README.md

# Office Workflow OpenEnv Environment

`office_workflow_env` simulates three realistic office tasks humans do and exposes them via OpenEnv’s standard `reset()` / `step()` / `state()` API.

It supports deterministic grading (0.0–1.0) for:

1. `email_triage` (easy)
2. `data_cleaning` (medium)
3. `support_escalation` (hard)

At the end of each episode, the environment writes the final task score to `observation.info.final_score` and the structured breakdown to `observation.info.final_breakdown`.

## Task / Episode API

### Reset
Call `reset(task_id=..., seed=..., episode_id=...)`.

* `task_id`: one of `email_triage`, `data_cleaning`, `support_escalation`
* `seed`: integer seed for deterministic shuffling

### Step
Call `step(action)` where `action` is an `OfficeAction`.

The server returns:
* `observation`: an `OfficeObservation`
* `reward`: scalar float (shaped with partial progress)
* `done`: boolean when the task is finished or `max_steps` is reached

The `info` field is carried inside `observation.info`.

### State
Call `state()` to retrieve `OfficeState` (progress + action trace; no hidden ground truth answers).

## Action Space (`OfficeAction`)

Single action schema for all tasks, discriminated by `type`:

* `type="triage_email"`
  * `email_id`: string
  * `category`: one of `billing`, `technical`, `spam`, `other`
  * `priority`: int in `[0, 5]`

* `type="correct_cell"`
  * `row_id`: string
  * `column`: one of `email`, `phone`, `date`
  * `value`: proposed cleaned value

* `type="support_decision"`
  * `ticket_id`: string
  * `intent`: one of `ask_for_information`, `resolve`, `escalate`
  * `reply`: draft reply text
  * plus intent-specific fields:
    * `requested_info` (when `ask_for_information`)
    * `resolution_steps` (when `resolve`)
    * `escalation_reason` (when `escalate`)

Optional helper actions:
* `type="request_status"`
* `type="noop"`

## Observation Space (`OfficeObservation`)

Common fields:
* `task_id`, `status`, `max_steps`, `step_index`

Task-specific fields:
* `email_triage`: `emails`, `triaged`
* `data_cleaning`: `dataset`, `cleaned_cells`
* `support_escalation`: `tickets`, `handled_tickets`

Grader-facing info:
* `info.objective`, `info.completion_rule` (task instructions)
* `info.reward_breakdown` (per-step reward component breakdown)
* final episode results in `info.final_score` and `info.final_breakdown`

## Reward Shaping (partial progress)

Rewards are not binary: they increase as the agent makes correct partial progress.

All tasks:
* progress component grows with corrected/handled items
* correctness component rewards correct submissions
* stalling/wrong actions are penalized by driving reward toward `0.0`

## Local Setup

### Step-by-step (Windows / PowerShell)

1. Open a terminal in `c:\Users\hp\Desktop\hackathon`
2. Install dependencies:

```bash
python -m pip install -U pip
python -m pip install "openenv-core[core]>=0.2.1" openai requests uvicorn
```

3. Validate OpenEnv structure:

```bash
openenv validate
```

4. Start the server:

```bash
uvicorn server.app:app --host 0.0.0.0 --port 8000
```

5. Confirm it responds:
   - `http://localhost:8000/health`
   - `http://localhost:8000/docs`

6. Run the connectivity smoke test (expected score 0.0 because it sends only `noop`):

```bash
python scripts/smoke_test.py
```

7. Run a “real” local demo baseline (no OpenAI key required; rule-based):

```bash
python scripts/baseline_inference.py
```

## Baseline Inference (OpenAI)

The baseline script runs a model against all 3 tasks and prints reproducible scores.

Environment variables:
* `OPENAI_API_KEY`
* `OPENAI_MODEL` (optional, default `gpt-4o-mini`)
* `OPENENV_BASE_URL` (optional, default `http://localhost:8000`)
* `BASELINE_SEED` (optional, default `123`)

Run:
```bash
python scripts/baseline_inference.py
```

## Hugging Face Spaces Deployment

This repo includes a `server/Dockerfile` suitable for Hugging Face Spaces.

When you’re ready to deploy:
```bash
openenv validate
openenv push --repo-id YOUR_HF_USERNAME/office-workflow-env
```

`openenv push` will package the environment and build the Docker image for Spaces.

After it deploys, validate the running Space:

```bash
openenv validate https://YOUR_HF_USERNAME-office-workflow-env.hf.space
```