"""OCR helper for part B. Wraps the local `tesseract` binary via pytesseract.

Deterministic on identical pixels; no network. We drew the overlay text
ourselves (clean, high-contrast), so tesseract reads it back reliably.
"""

from __future__ import annotations

import re

import pytesseract
from PIL import Image


def read_text(image: Image.Image) -> str:
    """Return the raw OCR text of ``image``.

    Convert to luminance ("L") first: tesseract binarizes internally, and on the
    raw RGB composite (colored background + light-on-dark text bands) its global
    threshold drops every glyph. A single-channel luminance image gives a clean
    histogram it reads reliably.
    """
    return pytesseract.image_to_string(image.convert("L"))


def read_band(image: Image.Image, band: tuple[float, float]) -> str:
    """OCR ONE horizontal band with single-line page segmentation.

    Whole-image OCR with Tesseract's DEFAULT page segmentation silently drops text regions when
    they are widely separated — measured at 151/1088 of our combos. Cropping to the band and
    forcing --psm 7 (treat the image as a single text line) reads them reliably.
    """
    width, height = image.size
    y0, y1 = band
    crop = image.crop((0, int(height * y0), width, int(height * y1)))
    return pytesseract.image_to_string(crop.convert("L"), config="--psm 7")


def _normalize(s: str) -> str:
    """Lowercase; collapse every run of non-alphanumerics to a single space."""
    return re.sub(r"[^a-z0-9]+", " ", s.lower()).strip()


def text_present(ocr_text: str, phrase: str, cutoff: float = 0.8) -> bool:
    """True if ``phrase`` appears in ``ocr_text`` (normalized).

    Exact normalized substring wins; otherwise fall back to token overlap
    (fraction of the phrase's tokens found) against ``cutoff``.
    """
    norm_ocr = _normalize(ocr_text)
    norm_phrase = _normalize(phrase)
    if not norm_phrase:
        return True
    if norm_phrase in norm_ocr:
        return True
    tokens = norm_phrase.split()
    if not tokens:
        return True
    hits = sum(1 for t in tokens if t in norm_ocr)
    return hits / len(tokens) >= cutoff