"""Vision LLM OCR for red circle UID reading.

Instead of using EasyOCR for each circle individually,
batches multiple circle crops into a single vision API call
for faster and more accurate UID reading.
"""

from __future__ import annotations

import base64
import json
import logging
import re
import urllib.error
import urllib.request
from typing import Any

import cv2
import numpy as np

logger = logging.getLogger(__name__)

_BATCH_SIZE = 30  # circles per API call


def _crop_circle(image_bgr: np.ndarray, box: tuple[int, int, int, int], pad: int = 5) -> np.ndarray:
    x, y, w, h = box
    y0 = max(0, y - pad)
    x0 = max(0, x - pad)
    y1 = min(image_bgr.shape[0], y + h + pad)
    x1 = min(image_bgr.shape[1], x + w + pad)
    return image_bgr[y0:y1, x0:x1]


def _build_grid_image(crops: list[np.ndarray], cols: int = 6) -> tuple[np.ndarray, list[tuple[int, int]]]:
    """Arrange circle crops into a grid image with labels for batch OCR."""
    if not crops:
        return np.zeros((1, 1, 3), dtype=np.uint8), []

    cell_w = max(c.shape[1] for c in crops) + 4
    cell_h = max(c.shape[0] for c in crops) + 20  # extra space for index label
    rows = (len(crops) + cols - 1) // cols

    grid = np.full((rows * cell_h, cols * cell_w, 3), 255, dtype=np.uint8)
    positions: list[tuple[int, int]] = []

    for i, crop in enumerate(crops):
        r, c = divmod(i, cols)
        gx = c * cell_w + 2
        gy = r * cell_h + 16  # leave space for index
        ch, cw = crop.shape[:2]
        grid[gy:gy + ch, gx:gx + cw] = crop
        # Draw index number above
        cv2.putText(grid, f"#{i + 1}", (gx, gy - 4), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 0), 1)
        positions.append((gx, gy))

    return grid, positions


def _encode_image(image: np.ndarray) -> str:
    success, buf = cv2.imencode(".png", image)
    if not success:
        raise ValueError("Failed to encode image")
    return base64.b64encode(buf.tobytes()).decode("ascii")


def batch_ocr_circles(
    image_bgr: np.ndarray,
    circle_boxes: list[tuple[int, int, int, int]],
    *,
    api_url: str,
    api_key: str = "",
    model: str = "",
) -> list[str]:
    """Read UID numbers from red circles using vision LLM.

    Batches circles into grid images and sends to vision API.
    Returns list of UID strings (same length as circle_boxes,
    empty string if not readable).
    """
    if not circle_boxes or not api_url:
        return [""] * len(circle_boxes)

    results: list[str] = [""] * len(circle_boxes)

    for batch_start in range(0, len(circle_boxes), _BATCH_SIZE):
        batch_end = min(batch_start + _BATCH_SIZE, len(circle_boxes))
        batch_boxes = circle_boxes[batch_start:batch_end]
        batch_crops = [_crop_circle(image_bgr, box) for box in batch_boxes]

        grid, _ = _build_grid_image(batch_crops)
        b64 = _encode_image(grid)
        n = len(batch_crops)

        prompt = (
            f"На изображении {n} красных кружков с цифрами внутри, пронумерованных #1..#{n}.\n"
            f"Для каждого кружка прочитай чёрное число внутри.\n"
            f"Верни ТОЛЬКО JSON массив из {n} строк, например: [\"42\", \"7\", \"103\", ...]\n"
            f"Если число не читается — пустая строка \"\".\n"
            f"Верни ровно {n} элементов."
        )

        payload = {
            "model": model,
            "messages": [{
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}},
                ],
            }],
            "max_tokens": 2048,
            "temperature": 0,
        }

        headers = {"Content-Type": "application/json"}
        if api_key:
            headers["Authorization"] = f"Bearer {api_key}"

        endpoint = api_url.rstrip("/") + "/chat/completions"
        body = json.dumps(payload).encode("utf-8")
        req = urllib.request.Request(endpoint, data=body, headers=headers, method="POST")

        try:
            with urllib.request.urlopen(req, timeout=60) as resp:
                raw = json.loads(resp.read().decode("utf-8"))
            content = str(raw.get("choices", [{}])[0].get("message", {}).get("content", ""))

            # Parse JSON array from response
            arr_match = re.search(r"\[.*\]", content, re.DOTALL)
            if arr_match:
                uids = json.loads(arr_match.group())
                for i, uid_val in enumerate(uids[:n]):
                    idx = batch_start + i
                    digits = "".join(c for c in str(uid_val) if c.isdigit())
                    if digits and len(digits) <= 4:
                        results[idx] = digits.lstrip("0") or digits

            logger.info("Vision batch OCR: %d/%d circles read (batch %d-%d)",
                        sum(1 for r in results[batch_start:batch_end] if r), n,
                        batch_start + 1, batch_end)

        except Exception as exc:
            logger.warning("Vision batch OCR failed for batch %d-%d: %s",
                           batch_start + 1, batch_end, exc)

    return results