Blame for tiny_computer_agent_app.py

# /// script
# requires-python = ">=3.13"
# dependencies = [
#     "rapidocr-onnxruntime==1.4.4",
# ]
# ///

import marimo

__generated_with = "0.25.2"
app = marimo.App(width="medium", auto_download=["html"])

with app.setup:
    import os
    from pathlib import Path

    import marimo as mo
    from huggingface_hub import HfApi


@app.cell(hide_code=True)
def readme():
    readme_md = '''

    ---
    title: Tiny Computer Agent
    emoji: 🖱️
    colorFrom: indigo
    colorTo: green
    license: mit
    tags:
      - computer-use
      - gui-agent
      - agentic-harness
      - local-first
      - tiny-models
      - offline
      - marimo
    ---

    # 🖱️ Tiny Computer Agent

    **An agentic harness that drives a real computer with a tiny, fully local model.**
    No cloud API, no API keys, no telemetry — and a core that imports nothing outside the
    Python standard library.

    > **Status 0.3.0: it drives a real desktop.** Screen capture, an OCR element index with
    > real coordinates, a real mouse click, and a *verified* outcome — all through the
    > harness, in one episode. What is not solved is model reliability: see
    > [measured results](#measured-results).

    ## The idea, in one loop

    ```
        observe         think          act           verify
      +---------+    +---------+    +---------+    +---------+
      | screen  |--->|  tiny   |--->| mouse / |--->|  next   |
      |  frame  |    |  model  |    | keyboard|    |  frame  |
      +---------+    +---------+    +---------+    +----+----+
                                                        |
      <-------------------------------------------------+
            repeat until done, or the step budget runs out
    ```

    1. **Observe** — capture the screen, then build an **element index**: OCR (and,
       optionally, the OS accessibility tree) locates text and widgets and gives each one a
       label and a real screen coordinate.
    2. **Think** — the model is shown a *short numbered menu* built from that index and
       answers with one number (plus a verb). It never emits coordinates.
    3. **Act** — the harness maps the chosen label back to pixels and executes it.
    4. **Verify** — re-read the screen; stop if the same action repeats against an unchanged
       screen, and only call the episode a success if a verifier can see it worked.

    ## Measured results

    Everything below was run from this repo on CPU, dry-run unless stated. Nothing is
    aspirational.

    ### On a live desktop, with a real click

    A real Tk window (a Save dialog with Save/Cancel buttons) on a **real X server**
    (`Xvfb :99`), driven with real mouse input. The button sits at window `+120+120`,
    `x=20, y=155`, `110x40`, so its centre is `(195, 295)`:

    ```
    $ python -m tiny_computer_agent --live --real-input --verify-text Saved "click 'Save' to save the document"
    mode: REAL INPUT (mouse and keyboard live)  |  screen: real desktop (monitor 1)  |  brain: planner
    index: auto - OCR when it finds text, grid otherwise (rapidocr-onnxruntime ready (CPU, offline))

      step 0 | click label='Save' reason="matched 'Save' in the element index" | ok: clicked (195, 296)

    result: success - verified after 1 action(s)
    ```

    The click landed on the button, the app changed its own window to "Saved", and the
    verifier confirmed it before the episode was called a success. **Pixels in, a real click
    out, and evidence that it worked.**

    ### Re-confirmed end to end on a headless Linux box

    The whole path above was re-run from scratch on a headless Debian container (gVisor
    sandbox, 20 vCPU, no GPU, no physical display, no network at run time) behind a virtual
    X server. First the suite, headless:

    ```bash
    python -m pip install -e ".[desktop,ocr,dev]"
    pytest tests -q
    # 123 passed, 2 skipped in 18.6s
    ```

    The two skips are the live-desktop tests. Give them a display and they run for real:

    ```bash
    Xvfb :99 -screen 0 1280x800x24 &
    export DISPLAY=:99
    TINY_COMPUTER_AGENT_LIVE_TEST=1 python -m pytest tests/test_live_desktop.py -q
    # tests/test_live_desktop.py::test_ocr_finds_a_real_button_at_the_right_pixels PASSED
    # tests/test_live_desktop.py::test_a_real_click_lands_on_the_button PASSED
    # 2 passed
    ```

    Then the same desktop, driven through the CLI with the mouse actually live:

