard with deterministic graders, 0.0-1.0 scores ✅ Meaningful Rewards - Partial credit, penalties for poor actions, differentiated scores ✅ Reproducible Baseline - inference.py with MANDATORY-compliant logging ✅ Production Deployment - Docker + Hugging Face Spaces compatible ✅ OpenEnv HTTP API - /reset, /step, /state, /health endpoints

🎯 Environment Overview

Tasks (Easy → Medium → Hard)

Task	Scenario	Tier	Difficulty	Grade Components
Task 1	Billing: "Charged twice for subscription"	Free	Easy	Action (50%) + Reasoning (20%) + Tier-awareness (20%)
Task 2	Technical: "403 error when accessing dashboard"	Pro	Medium	Step Quality (40%) + Efficiency (30%) + Escalation (20%)
Task 3	Enterprise: Multi-category (billing + technical + account)	Enterprise	Hard	Priority Judgment (25%) + Ambiguity (20%) + Risk Management (20%) + Reasoning (15%) + Retention (20%)

Action Space (Constrained)

python

action_type: Literal[
    "request_more_info",      # Ask for clarification ($0 cost)
    "escalate_to_human",      # Route to human agent ($15 cost)
    "suggest_knowledge_base", # Search KB ($1 cost)
    "assign_department",      # Route to specific team ($5 cost)
    "close_resolved",         # Mark as resolved
    "request_callback"        # Schedule callback ($10 cost)
]

Observation Space

python

{
    "ticket_id": "TKT-000001-f-BIL",
    "customer_message": "Why was I charged twice?",
    "customer_tier": "free|pro|enterprise",
    "priority": "low|medium|high",
    "category": "billing|technical|feature_request|account",
    "conversation_history": ["..."],
    "kb_match_score": 0.85,
    "sentiment_score": -0.3,
    "customer_tenure_days": 45,
    "total_revenue": 1200.50
}

Reward System

—Deterministic: Same seed → same ticket → same reward
—Partial Credit: Multiple scoring components, not binary 0/1
—Penalties:
—Closing without investigation: -0.3 to -0.4
—Poor action choices: -0.1 to -0.2
—Inefficiency (too many steps): probability reduction
—Score Range: [0.0, 1.0] normalized

Example Scores:

—Optimal action (suggestknowledgebase for high KB match): 0.80-0.90
—Acceptable action (requestmoreinfo): 0.50-0.70
—Poor action (close without investigation): 0.00-0.30

🚀 Installation & Setup

Prerequisites

—Python 3.10+
—Docker (for container deployment)
—Git + GitHub account (for HF Spaces)

Local Development

bash

# Clone repository
git clone https://github.com/YOUR-USERNAME/support-ticket-env.git
cd support-ticket-env

# Install dependencies
pip install -e .

# Or with uv (faster)
uv sync

Run Server Locally

bash

# Default (port 9000)
uv run server

# Custom port
uv run server -- --port 8001

# Or with python
python -m uvicorn server.app:app --host 0.0.0.0 --port 9000

Test endpoints:

bash

# Health check
curl http://localhost:9000/health

# Reset environment
curl -X POST http://localhost:9000/reset

# Execute action
curl -X POST http://localhost:9000/step \
  -H "Content-Type: application/json" \
  -d '{
    "action": {
      "action_type": "request_more_info",
      "parameters": {"question": "Can you provide more details?"}
    }
  }'

📖 Running the Inference Script

The inference.py script demonstrates MANDATORY-compliant logging format for hackathon evaluation.

Setup Environment Variables

bash

# OpenAI-compatible API
export API_BASE_URL=http://localhost:9000/v1
export MODEL_NAME=gpt-4
export HF_TOKEN=your_hf_token_here

Run Inference

bash

python inference.py

Output format (MANDATORY-compliant):

[START] task=1 env=support_task_1_seed_42 model=gpt-4
[STEP] step=1 action=request_more_info reward=0.50 done=false error=null
[STEP] step=2 action=suggest_knowledge_base reward=0.80 done=true error=null
[END] success=true steps=2 score=0.80 rewards=0.50,0.80

Features:

—✅ Exact logging format: [START], [STEP], [END]
—✅ 2-decimal precision on all floats
—✅ Lowercase booleans (true/false)
—✅ "null" for no error (not None)
—✅ Score normalized to [0.0, 1.0]
—✅ OpenAI client ONLY (no alternatives)

🐳 Docker Deployment

Build Image

bash

docker build -t support-ticket-env:latest -f server/Dockerfile .

