"""
Lightweight OCR Engine using EasyOCR
Optimized for CPU inference with high accuracy
Supports invoices, receipts, and forms
"""

# Fix for TensorFlow/PaddlePaddle mutex warnings on macOS
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
os.environ['NUMEXPR_NUM_THREADS'] = '1'

# Suppress TensorFlow warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=FutureWarning)

import numpy as np
from typing import List, Dict, Tuple, Optional
import logging
import easyocr
import json

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class LightweightOCR:
    """
    Lightweight OCR engine based on EasyOCR
    Optimized for CPU inference and document processing
    """
    
    def __init__(
        self,
        lang: str = 'en',
        use_gpu: bool = False,
        use_angle_cls: bool = True
    ):
        """
        Initialize EasyOCR engine
        
        Args:
            lang: Language code ('en' for English)
            use_gpu: Use GPU if available (False for CPU-only deployment)
            use_angle_cls: Not used in EasyOCR (kept for compatibility)
        """
        logger.info(f"Initializing EasyOCR (language: {lang}, GPU: {use_gpu})")
        
        try:
            # Initialize EasyOCR Reader
            # EasyOCR supports multiple languages, here we use English
            self.reader = easyocr.Reader(['en'], gpu=use_gpu)
            logger.info("EasyOCR initialized successfully")
        except Exception as e:
            logger.error(f"EasyOCR initialization failed: {str(e)}")
            raise
    
    def extract_text(
        self,
        image: np.ndarray,
        return_boxes: bool = True,
        confidence_threshold: float = 0.5
    ) -> Dict:
        """
        Extract text from document image
        
        Args:
            image: Input image as numpy array (BGR or RGB)
            return_boxes: Include bounding boxes in output
            confidence_threshold: Minimum confidence score to include results
            
        Returns:
            Dictionary containing:
                - text: Full extracted text
                - boxes: List of word/line detections with boxes and confidence
                - lines: Grouped by lines
        """
        logger.info("Starting OCR extraction")
        
        # Run EasyOCR
        # detail=1 returns [box, text, confidence]
        results = self.reader.readtext(image, detail=1)
        
        if not results:
            logger.warning("No text detected in image")
            return {
                "text": "",
                "boxes": [],
                "lines": []
            }
        
        # Parse results
        full_text_parts = []
        boxes = []
        lines = []
        
        for idx, (box_coords, text, confidence) in enumerate(results):
            # Filter by confidence
            if confidence < confidence_threshold:
                continue
            
            # Add to full text
            full_text_parts.append(text)
            
            # Convert box to simple bbox format [x1, y1, x2, y2]
            # EasyOCR returns [[x1,y1], [x2,y1], [x2,y2], [x1,y2]]
            box_array = np.array(box_coords)
            x_coords = box_array[:, 0]
            y_coords = box_array[:, 1]
            bbox = [
                float(np.min(x_coords)),
                float(np.min(y_coords)),
                float(np.max(x_coords)),
                float(np.max(y_coords))
            ]
            
            # Create box entry
            box_entry = {
                "text": text,
                "bbox": bbox,
                "confidence": float(confidence),
                "line_number": idx
            }
            boxes.append(box_entry)
            
            # Create line entry
            line_entry = {
                "text": text,
                "bbox": bbox,
                "confidence": float(confidence)
            }
            lines.append(line_entry)
        
        # Combine full text
        full_text = "\n".join(full_text_parts)
        
        result_dict = {
            "text": full_text,
            "boxes": boxes if return_boxes else [],
            "lines": lines
        }
        
        logger.info(f"Extracted {len(lines)} lines with avg confidence: "
                   f"{np.mean([l['confidence'] for l in lines]) if lines else 0:.3f}")
        
        return result_dict
    
    def get_text_with_positions(
        self,
        image: np.ndarray,
        confidence_threshold: float = 0.5
    ) -> Tuple[str, List[Dict]]:
        """
        Convenience method to get both text and position information
        