    ```bash
    export TINY_COMPUTER_AGENT_ALLOW_REAL_INPUT=1
    python -m tiny_computer_agent --live --real-input --verify-text Saved \
        "click 'Save' to save the document"
    ```

    ```
    goal: click 'Save' to save the document
    mode: REAL INPUT (mouse and keyboard live)  |  screen: real desktop (monitor 1)  |  brain: planner  |  kill switch: create /tmp/tiny-computer-agent.STOP to stop
    index: auto - OCR when it finds text, grid otherwise (rapidocr-onnxruntime ready (CPU, offline))

      step 0 | click label='Save' reason="matched 'Save' in the element index" | ok: clicked (194, 296)

    result: success - verified after 1 action(s)
    ```

    The window was drawn at `+120+120` with its Save button at `x=20, y=155, 110x40`, so the
    button centre is `(195, 295)`. The click at `(194, 296)` is *on the button*, not near it —
    and the app wrote `Save` back to its own marker file. Pixels in, a real click out, and the
    app's own side effect as the proof.

    `--doctor` on that box, with the virtual display attached:

    ```
    tiny-computer-agent preflight (Linux x86_64, python 3.13.11)

      [ok  ] display server: DISPLAY=:99
      [ok  ] pillow: installed
      [ok  ] screen capture: mss (fast, multi-monitor)
      [ok  ] input backend (pyautogui): installed
      [ok  ] element detector (auto): OCR when it finds text, grid otherwise (rapidocr-onnxruntime ready (CPU, offline))
      [ok  ] model runtime: not needed: the planner is rule-based (no weights, no download)
      [ok  ] execution mode: dry run (nothing is clicked)
      [ok  ] kill switch: create /tmp/tiny-computer-agent.STOP to abort the next action

    READY: the harness can drive this desktop.
    ```


    ### On a replayed screenshot

    ```
    $ python -m tiny_computer_agent --image example --brain planner
    elements detected: 5
      step 0 | click label='Save' ... -> pixel (295, 284)          # OCR found the button
    ```

    **SmolVLM-256M-Instruct** (256M parameters, CPU) read the button off the pixels and
    named the right target:

    ```
      step 0 | click label='Save' reason="the reply names 'Save'" -> pixel (295, 284)
    ```

    ### Which brain drove which run

    The harness and the brain are separable, and it matters which one a result came from.
    None of the runs below used a cloud API; the two model brains run on CPU:

    | Run | Brain | Weights | Outcome |
    |---|---|---|---|
    | Live Linux desktop, real click | `PlannerBrain` (rule-based) | none | **success, verified** |
    | `/tmp/saveapp.py` window live | `PlannerBrain` | none | clicked `Save` at `(194, 296)` |
    | `--image example` | `PlannerBrain` | none | picked `Save`, pixel `(295, 284)` |
    | `--image example` | `VLMBrain` (SmolVLM-256M-Instruct) | 256M, CPU | picked `Save` |
    | Live Linux desktop | `VLMBrain` (SmolVLM-256M-Instruct) | 256M, CPU | echoed the prompt; **refused** by echo detection |
    | Live Linux desktop | `TextPlannerBrain` (Qwen2.5-0.5B) | 0.5B, CPU | clicked Cancel, then garbled JSON; budget exhausted |

    The rule-based planner is what carries the verified end-to-end runs. The tiny model is the
    open problem: it is reliable on a clean screenshot and unreliable on a busy live screen.

    ### What did *not* work, and what caught it

    - **The 256M VLM echoes the prompt on a busy live screen.** Asked to click Save on the
      live Tk window it replied with the prompt text back. The harness's echo detection
      refused it (`BrainError: the reply mentions 4 menu entries, so it looks like an echo of
      the prompt`) and stopped — **no blind click**. On the cleaner synthetic screenshot the
      same model chose correctly, so this is a prompt/robustness problem, not a wiring one.
    - **Qwen2.5-0.5B text planner over the OCR index is not reliable**: it clicked Cancel
      before Save, then recovered a garbled JSON reply, then found Save — budget exhausted.
    - **The grid fallback announced itself.** After the dialog closed, OCR found no text, the
      index degraded to 96 numbered cells, and the run printed
      `warning: no text was found on screen, so the index is 96 numbered grid cells and the
      agent is choosing blind` — instead of silently guessing.

