"""The ONE entrypoint a tool author edits.

`process(image_path, engine, diameter)` segments cells/nuclei and returns an
overlay summary image + a report. `simulate_full` also returns the raw result.
"""
from __future__ import annotations

import os

import numpy as np

from . import fast_seg, viz
from .io import load_image

ENGINES = ["fast", "cellpose"]


def simulate_full(image_path: str, engine: str = "fast", diameter: float = 0.0) -> dict:
    if engine not in ENGINES:
        raise ValueError(f"Unknown engine '{engine}'. Choose one of {ENGINES}.")
    img = load_image(image_path)

    # Use ground-truth labels if a sidecar exists (the baked example) to score.
    gt = None
    side = os.path.splitext(str(image_path))[0] + "_labels.npy"
    if os.path.exists(side):
        try:
            gt = np.load(side)
        except Exception:  # noqa: BLE001
            gt = None

    if engine == "cellpose":
        from . import cellpose_engine
        res = cellpose_engine.analyze(img, diameter=float(diameter), gt=gt)
    else:
        md = int(diameter / 2) if diameter else 8
        res = fast_seg.analyze(img, min_distance=max(3, md), gt=gt)

    return {"summary": viz.summary_image(res), "report": res["report"], "res": res}


def process(image_path: str, engine: str = "fast", diameter: float = 0.0) -> tuple[np.ndarray, dict]:
    """Returns (segmentation overlay RGB, report dict)."""
    r = simulate_full(image_path, engine, diameter)
    return r["summary"], r["report"]