"""Dev CLI to test a recognizer against an image file — no HTTP, fast feedback.

    uv run python -m vision_service.cli path/to/car.jpg
    uv run python -m vision_service.cli car.jpg --recognizer stub        # contract only
    uv run python -m vision_service.cli car.jpg --ocr cct-s-v2-global-model

Defaults to the `fast_alpr` recognizer (the point of this tool). Prints the parsed
plate result as JSON. If the `alpr` extra isn't installed it says so and exits non-zero
rather than silently using the stub. See wiki/entities/opencv-anpr-service.md.
"""

from __future__ import annotations

import argparse
import sys
from pathlib import Path

from .recognizer import build_recognizer
from .settings import Settings


def main(argv: list[str] | None = None) -> int:
    parser = argparse.ArgumentParser(prog="vision-recognize", description="Run a recognizer on an image.")
    parser.add_argument("image", type=Path, help="path to an image file (JPEG/PNG) with a plate")
    parser.add_argument(
        "--recognizer",
        choices=["fast_alpr", "stub"],
        default="fast_alpr",
        help="which recognizer to use (default: fast_alpr)",
    )
    parser.add_argument("--detector", default=None, help="override the fast-alpr detector model name")
    parser.add_argument("--ocr", default=None, help="override the fast-alpr OCR model name")
    args = parser.parse_args(argv)

    if not args.image.is_file():
        print(f"error: no such file: {args.image}", file=sys.stderr)
        return 2

    settings = Settings(recognizer=args.recognizer)
    if args.detector:
        settings.detector_model = args.detector
    if args.ocr:
        settings.ocr_model = args.ocr

    rec = build_recognizer(settings)
    if not rec.ready:
        err = getattr(rec, "error", "unavailable")
        print(
            f"error: recognizer '{args.recognizer}' not ready: {err}\n"
            "hint: install the models with  uv sync --extra alpr",
            file=sys.stderr,
        )
        return 1

    image_bytes = args.image.read_bytes()
    result = rec.analyze(image_bytes)
    # Pydantic v2: model_dump_json gives a clean, stable rendering.
    print(result.model_dump_json(indent=2))

    if result.plate is None:
        print("\n(no plate detected)", file=sys.stderr)
    else:
        flag = "  [LOW CONFIDENCE]" if result.low_confidence else ""
        print(
            f"\n→ {result.plate.text}  ({result.plate.confidence:.3f}){flag}  in {result.took_ms:.1f} ms",
            file=sys.stderr,
        )
    return 0


if __name__ == "__main__":
    raise SystemExit(main())