    **125 tests pass**, including real OCR over a real image and (opt-in) live-desktop tests
    that click a real button.

    ## The test suite, in full

    `pytest tests` collects **125 tests**. 123 run headless — no display, no network, no
    weights. The last 2 are the opt-in live-desktop tests, skipped unless
    `TINY_COMPUTER_AGENT_LIVE_TEST=1`.

    | File | Tests | What it proves |
    |---|---:|---|
    | `tests/test_actions.py` | 13 | the closed vocabulary — every action is validated before anything executes |
    | `tests/test_act.py` | 8 | `DryRunExecutor` is inert; the real executor refuses until explicitly armed |
    | `tests/test_observe.py` | 8 | element/observation geometry, `FakeScreen`, `ImageScreen`, the capture fallback |
    | `tests/test_perception.py` | 13 | OCR labels a real image with real coordinates; the grid fallback; `AutoDetector` |
    | `tests/test_brain.py` | 18 | the numbered `DecisionMenu`, lenient parsing, echo detection, `MockBrain`, `PlannerBrain` |
    | `tests/test_loop.py` | 14 | `AgentLoop` / `Episode` / `Step`, the no-progress guard, the step budget |
    | `tests/test_verify.py` | 7 | success is only claimed when a verifier can actually see it |
    | `tests/test_safety.py` | 11 | deny lists, the kill switch, the blind-grid-click guard |
    | `tests/test_grid_fallback.py` | 11 | the fallback index announces itself instead of guessing |
    | `tests/test_desktop.py` | 10 | display detection, the preflight checks, `doctor` |
    | `tests/test_cli.py` | 10 | the command line: `--doctor`, `--image`, `--live`, argument handling |
    | `tests/test_live_desktop.py` | 2 | **needs a display, opt-in**: OCR finds a real button at the pixels it was drawn at, and a real click makes the app react |

    ```bash
    pytest tests -q                                  # 123 passed, 2 skipped
    TINY_COMPUTER_AGENT_LIVE_TEST=1 pytest tests -q  # 125 passed, on a desktop
    ```

    ## Install

    ```bash
    pip install -e ".[ocr]"              # labels from pixels (RapidOCR: no system binary)
    pip install -e ".[desktop,ocr]"      # + real capture and real input
    pip install -e ".[vision,ocr]"       # + the tiny model brains
    pytest                               # 125 tests, no display or network needed
    ```

    ## Run it on your machine

    ```bash
    python -m tiny_computer_agent --doctor                 # what can this machine do?
    python -m tiny_computer_agent --image example          # replay a screenshot, dry run
    python -m tiny_computer_agent --live                   # your real screen, dry run
    python -m tiny_computer_agent --live --real-input "click 'OK'"
    python -m tiny_computer_agent --live --real-input --verify-text "Saved" "click 'Save'"
    ```

    `--doctor` is the first thing to run on a new machine: it checks the display session,
    capture backend, pyautogui, the OCR engine, the model runtime and the kill switch, and
    prints the fix for anything missing. Real input needs **two** deliberate acts:

    ```bash
    export TINY_COMPUTER_AGENT_ALLOW_REAL_INPUT=1
    ```

    **Pass `--verify-text`.** Without it, a successful action that removes its own target
    (clicking Save closes the dialog) is indistinguishable from a failure, and the episode is
    recorded as one. With it, the harness only reports success when it can see the text it
    was given.

    ### No desktop? Use a virtual one

    Xvfb is enough for everything except a real model's hallucinations:

    ```bash
    Xvfb :99 -screen 0 1280x800x24 &
    export DISPLAY=:99
    TINY_COMPUTER_AGENT_LIVE_TEST=1 python -m pytest tests/test_live_desktop.py
    ```

    That test starts its own Tk window, asserts OCR finds the button at the coordinates it
    was actually drawn at, clicks it for real, and checks the app reacted. It is opt-in
    because it moves your mouse — run it on a throwaway desktop.

