README.md

metadata

title: ADHD Task Initiation Coaching Environment
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
app_port: 8000
tags:
  - openenv
  - reinforcement-learning
  - adhd
  - executive-function

ADHD Task Initiation Coaching Environment

An OpenEnv environment that evaluates ADHD coaching response quality. It scores AI coaching responses for task initiation paralysis based on tool calling and response quality.

Innovation: State tracking ("knobs") + tool calling evaluation - not just text scoring.

Quick Start

from adhd_env import ADHDAction, ADHDEnv

# Connect to deployed environment
with ADHDEnv(base_url="https://YOUR-SPACE.hf.space") as env:
    # Get an ADHD scenario
    result = env.reset()
    print(f"Scenario: {result.observation.scenario}")

    # Submit a coaching response for scoring
    result = env.step(ADHDAction(
        tool_calls=["adhd_task_initiation_coach"],
        message="Open email and type just the recipient name. Stop there."
    ))
    print(f"Reward: {result.reward}")  # 1.0

How Scoring Works

The environment evaluates coaching responses on tool calling (V1):

Action	Reward	Why
Called adhd_task_initiation_coach	1.0	Used the primary coaching tool
Called set_timer or break_down_task	0.5	Valid tool, but not the primary one
No tools called	0.0	No tool engagement

Available Tools

• adhd_task_initiation_coach - Primary coaching tool for task initiation
• set_timer - Focus timers for task boxing
• break_down_task - Decompose large tasks into micro-steps

API

POST /reset

Returns a new ADHD scenario with user state.

POST /step

Scores a coaching response. Body: {"action": {"tool_calls": [...], "message": "..."}}

GET /health

Health check endpoint.

GET /schema

JSON schemas for action and observation models.

Environment Details

ADHDAction

• tool_calls (list[str]) - Tools the model would call
• message (str) - The coaching response text

ADHDObservation

• scenario (str) - The ADHD task initiation scenario
• state (dict) - User state tracking (sitting time, energy, etc.)
• scoring (dict) - Detailed scoring breakdown with explanations
• reward (float) - Score 0.0-1.0
• done (bool) - Episode complete flag

Development

# Install dependencies
cd adhd_env && uv sync

# Run locally
uvicorn server.app:app --reload --host 0.0.0.0 --port 8000

# Test
python test_environment.py        # Direct test
python test_environment.py --http  # HTTP test (server must be running)

# Validate structure
openenv validate --verbose

# Deploy to HF Spaces
openenv push --repo-id USERNAME/adhd-env