# utils/click.py — Click detection: nearest leg, nearest city, coordinate extraction
import math
from typing import Optional, Tuple

import gradio as gr

from config import get_style, DEFAULT_STYLE
from core.geometry import control_point, sample_curve
from models.city import DB


def _extract_xy(evt: gr.SelectData) -> Tuple[Optional[float], Optional[float], Optional[any]]:
    """Extract (x, y, size) in base image pixels from Gradio click event."""
    if evt is None:
        return None, None, None
    x = getattr(evt, "x", None)
    y = getattr(evt, "y", None)
    size = getattr(evt, "size", None)
    if x is not None and y is not None:
        return float(x), float(y), size
    idx = getattr(evt, "index", None)
    if isinstance(idx, (list, tuple)) and len(idx) >= 2:
        return float(idx[0]), float(idx[1]), size
    val = getattr(evt, "value", None)
    if isinstance(val, dict) and ("x" in val) and ("y" in val):
        w = val.get("width")
        h = val.get("height")
        if (w is None or h is None) and isinstance(val.get("image"), dict):
            w = val["image"].get("width")
            h = val["image"].get("height")
        return float(val["x"]), float(val["y"]), (w, h)
    return None, None, None


def _nearest_leg_on_click(x, y, legs, to_xy, bulge_overrides=None, n=220):
    best = None
    _default_bulge = get_style(DEFAULT_STYLE).curve_bulge
    for idx, e in enumerate(legs or []):
        a, b = e.get("a"), e.get("b")
        if a not in DB.by_name or b not in DB.by_name:
            continue
        bulge = float((bulge_overrides or {}).get((a, b), _default_bulge))
        p0 = to_xy(a)
        p2 = to_xy(b)
        pc = control_point(p0, p2, bulge)
        pts = sample_curve(p0, pc, p2, n=n)
        best_d = 1e18
        best_i = 0
        for i, (xx, yy) in enumerate(pts):
            d = (xx - x) ** 2 + (yy - y) ** 2
            if d < best_d:
                best_d = d
                best_i = i
        d_px = math.sqrt(best_d)
        t = best_i / (n - 1)
        if (best is None) or (d_px < best[0]):
            best = (d_px, idx, t)
    return best


def _nearest_city_on_click(x, y, to_xy, city_names):
    best_nm, best_d = None, 1e18
    for nm in city_names:
        cx, cy = to_xy(nm)
        d = math.hypot(x - cx, y - cy)
        if d < best_d:
            best_d, best_nm = d, nm
    return best_nm, best_d