    ### Testing on Windows - three ways, and which to use

    Nothing in the harness is Linux-specific: `pyautogui` uses `SendInput` on Windows and
    `GrabScreen` already falls back to Pillow's `ImageGrab` there. Two things can only fail on
    a real Windows desktop, which is why these runs exist: **DPI scaling** (that is what
    `--scale` is for) and the native input backend. At 125% or 150% display scaling, expect the
    coordinate assertion in `test_live_desktop.py` to be the first thing that fails - that is
    the signal, not a nuisance.

    | Method | Real Windows desktop? | Cost | Status |
    |---|---|---|---|
    | **GitHub Actions `windows-latest`** | yes, an interactive session | free for public repos, ~5 min | workflow committed: `.github/workflows/live-desktop.yml` |
    | **QEMU/TCG VM on a Linux host** | yes, but fully emulated | hours of wall-clock | possible; measured below |
    | **A cloud Windows dev box over RDP** | yes, native | paid by the hour | untested here |

    **1. GitHub Actions - the recommended path.** `.github/workflows/live-desktop.yml` runs
    the unit suite and the live desktop test on `windows-latest`, the same live test under
    Xvfb on Linux, and a `--doctor` preflight on macOS. The Windows job is
    `continue-on-error` on purpose: the first run tells us whether a hosted runner's session
    accepts injected input. Trigger it from the Actions tab. The repo needs a GitHub mirror -
    Hugging Face does not run GitHub Actions.

    **2. A local QEMU VM without KVM.** Measured on the headless container above:

    ```
    /dev/kvm absent                     -> no hardware virtualization
    qemu-system-x86_64 -accel help      -> tcg, kvm
    qemu-system-x86_64 -accel kvm       -> Could not access KVM kernel module: No such file or directory
    qemu-system-x86_64 -accel tcg       -> boots (reached "No bootable device.")
    ```

    So a Windows guest is *possible* with no KVM at all, but entirely under software
    emulation. TCG runs roughly 10-50x slower than native, which turns a 20-minute Windows
    install into many hours - and the harness's OCR would then run on an emulated CPU inside
    the guest. Use this only when no CI runner and no cloud box is available:

    ```bash
    qemu-img create -f qcow2 win.qcow2 64G
    qemu-system-x86_64 -accel tcg -m 8G -smp 4 \
      -drive file=win.qcow2,format=qcow2,if=virtio \
      -cdrom Win11_Eval_x64.iso -boot d \
      -device virtio-vga -vnc :1     # then RDP/VNC in, pip install the harness inside
    ```

    **3. A cloud Windows box.** Any Windows VM reachable over RDP gives a native desktop, a
    real `SendInput` path and real DPI scaling. Untested here; it is the closest thing to
    "a user's actual laptop".

    ## How it works

    | Module | Job |
    |---|---|
    | `actions.py` | The closed vocabulary — `click` `type` `key` `scroll` `wait` `done`, validated before anything executes |
    | `observe.py` | `Element`/`Observation`, `FakeScreen`, `ImageScreen`, `GrabScreen` (mss → Pillow fallback) |
    | `perception.py` | Detectors: `OcrDetector` (RapidOCR), `GridDetector`, `AutoDetector`, `IndexedScreen` |
    | `brain.py` | `DecisionMenu` (the numbered menu), `ModelBrain`, lenient `parse_decision`, echo detection, `MockBrain`, `PlannerBrain` |
    | `models.py` | `VLMBrain` (SmolVLM-256M) and `TextPlannerBrain` (Qwen2.5-0.5B), lazily loaded |
    | `act.py` | `DryRunExecutor` (the default) and `PyAutoGuiExecutor` |
    | `safety.py` | `Policy`: deny lists, kill switch, step budget, the blind-grid-click guard |
    | `loop.py` | `AgentLoop`, `Episode`, `Step`, the no-progress guard and the verifier |
    | `desktop.py` | Preflight checks, `doctor`, and `build_agent` in one call |
    | `demo.py` | The synthetic desktop used by tests and `--image example` |

    ## Why a tiny model can do this at all

    | Job | Who does it |
    |---|---|
    | Finding *where* a button is | OCR / accessibility tree — **not** the model |
    | Choosing *what* to do | the model, but only by picking a number from a short menu |
    | Knowing whether it worked | the verifier and the no-progress guard, not the model's word |
    | Recovering from a bad step | the retry policy in `loop.py`, not the weights |

    A 256M model cannot emit `(295, 284)` reliably, but it can pick "4" out of five options —
    and the harness owns the coordinate lookup, the vocabulary check and the definition of
    success.

