| from __future__ import annotations | |
| from pathlib import Path | |
| from typing import Any | |
| from .runtime.model import LoadedUIActionPolicy | |
| from .schema import UIAction, UIElement | |
| import os | |
| DEFAULT_CHECKPOINT = Path(os.environ.get("LEO_UI7M_CHECKPOINT", Path(__file__).resolve().parents[1] / "checkpoints" / "leo_ui7m_v4_best.pt")) | |
| class UIActionModel: | |
| """LEO-UI7M local inference API.""" | |
| def __init__(self, checkpoint_path: str | Path | None = None, device: str = "cpu") -> None: | |
| self.checkpoint_path = Path(checkpoint_path) if checkpoint_path else DEFAULT_CHECKPOINT | |
| self.device = device | |
| self.policy = LoadedUIActionPolicy(self.checkpoint_path, device=device) | |
| def predict_action( | |
| self, | |
| goal: str, | |
| elements: list[UIElement | dict[str, Any]], | |
| history: list[dict[str, Any]] | None = None, | |
| step_index: int = 0, | |
| ) -> UIAction: | |
| normalized = [ | |
| e if isinstance(e, dict) else e.__dict__ | |
| for e in elements | |
| ] | |
| raw = self.policy.predict_raw( | |
| goal=goal, | |
| elements=normalized, | |
| history=history or [], | |
| step_index=step_index, | |
| ) | |
| return UIAction( | |
| action=raw["action"], | |
| target_element_id=raw.get("target_element_id"), | |
| confidence=raw.get("confidence"), | |
| raw=raw, | |
| ) | |