"""Lightweight OCR model using Tesseract + RapidOCR fallback.
No PyTorch or heavy ML deps — works on HF free tier.
"""
import logging
import os
import shutil
from pathlib import Path

try:
    import pytesseract
    from PIL import Image
    HAS_TESSERACT = True
except ImportError:
    HAS_TESSERACT = False

try:
    from rapidocr_onnxruntime import RapidOCR
    HAS_RAPIDOCR = True
except ImportError:
    HAS_RAPIDOCR = False


class OCRModel:
    def __init__(self, tesseract_cmd=""):
        self._rapidocr_engine = None
        self._tesseract_path = self._find_tesseract(tesseract_cmd) if HAS_TESSERACT else ""
        logging.getLogger("RapidOCR").disabled = True

    def _find_tesseract(self, tesseract_cmd=""):
        env_path = tesseract_cmd.strip() or os.environ.get("TESSERACT_CMD", "").strip()
        if env_path and Path(env_path).exists():
            return env_path
        cmd = shutil.which("tesseract")
        if cmd:
            return cmd
        for p in (r"C:\Program Files\Tesseract-OCR\tesseract.exe",
                  r"C:\Program Files (x86)\Tesseract-OCR\tesseract.exe",
                  "/usr/bin/tesseract"):
            if Path(p).exists():
                return p
        return ""

    def extract_text(self, image_path):
        """Extract text from image using Tesseract, falling back to RapidOCR."""
        text = self._tesseract_ocr(image_path)
        if text:
            return text
        return self._rapidocr(image_path)

    def _tesseract_ocr(self, image_path):
        if not HAS_TESSERACT or not self._tesseract_path:
            return ""
        pytesseract.pytesseract.tesseract_cmd = self._tesseract_path
        try:
            image = Image.open(image_path)
            return pytesseract.image_to_string(image).strip()
        except Exception:
            return ""

    def _rapidocr(self, image_path):
        if not HAS_RAPIDOCR:
            return ""
        if self._rapidocr_engine is None:
            self._rapidocr_engine = RapidOCR()
        try:
            result = self._rapidocr_engine(str(image_path))
            if result and result[0]:
                texts = [item[1].strip() for item in result[0] if item[1].strip()]
                return "\n".join(texts).strip()
        except Exception:
            pass
        return ""