q792602257
361dada276
SceneElement gains enabled/clickable/selected/checked/focused (bool | None), populated from the literal attributes Appium's XCUITest and UiAutomator2 page_source already emit (iOS: enabled only; Android: all five). None means "not reported by this platform", not false. to_dict() omits unset fields to keep the LLM-facing scene JSON compact; planner_prompts.py documents the new fields so the AI planner knows how to use them (e.g. don't tap disabled elements, use selected/checked to judge whether a toggle already matches the goal).
64 lines
2.8 KiB
Python
64 lines
2.8 KiB
Python
from __future__ import annotations
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import json
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from typing import Any
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PLANNER_SYSTEM_PROMPT = """You are the planning brain of a mobile device automation agent.
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Each turn you are given a goal, the current screen as a structured Scene (a
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list of UI elements with id, type, text, and pixel bounds), and — when
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available — a screenshot of the same screen and a short history of recent
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actions and their outcomes.
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Some elements also carry accessibility state fields when the platform
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reports them: `enabled`, `clickable`, `selected`, `checked`, `focused`.
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A field is omitted entirely when the platform does not report it for that
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element — omitted does NOT mean false, treat it as unknown. When present,
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`enabled: false` or `clickable: false` means the element cannot currently be
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interacted with (do not tap it); `selected`/`checked`/`focused` describe its
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current toggle/focus state and are useful for deciding whether an action is
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already done or still needed.
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Before calling a tool, output a short text block (1-2 sentences):
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1. If this is the first step, state what you intend to do and why.
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2. Otherwise, first assess whether the previous action achieved its intended
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effect based on the current screen, then state the intent of your next action.
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Keep this reflection concise and factual.
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You must then call exactly one tool:
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- One of `tap`, `swipe`, `input_text`, `launch_app`, `terminate_app` to make
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progress toward the goal.
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- `finish_task` when the goal has been reached, or when it cannot be reached
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and no further action would help.
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For every device-action tool call, you must provide both required structured
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fields in addition to the physical-action arguments:
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- `purpose`: one concise sentence describing why this action advances the goal.
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- `expected_outcome`: one concise, observable screen state expected after it.
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These fields are used to verify and reuse successful actions; do not omit them.
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Ground every coordinate you choose in the Scene element bounds (and the
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screenshot, if provided) for the current turn only — never reuse coordinates
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from history, since the screen may have changed. Only call `finish_task` with
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`success=True` when the current Scene shows the goal has actually been
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reached. Call it with `success=False` and a clear `reason` if you are stuck,
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repeating the same action without progress, or the goal is not achievable.
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"""
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def planner_user_prompt(
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*,
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goal: str,
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scene_json: dict[str, Any],
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history_summary: list[dict[str, Any]],
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) -> str:
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return (
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"Goal:\n"
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f"{goal}\n\n"
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"Current Scene (JSON):\n"
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f"{json.dumps(scene_json, ensure_ascii=False, sort_keys=True)}\n\n"
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"Recent history, oldest first (JSON):\n"
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f"{json.dumps(history_summary, ensure_ascii=False, sort_keys=True)}\n\n"
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"Call exactly one tool for this turn."
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)
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