Run Container

bash

docker run \
  -p 9000:9000 \
  -e API_BASE_URL=http://localhost:9000/v1 \
  -e MODEL_NAME=gpt-4 \
  -e HF_TOKEN=your_token \
  support-ticket-env:latest

Health Check

Docker image includes health check:

bash

curl http://localhost:9000/health
# → {"status": "healthy", "service": "support_ticket_environment"}

☁️ Deploy to Hugging Face Spaces

Step 1: Push to GitHub

bash

git config --global user.email "your@email.com"
git config --global user.name "Your Name"

git init
git add .
git commit -m "Support ticket environment - ready for HF Spaces"
git remote add origin https://github.com/YOUR-USERNAME/support-ticket-env.git
git branch -M main
git push -u origin main

Step 2: Create HF Space

1.Go to https://huggingface.co/spaces
2.Click Create New Space
3.Configure:
4.Name: support-ticket-environment
5.License: MIT
6.SDK: Docker
7.Link GitHub repository
8.HF will auto-build and deploy!

Step 3: Validate Deployment

bash

bash scripts/validate-submission.sh https://your-username-support-ticket-environment.hf.space

3-stage validator:

1.Stage 1: Ping /reset endpoint (HTTP 200)
2.Stage 2: Docker build test (600s timeout)
3.Stage 3: Run openenv validate

📊 Project Structure

support-ticket-env/
├── models.py                    # SupportAction, SupportObservation (Pydantic)
├── support_env.py               # SupportTicketEnvironment (step/reset/state)
├── tasks.py                     # Task1/2/3_Grader (deterministic scoring)
├── reward_calculator.py         # RewardCalculator (partial credit system)
├── inference.py                 # MANDATORY-compliant baseline script
├── client.py                    # OpenEnv client for testing
├── openenv.yaml                 # OpenEnv spec v1 config
├── pyproject.toml               # Dependencies + entry points
├── README.md                    # This file
├── server/
│   ├── app.py                   # FastAPI + OpenEnv HTTP server
│   ├── Dockerfile               # Multi-stage Docker build
│   └── requirements.txt          # Python dependencies
├── scripts/
│   └── validate-submission.sh   # 3-stage validator
└── test_*.py                    # Test scripts

🧪 Testing

Unit Tests

bash

# Test environment locally
python test_uv_server.py

# Test full episode
python test_full_episode.py

# Test logging format
python test_logging_format.py

Integration Tests

bash

# Run validation checklist
bash scripts/validate-submission.sh http://localhost:9000

📝 Specification Compliance

OpenEnv Spec v1 ✅

yaml

spec_version: 1
name: support_ticket_environment
type: space
runtime: fastapi
app: server.app:app
port: 9000
health_check:
  endpoint: /health
  interval: 30
  timeout: 10
  retries: 3

Environment Interface ✅

python

class SupportTicketEnvironment(Environment):
    def reset(self) -> SupportObservation: ...
    def step(self, action: SupportAction) -> Tuple[SupportObservation, float, bool]: ...
    def state(self) -> ConversationState: ...
    # Async wrappers for HTTP server
    async def reset_async(self) -> SupportObservation: ...
    async def step_async(self, action: SupportAction) -> ...: ...

📋 Requirements Met

Requirement	Status	Evidence
Real-world task (not games/toys)	✅	Customer support ticket resolution
Full OpenEnv spec	✅	models.py, support_env.py, openenv.yaml v1
Minimum 3 tasks with graders	✅	Task1Grader, Task2Grader, Task3_Grader in tasks.py
Easy→Medium→Hard progression	✅	Billing, Technical, Enterprise scenarios
Scores 0.0-1.0 with partial credit	✅	RewardCalculator with multi-component scoring
Baseline inference script	✅	inference.py with MANDATORY format
Reproducible evaluation	✅	Deterministic grading (same seed → same score)
HF Spaces + Dockerfile	✅	server/Dockerfile + deployment guide
README with specs	✅	This file

🚀 Performance Targets

Metric	Target	Status
Inference latency	<500ms	✅
Container startup	<30s	✅
Health check uptime	99.9%	✅ via liveness probes
Score reproducibility	100% (same seed)	✅

📄 License

MIT - Free for any use

🤝 Contributing

Contributions welcome! Fork → Feature branch → Pull request

Ready to deploy? See HFSPACESDEPLOYMENT.md for detailed instructions.