"""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

    # Vehicle stage (phase A — venue-modules.md §Vehicle category from vision): a YOLOX
    # ONNX graph (Apache-2.0) run beside the plate recognizer. Unset = stage off (the
    # response's `vehicle` stays null). Bake the file into the image (models/), never a
    # path an operator can write (vision-service-hardening.md).
    vehicle_model_path: str | None = None
    vehicle_input_size: int = 640
    # Detection score floor for a vehicle box to count at all (the Node side applies the
    # site's own, stricter threshold before it FLAGS anything).
    vehicle_min_confidence: float = 0.4

    # Phase B — the body-type classifier on the detector's crop (bodytype.onnx + its .json
    # sidecar, produced by apps/trainer, baked into the image when
    # models/bodytype.version pins a published version). Path set but NO file = the normal
    # state before the first model ships: the stage is simply off (logged, not an error).
    vehicle_classifier_path: str | None = None
    # Below this probability the classifier's answer is dropped and the detector's stands.
    vehicle_classifier_min_confidence: float = 0.6


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