ocr_utils.py

"""
Utility functions for OCR processing with Mistral AI.
Contains helper functions for working with OCR responses and image handling.
"""

import json
import base64
import io
import zipfile
import logging
import numpy as np
import time
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional, Union, Any, Tuple
from functools import lru_cache

# Configure logging
logger = logging.getLogger("ocr_utils")

try:
    from PIL import Image, ImageEnhance, ImageFilter, ImageOps
    import cv2
    PILLOW_AVAILABLE = True
    CV2_AVAILABLE = True
except ImportError as e:
    # Check which image libraries are available
    if "PIL" in str(e):
        PILLOW_AVAILABLE = False
    if "cv2" in str(e):
        CV2_AVAILABLE = False

from mistralai import DocumentURLChunk, ImageURLChunk, TextChunk
from mistralai.models import OCRImageObject

# Import configuration
try:
    from config import IMAGE_PREPROCESSING
except ImportError:
    # Fallback defaults if config not available
    IMAGE_PREPROCESSING = {
        "enhance_contrast": 1.5,
        "sharpen": True,
        "denoise": True,
        "max_size_mb": 8.0,
        "target_dpi": 300,
        "compression_quality": 92
    }

def replace_images_in_markdown(markdown_str: str, images_dict: dict) -> str:
    """
    Replace image placeholders in markdown with base64-encoded images.

    Args:
        markdown_str: Markdown text containing image placeholders
        images_dict: Dictionary mapping image IDs to base64 strings

    Returns:
        Markdown text with images replaced by base64 data
    """
    for img_name, base64_str in images_dict.items():
        markdown_str = markdown_str.replace(
            f"![{img_name}]({img_name})", f"![{img_name}]({base64_str})"
        )
    return markdown_str

def get_combined_markdown(ocr_response) -> str:
    """
    Combine OCR text and images into a single markdown document.
    
    Args:
        ocr_response: OCR response object from Mistral AI
        
    Returns:
        Combined markdown string with embedded images
    """
    markdowns = []
    
    # Process each page of the OCR response
    for page in ocr_response.pages:
        # Extract image data if available
        image_data = {}
        if hasattr(page, "images"):
            for img in page.images:
                if hasattr(img, "id") and hasattr(img, "image_base64"):
                    image_data[img.id] = img.image_base64
        
        # Replace image placeholders with base64 data
        page_markdown = page.markdown if hasattr(page, "markdown") else ""
        processed_markdown = replace_images_in_markdown(page_markdown, image_data)
        markdowns.append(processed_markdown)
    
    # Join all pages' markdown with double newlines
    return "\n\n".join(markdowns)

def encode_image_for_api(image_path: Union[str, Path]) -> str:
    """
    Encode an image as base64 data URL for API submission.
    
    Args:
        image_path: Path to the image file
        
    Returns:
        Base64 data URL for the image
    """
    # Convert to Path object if string
    image_file = Path(image_path) if isinstance(image_path, str) else image_path
    
    # Verify image exists
    if not image_file.is_file():
        raise FileNotFoundError(f"Image file not found: {image_file}")
    
    # Encode image as base64
    encoded = base64.b64encode(image_file.read_bytes()).decode()
    return f"data:image/jpeg;base64,{encoded}"

def process_image_with_ocr(client, image_path: Union[str, Path], model: str = "mistral-ocr-latest"):
    """
    Process an image with OCR and return the response.
    
    Args:
        client: Mistral AI client
        image_path: Path to the image file
        model: OCR model to use
        
    Returns:
        OCR response object
    """
    # Encode image as base64
    base64_data_url = encode_image_for_api(image_path)
    
    # Process image with OCR
    image_response = client.ocr.process(
        document=ImageURLChunk(image_url=base64_data_url),
        model=model
    )
    
    return image_response

def ocr_response_to_json(ocr_response, indent: int = 4) -> str:
    """
    Convert OCR response to a formatted JSON string.
    
    Args:
        ocr_response: OCR response object
        indent: Indentation level for JSON formatting
        
    Returns:
        Formatted JSON string
    """
    # Convert OCR response to a dictionary
    response_dict = {
        "text": ocr_response.text if hasattr(ocr_response, "text") else "",
        "pages": []
    }
    
    # Process pages if available
    if hasattr(ocr_response, "pages"):
        for page in ocr_response.pages:
            page_dict = {
                "text": page.text if hasattr(page, "text") else "",
                "markdown": page.markdown if hasattr(page, "markdown") else "",
                "images": []
            }
            
            # Process images if available
            if hasattr(page, "images"):
                for img in page.images:
                    img_dict = {
                        "id": img.id if hasattr(img, "id") else "",
                        "base64": img.image_base64 if hasattr(img, "image_base64") else ""
                    }
                    page_dict["images"].append(img_dict)
            
            response_dict["pages"].append(page_dict)
    
    # Convert dictionary to JSON
    return json.dumps(response_dict, indent=indent)

def create_results_zip_in_memory(results):
    """
    Create a zip file containing OCR results in memory.
    
