#!/usr/bin/env python3
"""Render a diagnostic first-stage preview from source evidence."""

from __future__ import annotations

import json
from pathlib import Path
from typing import Any

from PIL import Image, ImageDraw, ImageFont


PROJECT = Path(__file__).resolve().parents[1]
SOURCE_NODES = PROJECT / "source_export/source_nodes.json"
OUT_DIR = PROJECT / "artifacts/canvas_preview"


def read_json(path: Path) -> Any:
    with path.open("r", encoding="utf-8") as fh:
        return json.load(fh)


def cocos_to_image(point: dict[str, float], width: int, height: int) -> tuple[float, float]:
    return width / 2 + float(point.get("x", 0)), height / 2 - float(point.get("y", 0))


def draw_centered_rect(draw: ImageDraw.ImageDraw, node: dict[str, Any], canvas: tuple[int, int], outline: str, fill: str | None = None) -> None:
    pos = node.get("world_position_transformed") or node.get("position") or {"x": 0, "y": 0}
    size = node.get("primary_size") or node.get("content_size") or {"width": 40, "height": 40}
    cx, cy = cocos_to_image(pos, canvas[0], canvas[1])
    w = float(size.get("width") or 40)
    h = float(size.get("height") or 40)
    box = [cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2]
    draw.rectangle(box, outline=outline, fill=fill, width=2)


def draw_land_cell(draw: ImageDraw.ImageDraw, node: dict[str, Any], canvas: tuple[int, int]) -> None:
    pos = node.get("world_position_transformed") or {"x": 0, "y": 0}
    cx, cy = cocos_to_image(pos, canvas[0], canvas[1])
    size = node.get("primary_size") or {"width": 130, "height": 130}
    w = float(size.get("width") or 130)
    h = float(size.get("height") or 130) * 0.5
    pts = [(cx, cy - h / 2), (cx + w / 2, cy), (cx, cy + h / 2), (cx - w / 2, cy)]
    draw.polygon(pts, outline="#2f7d32", fill="#86bf6a")


def main() -> None:
    data = read_json(SOURCE_NODES)
    width = int(data["design_resolution"]["width"])
    height = int(data["design_resolution"]["height"])
    img = Image.new("RGB", (width, height), "#d7efff")
    draw = ImageDraw.Draw(img, "RGBA")

    for node in data["background_nodes"]:
        draw_centered_rect(draw, node, (width, height), "#7a9f4b", "#b7d77a55")

    for node in data["land_candidates"]["nodes"]:
        draw_land_cell(draw, node, (width, height))

    for node in data["house_and_doghouse"]["doghouse_nodes"]:
        draw_centered_rect(draw, node, (width, height), "#7a3f16", "#b8753555")

    for record in data["house_and_doghouse"]["skin_cfg_default_records"]:
        pos = record.get("position") or {"x": 0, "y": 0}
        cx, cy = cocos_to_image(pos, width, height)
        label = str(record.get("skin_name", record.get("id")))
        draw.rectangle([cx - 55, cy - 35, cx + 55, cy + 35], outline="#5c2d91", fill="#c7a5ff66", width=2)
        draw.text((cx - 48, cy - 8), label, fill="#2b1545")

    for node in data["bottom_menu_nodes"]:
        draw_centered_rect(draw, node, (width, height), "#1f4f99", "#6fa8ff55")

    draw.rectangle([0, 0, width - 1, height - 1], outline="#222222", width=2)
    draw.text((12, 12), "Stage1 diagnostic preview - source evidence only", fill="#111111")
    draw.text((12, 34), f"land candidates: {data['land_candidates']['source_count']} / visible 24 blocked", fill="#8a1f11")

    OUT_DIR.mkdir(parents=True, exist_ok=True)
    png = OUT_DIR / "stage1_source_preview.png"
    report = OUT_DIR / "stage1_source_preview_report.json"
    img.save(png)
    report.write_text(json.dumps({
        "status": "generated",
        "image": str(png),
        "resolution": {"width": width, "height": height},
        "land_candidates_rendered": data["land_candidates"]["source_count"],
        "visible_land_selection": data["land_candidates"]["selection_status"],
        "note": "Diagnostic rendering only; it does not define final Cocos coordinates.",
    }, indent=2) + "\n", encoding="utf-8")
    print(png)


if __name__ == "__main__":
    main()