"""Runtime configuration, from environment (prefix VISION_).

Offline-first: every default is local and works with no network. The recognizer is
chosen by `recognizer` — "stub" (no models, deterministic placeholder) or "fast_alpr"
(the real MIT YOLOv9+CCT/ONNX stack, installed via the `alpr` extra).
"""

from __future__ import annotations

from typing import Literal

from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    model_config = SettingsConfigDict(env_prefix="VISION_", env_file=".env", extra="ignore")

    host: str = "0.0.0.0"
    port: int = 8089

    # Which recognizer to load. "stub" needs no model weights (boots anywhere, for
    # dev/CI); "fast_alpr" loads the real models (requires the `alpr` extra installed).
    recognizer: Literal["stub", "fast_alpr"] = "stub"

    # fast-alpr model names (only used when recognizer="fast_alpr"). Defaults match the
    # library defaults; swap the OCR for the 40+country EU model to benchmark Albanian
    # plates. See wiki/entities/opencv-anpr-service.md "Recognizer evaluation".
    detector_model: str = "yolo-v9-t-384-license-plate-end2end"
    ocr_model: str = "cct-xs-v2-global-model"

    # Below this OCR confidence the read is returned but flagged low_confidence, so the
    # Node side can fall back to the ticket path rather than trust it.
    min_confidence: float = 0.5


def get_settings() -> Settings:
    return Settings()