"""
OCR processing for extracted video frames.
Supports multiple OCR engines and text deduplication.
"""
from typing import List, Tuple, Dict, Optional
from pathlib import Path
from difflib import SequenceMatcher
import re
import logging

logger = logging.getLogger(__name__)


class OCRProcessor:
    """Process frames with OCR to extract text."""

    def __init__(self, engine: str = "tesseract", lang: str = "eng"):
        """
        Initialize OCR processor.

        Args:
            engine: OCR engine to use ('tesseract', 'easyocr', 'paddleocr')
            lang: Language code for OCR
        """
        self.engine = engine.lower()
        self.lang = lang
        self._ocr_engine = None
        self._init_engine()

    def _init_engine(self):
        """Initialize the selected OCR engine."""
        if self.engine == "tesseract":
            try:
                import pytesseract
                self._ocr_engine = pytesseract
            except ImportError:
                raise ImportError("pytesseract not installed. Run: pip install pytesseract")

        elif self.engine == "easyocr":
            try:
                import easyocr
                self._ocr_engine = easyocr.Reader([self.lang])
            except ImportError:
                raise ImportError("easyocr not installed. Run: pip install easyocr")

        elif self.engine == "paddleocr":
            try:
                from paddleocr import PaddleOCR
                self._ocr_engine = PaddleOCR(lang=self.lang, use_angle_cls=True, show_log=False)
            except ImportError:
                raise ImportError("paddleocr not installed. Run: pip install paddleocr")

        else:
            raise ValueError(f"Unknown OCR engine: {self.engine}")

    def extract_text(self, image_path: str, preserve_layout: bool = True) -> str:
        """
        Extract text from a single image.

        Args:
            image_path: Path to image file
            preserve_layout: Try to preserve whitespace and layout

        Returns:
            Extracted text
        """
        if self.engine == "tesseract":
            from PIL import Image
            import pytesseract
            image = Image.open(image_path)

            # Use PSM 6 (uniform block of text) to preserve layout better
            config = '--psm 6' if preserve_layout else ''
            text = pytesseract.image_to_string(image, config=config)

        elif self.engine == "easyocr":
            result = self._ocr_engine.readtext(image_path, detail=0)
            text = "\n".join(result)

        elif self.engine == "paddleocr":
            result = self._ocr_engine.ocr(image_path, cls=True)
            if result and result[0]:
                text = "\n".join([line[1][0] for line in result[0]])
            else:
                text = ""

        return self._clean_text(text)

    def _clean_text(self, text: str, preserve_indentation: bool = True) -> str:
        """
        Clean up OCR output.

        Args:
            text: Raw OCR text
            preserve_indentation: Keep leading whitespace on lines

        Returns:
            Cleaned text
        """
        if preserve_indentation:
            # Remove excessive blank lines but preserve indentation
            lines = text.split('\n')
            cleaned_lines = []
            for line in lines:
                # Keep line if it has content or is single empty line
                if line.strip() or (cleaned_lines and cleaned_lines[-1].strip()):
                    cleaned_lines.append(line)
            return '\n'.join(cleaned_lines).strip()
        else:
            # Original aggressive cleaning
            text = re.sub(r'\n\s*\n', '\n', text)
            text = re.sub(r' +', ' ', text)
            return text.strip()

    def process_frames(
        self,
        frames_info: List[Tuple[str, float]],
        deduplicate: bool = True,
        similarity_threshold: float = 0.85
    ) -> List[Dict]:
        """
        Process multiple frames and extract text.

        Args:
            frames_info: List of (frame_path, timestamp) tuples
            deduplicate: Whether to remove similar consecutive texts
            similarity_threshold: Threshold for considering texts as duplicates (0-1)

        Returns:
            List of dicts with 'timestamp', 'text', and 'frame_path'
        """
        results = []
        prev_text = ""

        for idx, (frame_path, timestamp) in enumerate(frames_info, 1):
            logger.debug(f"Processing frame {idx}/{len(frames_info)} at {timestamp:.2f}s...")
            text = self.extract_text(frame_path)

            if not text:
                logger.debug(f"No text extracted from frame at {timestamp:.2f}s")
                continue

            # Debug: Show what was extracted
            logger.debug(f"Frame {idx} ({timestamp:.2f}s): Extracted {len(text)} chars")
            logger.debug(f"Content preview: {text[:150]}{'...' if len(text) > 150 else ''}")

            # Deduplicate similar consecutive frames
            if deduplicate and prev_text:
                similarity = self._text_similarity(prev_text, text)
                logger.debug(f"Similarity to previous frame: {similarity:.2f} (threshold: {similarity_threshold})")
                if similarity > similarity_threshold:
                    logger.debug(f"⊘ Skipping duplicate frame at {timestamp:.2f}s (similarity: {similarity:.2f})")
                    continue

            results.append({
                'timestamp': timestamp,
                'text': text,
                'frame_path': frame_path
            })

            prev_text = text

        logger.info(f"Extracted text from {len(results)} frames (deduplication: {deduplicate})")
        return results

    def _text_similarity(self, text1: str, text2: str) -> float:
        """
        Calculate similarity between two texts.

        Returns:
            Similarity score between 0 and 1
        """
        return SequenceMatcher(None, text1, text2).ratio()