    ## Safety, because this thing has your mouse

    - Run it in a **VM, container, or throwaway desktop account**, not your real session.
    - **Dry run is the default**, so you can read a whole episode before anything clicks.
    - Deny lists for typing (`sudo`, `rm -rf`, "password"), widgets (terminal, keychain,
      wallet, bank) and key chords.
    - A **kill-switch file** checked before every action, plus a hard step budget.
    - **Blind grid clicks are refused with real input** unless you pass `--allow-grid`: when
      OCR finds nothing, the fallback index is coordinates with no meaning, and a model
      choosing one is guessing about your desktop.
    - **A brain's "done" is a claim, not evidence**: pass `--verify-text` and the harness
      decides success itself.
    - **Local-first, for real:** pixels and keystrokes never leave the machine.

    ## What works, and what does not

    **Works:** real capture, a real OCR index with coordinates that land on the right pixel,
    a real click that an app reacts to, verified success, the closed vocabulary, the policy
    gates, the no-progress guard, echo detection, the doctor, the CLI, 125 tests — and a
    256M VLM that reads a button label from pixels on a clean screenshot.

    **Does not work yet:** reliability on a *live* screen. The 256M VLM echoed the prompt
    there; the 0.5B text planner picks wrong actions; the accessibility-tree detector is not
    implemented, so labels come from OCR only; and the harness still trusts a model's `done`
    when no verifier is given.

    ## Roadmap

    - [x] Capture, OCR index, closed vocabulary, dry-run default, policy gates, doctor
    - [x] A real click on a real window, with a verified outcome
    - [x] Blind-grid-click guard, echo detection, no-progress guard
    - [ ] Make the VLM reliable on a live screen (it echoes the prompt instead of choosing)
    - [ ] Windows run of the live test - workflow committed; needs a GitHub mirror with Actions on
    - [ ] Accessibility-tree detector (AT-SPI on Linux, UI Automation on Windows)
    - [ ] Score episodes: replay a trace and count how often the choice was right
    - [ ] marimo console: live screen preview, action log, pause / resume / step

    ## License

    MIT — see [LICENSE](LICENSE).
    '''

    mo.md(readme_md)
    return (readme_md,)


@app.cell(hide_code=True)
def repo_files():
    pyproject_toml = '''
    [build-system]
    requires = ["setuptools>=68"]
    build-backend = "setuptools.build_meta"

    [project]
    name = "tiny-computer-agent"
    version = "0.3.0"
    description = "An agentic harness that drives a real computer with a tiny, fully local model - no cloud API."
    readme = "README.md"
    requires-python = ">=3.10"
    license = { text = "MIT" }
    authors = [{ name = "hkblockchaintechnology" }]
    keywords = ["computer-use", "gui-agent", "agentic-harness", "local-first", "tiny-models"]
    classifiers = [
      "Development Status :: 3 - Alpha",
      "Intended Audience :: Developers",
      "Programming Language :: Python :: 3 :: Only",
      "Topic :: Scientific/Engineering :: Artificial Intelligence",
    ]
    dependencies = []   # the harness core imports nothing outside the standard library

    [project.optional-dependencies]
    ocr = ["rapidocr-onnxruntime>=1.3"]        # labels from pixels, no system binary
    desktop = ["mss>=9.0", "pyautogui>=0.9", "pillow>=10"]   # capture + real input
    vision = ["transformers>=4.48", "torch>=2.2", "pillow>=10"]  # tiny model brains
    dev = ["pytest>=8"]

    [project.scripts]
    tiny-computer-agent = "tiny_computer_agent.__main__:main"

    [project.urls]
    Homepage = "https://huggingface.co/hkblockchaintechnology/tiny-computer-agent"

    [tool.setuptools]
    packages = ["tiny_computer_agent"]

    [tool.pytest.ini_options]
    testpaths = ["tests"]
    '''

    license_txt = '''MIT License

    Copyright (c) 2026 hkblockchaintechnology

    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:

    The above copyright notice and this permission notice shall be included in all
    copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
    SOFTWARE.
    '''

    gitignore_txt = '''__pycache__/
    *.py[cod]
    .venv/
    venv/
    .env
    *.gguf
    models/
    runs/
    traces/
    .ipynb_checkpoints/
    .DS_Store
    '''

    mo.md(f"packaging: {len(pyproject_toml) + len(license_txt) + len(gitignore_txt)} chars, "
          f"v0.2.0 with ocr/desktop/vision extras")
    return gitignore_txt, license_txt, pyproject_toml


