from dataclasses import dataclass
from typing import Any, Dict, Sequence

from .clickthrough_skill import ActionPlan, ClickthroughSkill


@dataclass
class AgentRunResult:
    summary: Dict[str, Any]
    action: Dict[str, Any]
    history: Dict[str, Any]
    grid: Dict[str, Any]


class ClickthroughAgentRunner:
    def __init__(self, skill: ClickthroughSkill) -> None:
        self.skill = skill

    def run_once(
        self,
        screenshot_base64: str,
        width: int,
        height: int,
        rows: int = 4,
        columns: int = 4,
        preferred_label: str | None = None,
        action: str = "click",
        text: str | None = None,
    ) -> AgentRunResult:
        grid = self.skill.describe_grid(
            screenshot_base64=screenshot_base64,
            width=width,
            height=height,
            rows=rows,
            columns=columns,
        )
        cells = grid.get("cells") or []
        target_cell = self._choose_cell(cells, preferred_label)
        plan = ActionPlan(
            grid_id=grid["grid_id"],
            target_cell=target_cell,
            action=action,
            text=text,
        )
        action_result = self.skill.plan_action(plan)
        summary = self.skill.grid_summary(grid["grid_id"])
        history = self.skill.grid_history(grid["grid_id"])
        return AgentRunResult(summary=summary, action=action_result, history=history, grid=grid)

    def _choose_cell(
        self, cells: Sequence[dict[str, Any]], preferred_label: str | None
    ) -> str:
        if not cells:
            raise ValueError("Grid contains no cells")
        if preferred_label:
            search = preferred_label.lower()
            for cell in cells:
                label_value = cell.get("label")
                if label_value and search in label_value.lower():
                    return cell["cell_id"]
        center_index = len(cells) // 2
        return cells[center_index]["cell_id"]