        Args:
            image: Input image
            confidence_threshold: Minimum confidence
            
        Returns:
            Tuple of (full_text, list of box dictionaries)
        """
        result = self.extract_text(image, return_boxes=True, confidence_threshold=confidence_threshold)
        return result["text"], result["boxes"]


class PDFtoImageConverter:
    """
    Convert PDF pages to images for OCR processing
    Lightweight implementation
    """
    
    @staticmethod
    def pdf_to_images(pdf_path: str, dpi: int = 200) -> List[np.ndarray]:
        """
        Convert PDF to list of images
        
        Args:
            pdf_path: Path to PDF file
            dpi: Resolution for conversion (200 is good for OCR)
            
        Returns:
            List of images as numpy arrays
        """
        try:
            from pdf2image import convert_from_path
            import cv2
            
            logger.info(f"Converting PDF to images: {pdf_path}")
            
            # Convert PDF to PIL images
            pil_images = convert_from_path(pdf_path, dpi=dpi)
            
            # Convert to numpy arrays (OpenCV format)
            images = []
            for pil_img in pil_images:
                # Convert PIL RGB to OpenCV BGR
                img_array = np.array(pil_img)
                img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
                images.append(img_bgr)
            
            logger.info(f"Converted {len(images)} pages")
            return images
            
        except ImportError:
            logger.error("pdf2image not installed. Install with: pip install pdf2image")
            logger.error("Also requires poppler-utils system package")
            raise
        except Exception as e:
            logger.error(f"Error converting PDF: {str(e)}")
            raise


def extract_text_from_file(
    file_path: str,
    lang: str = 'en',
    use_gpu: bool = False,
    confidence_threshold: float = 0.5
) -> Dict:
    """
    Convenience function to extract text from image or PDF file
    
    Args:
        file_path: Path to image or PDF file
        lang: Language code
        use_gpu: Use GPU for OCR
        confidence_threshold: Minimum confidence score
        
    Returns:
        Dictionary with extracted text and metadata
    """
    import cv2
    import os
    
    # Initialize OCR
    ocr_engine = LightweightOCR(lang=lang, use_gpu=use_gpu)
    
    # Check file type
    ext = os.path.splitext(file_path)[1].lower()
    
    if ext == '.pdf':
        # Convert PDF to images
        images = PDFtoImageConverter.pdf_to_images(file_path)
        
        # Process each page
        results = []
        for page_num, image in enumerate(images):
            logger.info(f"Processing page {page_num + 1}/{len(images)}")
            page_result = ocr_engine.extract_text(image, confidence_threshold=confidence_threshold)
            page_result['page_number'] = page_num + 1
            results.append(page_result)
        
        return {
            "file_path": file_path,
            "file_type": "pdf",
            "num_pages": len(images),
            "pages": results
        }
    
    else:
        # Load image
        image = cv2.imread(file_path)
        if image is None:
            raise ValueError(f"Could not load image from {file_path}")
        
        # Process single image
        result = ocr_engine.extract_text(image, confidence_threshold=confidence_threshold)
        
        return {
            "file_path": file_path,
            "file_type": "image",
            "num_pages": 1,
            "pages": [result]
        }


if __name__ == "__main__":
    import sys
    
    if len(sys.argv) < 2:
        print("Usage: python ocr_engine.py <image_or_pdf_path>")
        sys.exit(1)
    
    input_path = sys.argv[1]
    output_path = "ocr_output.json"
    
    # Extract text
    result = extract_text_from_file(input_path)
    
    # Save to JSON
    with open(output_path, 'w', encoding='utf-8') as f:
        json.dump(result, f, indent=2, ensure_ascii=False)
    
    print(f"\nOCR results saved to {output_path}")
    print(f"\nExtracted text preview:")
    print("-" * 50)
    for page in result['pages']:
        print(page['text'][:500])  # First 500 chars
        if len(page['text']) > 500:
            print("...")