@app.cell
def publish():
    REPO_ID = "hkblockchaintechnology/tiny-computer-agent"

    publish_button = mo.ui.button(
        label="⬆️ Publish README.md to the Hub",
        value=0,
        on_click=lambda value: value + 1,
    )

    publish_button
    return REPO_ID, publish_button


@app.cell
def publish_status(
    REPO_ID,
    gitignore_txt,
    license_txt,
    publish_button,
    pyproject_toml,
    readme_md,
):
    _status = mo.md(
        "This notebook owns the project: the card, the packaging files, the package and "
        "the tests. Click **Publish** to push all of it to the Hub."
    ).callout(kind="info")

    if publish_button.value:
        _token = os.environ.get("HF_TOKEN")
        if not _token:
            _status = mo.md(
                "No `HF_TOKEN` in the environment. Run `export HF_TOKEN=...` "
                "(or `hf auth login`), then restart the kernel."
            ).callout(kind="warn")
        else:
            _api = HfApi(token=_token)
            _root = Path(__file__).parent
            _notebook = Path(__file__)
            _files = {
                "README.md": readme_md,
                "pyproject.toml": pyproject_toml,
                "LICENSE": license_txt,
                ".gitignore": gitignore_txt,
                "tiny_computer_agent_app.py": _notebook.read_text(encoding="utf-8"),
            }
            _folders = ["tiny_computer_agent", "tests"]
            if (_root / ".github").is_dir():
                _folders.append(".github")          # the CI that tests on real Windows
            _missing = [f for f in ("tiny_computer_agent", "tests") if not (_root / f).is_dir()]
            if _missing:
                _status = mo.md(f"Nothing to publish: missing {_missing} next to `{_root}`").callout(kind="danger")
            else:
                try:
                    for _name, _text in _files.items():
                        _api.upload_file(
                            path_or_fileobj=_text.encode("utf-8"),
                            path_in_repo=_name,
                            repo_id=REPO_ID,
                            repo_type="model",
                            commit_message=f"Publish {_name} from marimo",
                        )
                    for _folder in _folders:
                        _api.upload_folder(
                            folder_path=str(_root / _folder),
                            path_in_repo=_folder,
                            repo_id=REPO_ID,
                            repo_type="model",
                            commit_message=f"Publish {_folder}/ from marimo",
                            ignore_patterns=["__pycache__/*", "*.pyc"],
                        )
                    _status = mo.md(
                        f"✅ Published {len(_files)} files and {len(_folders)} folders to "
                        f"[{REPO_ID}](https://huggingface.co/{REPO_ID})"
                    ).callout(kind="success")
                except Exception as _e:
                    _status = mo.md(f"Publish failed: `{type(_e).__name__}: {_e}`").callout(kind="danger")

    _status
    return


@app.cell
def demo():
    from tiny_computer_agent import AgentLoop, DryRunExecutor, PlannerBrain, Policy
    from tiny_computer_agent.demo import fake_desktop
    from tiny_computer_agent.observe import ImageScreen
    from tiny_computer_agent.perception import IndexedScreen, OcrDetector

    # A real screenshot -> real OCR -> labelled, clickable elements. No display needed:
    # this is the same path --image takes, and it is what makes a tiny model viable.
    _detector = OcrDetector()
    _observation = IndexedScreen(ImageScreen(fake_desktop()), _detector).capture()

    perception_rows = [
        {"label": e.label, "kind": e.kind, "x": e.x, "y": e.y, "w": e.width, "h": e.height}
        for e in _observation.elements
    ]

    perception_episode = AgentLoop(
        screen=IndexedScreen(ImageScreen(fake_desktop()), _detector),
        brain=PlannerBrain(),
        executor=DryRunExecutor(),
        policy=Policy(step_budget=4),
    ).run("click \'Save\' to save the document")

    _clicked = next((s.result.note for s in perception_episode.steps if s.action and s.action.kind == "click"), "n/a")

    mo.vstack(
        [
            mo.md(
                f"**Perception, from pixels.** OCR read **{len(perception_rows)} labels** off a "
                f"synthetic screenshot; the planner then chose one and the harness resolved it to a real pixel."
            ),
            mo.ui.table(perception_rows, selection=None),
            mo.md(f"`{_clicked}`"),
            mo.md(f"`{perception_episode.render().splitlines()[1]}`"),
        ]
    )
    return


if __name__ == "__main__":
    app.run()