"""
Post-processing — applies final cleanup to provider results before
they enter normalization.

Examples:
  - de-duplicate scraped image URLs
  - clamp bounding boxes to image bounds
  - strip PII from raw responses (placeholder for future policy hooks)
"""

from __future__ import annotations

from typing import Any, List

from loguru import logger


class ResultPostprocessor:
    """Cleans provider outputs before normalization."""

    @staticmethod
    def dedupe_images(images: List[dict]) -> List[dict]:
        """Remove duplicate image URLs."""
        seen: set[str] = set()
        out: List[dict] = []
        for img in images:
            url = img.get("url") or img.get("image_url")
            if not url or url in seen:
                continue
            seen.add(url)
            out.append(img)
        return out

    @staticmethod
    def clamp_boxes(boxes: List[dict], width: int, height: int) -> List[dict]:
        """Clamp bounding boxes to image bounds."""
        out: List[dict] = []
        for b in boxes:
            x = max(0, min(b["x"], width - 1))
            y = max(0, min(b["y"], height - 1))
            x2 = max(0, min(b["x"] + b["w"], width))
            y2 = max(0, min(b["y"] + b["h"], height))
            out.append({"x": x, "y": y, "w": max(0, x2 - x), "h": max(0, y2 - y)})
        return out

    @staticmethod
    def filter_low_confidence(boxes: List[dict], confs: List[float],
                              threshold: float = 0.5) -> tuple[List[dict], List[float]]:
        """Drop detections below confidence threshold."""
        out_boxes, out_confs = [], []
        for b, c in zip(boxes, confs):
            if c >= threshold:
                out_boxes.append(b)
                out_confs.append(c)
        return out_boxes, out_confs