import json
from pathlib import Path

from PIL import Image, ImageChops, ImageFilter, ImageStat

reference_path = Path("reference.png")
desktop_path = Path("actual-desktop.png")
mobile_path = Path("actual-mobile.png")
metadata_path = Path("render-metadata.json")
for path in (reference_path, desktop_path, mobile_path, metadata_path):
    if not path.is_file():
        raise SystemExit(f"missing real visual artifact: {path}")

metadata = json.loads(metadata_path.read_text(encoding="utf-8"))
if metadata.get("engine") != "playwright":
    raise SystemExit("visual render was not captured through Playwright")

reference = Image.open(reference_path).convert("RGB")
desktop = Image.open(desktop_path).convert("RGB")
mobile = Image.open(mobile_path).convert("RGB")
if desktop.size != (1440, 900) or mobile.size != (390, 844):
    raise SystemExit("browser screenshots do not match the required viewports")

resampling = getattr(Image, "Resampling", Image).LANCZOS
normalized_reference = reference.resize(desktop.size, resampling)
difference = ImageChops.difference(normalized_reference, desktop)
mean_error = sum(ImageStat.Stat(difference).mean) / (3 * 255)
perceptual_similarity = max(0.0, 1.0 - mean_error)
histogram = difference.convert("L").histogram()
matched_pixels = sum(histogram[:32])
pixel_similarity = matched_pixels / (desktop.width * desktop.height)

reference_edges = normalized_reference.convert("L").filter(ImageFilter.FIND_EDGES)
desktop_edges = desktop.convert("L").filter(ImageFilter.FIND_EDGES)
edge_error = ImageStat.Stat(ImageChops.difference(reference_edges, desktop_edges)).mean[0] / 255
edge_similarity = max(0.0, 1.0 - edge_error)

viewport_facts = metadata["viewports"]
responsive_checks = [
    not viewport_facts["desktop"]["horizontalOverflow"],
    not viewport_facts["mobile"]["horizontalOverflow"],
    viewport_facts["mobile"]["bodyWidth"] <= 391,
]
responsive_score = sum(responsive_checks) / len(responsive_checks)
desktop_facts = viewport_facts["desktop"]
accessibility_checks = [
    desktop_facts["language"] == "en",
    desktop_facts["mainCount"] == 1,
    desktop_facts["navCount"] >= 1,
    desktop_facts["headingCount"] >= 2,
    desktop_facts["labeledCount"] >= 1,
    desktop_facts["focusableCount"] >= 1,
    desktop_facts["unlabeledImages"] == 0,
]
accessibility_score = sum(accessibility_checks) / len(accessibility_checks)

metrics = {
    "perceptual_similarity": perceptual_similarity,
    "pixel_similarity": pixel_similarity,
    "edge_similarity": edge_similarity,
    "responsive_score": responsive_score,
    "accessibility_score": accessibility_score,
}
default_thresholds = {
    "perceptual_similarity": 0.72,
    "pixel_similarity": 0.35,
    "edge_similarity": 0.72,
    "responsive_score": 1.0,
    "accessibility_score": 1.0,
}
holdout = Path("visual-holdout.json")
thresholds = (
    json.loads(holdout.read_text(encoding="utf-8")) if holdout.is_file() else default_thresholds
)
if set(thresholds) != set(default_thresholds):
    raise SystemExit("visual holdout threshold schema is invalid")
failures = [name for name, threshold in thresholds.items() if metrics[name] < threshold]
if failures:
    raise SystemExit(
        "visual gates failed: " + ", ".join(f"{name}={metrics[name]:.3f}" for name in failures)
    )
print(json.dumps({"metrics": metrics}, sort_keys=True))