"""Agent config, all from env (the control container is configured at run)."""
import os
from dataclasses import dataclass


@dataclass
class Config:
    fc_url: str          # base URL of the FabledCurator web service
    token: str           # the bearer token from Settings → Tagging → GPU agent
    agent_id: str        # identifies this agent's leases
    batch_size: int      # jobs a worker leases per round
    concurrency: int     # INITIAL parallel workers (tunable live from the UI)
    ccip_model: str      # imgutils CCIP model name ("" → imgutils default)
    detector_level: str  # imgutils person-detector level: n|s|m|x
    poll_idle_seconds: float   # wait between empty leases
    embed_dtype: str     # torch dtype for the crop embedder: float16|float32
    embed_model_override: str  # force a SigLIP-family model ("" → use the one
    #                            the server announces in the lease)
    auto_start: bool     # start the worker pool on boot (so a container restart
    #                      resumes processing without anyone clicking Start)
    auto_scale: bool     # autoscale the worker count (throughput hill-climb)
    # Crop PROPOSERS (extra YOLO detectors that say where to crop). Each weight
    # spec is an ultralytics name | http(s) URL | "hf_repo::file" ("" = off).
    person_weights: str  # general COCO person detector (Western/realistic figs)
    person_conf: float
    anatomy_weights: str  # booru_yolo anime/furry/NSFW components
    anatomy_conf: float
    panel_weights: str    # comic-panel detector
    panel_conf: float
    max_components: int   # cap anatomy component crops per frame
    max_panels: int       # cap panel crops per frame

    @classmethod
    def from_env(cls) -> Config:
        return cls(
            fc_url=os.environ.get("FC_URL", "http://localhost:8000").rstrip("/"),
            token=os.environ.get("FC_TOKEN", ""),
            agent_id=os.environ.get("AGENT_ID", "desktop-agent"),
            batch_size=int(os.environ.get("BATCH_SIZE", "4")),
            concurrency=int(os.environ.get("CONCURRENCY", "1")),
            ccip_model=os.environ.get("CCIP_MODEL", ""),
            detector_level=os.environ.get("DETECTOR_LEVEL", "m"),
            poll_idle_seconds=float(os.environ.get("POLL_IDLE_SECONDS", "10")),
            embed_dtype=os.environ.get("SIGLIP_DTYPE", "float16"),
            embed_model_override=os.environ.get("EMBED_MODEL_NAME", ""),
            auto_start=os.environ.get("AUTO_START", "").lower() in ("1", "true", "yes"),
            auto_scale=os.environ.get("AUTO_SCALE", "true").lower() in ("1", "true", "yes"),
            person_weights=os.environ.get("PERSON_WEIGHTS", "yolo11n.pt"),
            person_conf=float(os.environ.get("PERSON_CONF", "0.35")),
            anatomy_weights=os.environ.get("ANATOMY_WEIGHTS", ""),
            anatomy_conf=float(os.environ.get("ANATOMY_CONF", "0.30")),
            panel_weights=os.environ.get("PANEL_WEIGHTS", ""),
            panel_conf=float(os.environ.get("PANEL_CONF", "0.30")),
            max_components=int(os.environ.get("MAX_COMPONENTS", "8")),
            max_panels=int(os.environ.get("MAX_PANELS", "8")),
        )