    Args:
        results: Dictionary or list of OCR results
        
    Returns:
        Binary zip file data
    """
    # Create a BytesIO object
    zip_buffer = io.BytesIO()
    
    # Check if results is a list or a dictionary
    is_list = isinstance(results, list)
    
    # Create zip file in memory
    with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zipf:
        if is_list:
            # Handle list of results
            for i, result in enumerate(results):
                try:
                    # Create a descriptive base filename for this result
                    base_filename = result.get('file_name', f'document_{i+1}').split('.')[0]
                    
                    # Add document type if available
                    if 'topics' in result and result['topics']:
                        topic = result['topics'][0].lower().replace(' ', '_')
                        base_filename = f"{base_filename}_{topic}"
                    
                    # Add language if available
                    if 'languages' in result and result['languages']:
                        lang = result['languages'][0].lower()
                        # Only add if it's not already in the filename
                        if lang not in base_filename.lower():
                            base_filename = f"{base_filename}_{lang}"
                    
                    # For PDFs, add page information
                    if 'total_pages' in result and 'processed_pages' in result:
                        base_filename = f"{base_filename}_p{result['processed_pages']}of{result['total_pages']}"
                    
                    # Add timestamp if available
                    if 'timestamp' in result:
                        try:
                            # Try to parse the timestamp and reformat it
                            dt = datetime.strptime(result['timestamp'], "%Y-%m-%d %H:%M")
                            timestamp = dt.strftime("%Y%m%d_%H%M%S")
                            base_filename = f"{base_filename}_{timestamp}"
                        except:
                            pass
                    
                    # Add JSON results for each file with descriptive name
                    result_json = json.dumps(result, indent=2)
                    zipf.writestr(f"{base_filename}.json", result_json)
                    
                    # Add HTML content (generated from the result)
                    html_content = create_html_with_images(result)
                    zipf.writestr(f"{base_filename}_with_images.html", html_content)
                    
                    # Add raw OCR text if available
                    if "ocr_contents" in result and "raw_text" in result["ocr_contents"]:
                        zipf.writestr(f"{base_filename}.txt", result["ocr_contents"]["raw_text"])
                    
                    # Add HTML visualization if available
                    if "html_visualization" in result:
                        zipf.writestr(f"visualization_{i+1}.html", result["html_visualization"])
                    
                    # Add images if available (limit to conserve memory)
                    if "pages_data" in result:
                        for page_idx, page in enumerate(result["pages_data"]):
                            for img_idx, img in enumerate(page.get("images", [])[:3]):  # Limit to first 3 images per page
                                img_base64 = img.get("image_base64", "")
                                if img_base64:
                                    # Strip data URL prefix if present
                                    if img_base64.startswith("data:image"):
                                        img_base64 = img_base64.split(",", 1)[1]
                                        
                                    # Decode base64 and add to zip
                                    try:
                                        img_data = base64.b64decode(img_base64)
                                        zipf.writestr(f"images/result_{i+1}_page_{page_idx+1}_img_{img_idx+1}.jpg", img_data)
                                    except:
                                        pass
                except Exception:
                    # If any result fails, skip it and continue
                    continue
        else:
            # Handle single result
            try:
                # Create a descriptive base filename for this result
                base_filename = results.get('file_name', 'document').split('.')[0]
                
                # Add document type if available
                if 'topics' in results and results['topics']:
                    topic = results['topics'][0].lower().replace(' ', '_')
                    base_filename = f"{base_filename}_{topic}"
                
                # Add language if available
                if 'languages' in results and results['languages']:
                    lang = results['languages'][0].lower()
                    # Only add if it's not already in the filename
                    if lang not in base_filename.lower():
                        base_filename = f"{base_filename}_{lang}"
                
                # For PDFs, add page information
                if 'total_pages' in results and 'processed_pages' in results:
                    base_filename = f"{base_filename}_p{results['processed_pages']}of{results['total_pages']}"
                
                # Add timestamp if available
                if 'timestamp' in results:
                    try:
                        # Try to parse the timestamp and reformat it
                        dt = datetime.strptime(results['timestamp'], "%Y-%m-%d %H:%M")
                        timestamp = dt.strftime("%Y%m%d_%H%M%S")
                        base_filename = f"{base_filename}_{timestamp}"
                    except:
                        # If parsing fails, create a new timestamp
                        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
                        base_filename = f"{base_filename}_{timestamp}"
                else:
                    # No timestamp in the result, create a new one
                    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
                    base_filename = f"{base_filename}_{timestamp}"
                
                # Add JSON results with descriptive name
                results_json = json.dumps(results, indent=2)
                zipf.writestr(f"{base_filename}.json", results_json)
                
                # Add HTML content with descriptive name
                html_content = create_html_with_images(results)
                zipf.writestr(f"{base_filename}_with_images.html", html_content)
                
                # Add raw OCR text if available
                if "ocr_contents" in results and "raw_text" in results["ocr_contents"]:
                    zipf.writestr(f"{base_filename}.txt", results["ocr_contents"]["raw_text"])
                
                # Add HTML visualization if available
                if "html_visualization" in results:
                    zipf.writestr("visualization.html", results["html_visualization"])
                
                # Add images if available
                if "pages_data" in results:
                    for page_idx, page in enumerate(results["pages_data"]):
                        for img_idx, img in enumerate(page.get("images", [])):
                            img_base64 = img.get("image_base64", "")
                            if img_base64:
                                # Strip data URL prefix if present
                                if img_base64.startswith("data:image"):
                                    img_base64 = img_base64.split(",", 1)[1]
                                    
                                # Decode base64 and add to zip
                                try:
                                    img_data = base64.b64decode(img_base64)
                                    zipf.writestr(f"images/page_{page_idx+1}_img_{img_idx+1}.jpg", img_data)
                                except:
                                    pass
            except Exception:
                # If processing fails, return empty zip
                pass
    
    # Seek to the beginning of the BytesIO object
    zip_buffer.seek(0)
    
    # Return the zip file bytes
    return zip_buffer.getvalue()

def create_results_zip(results, output_dir=None, zip_name=None):
    """
    Create a zip file containing OCR results.
    
    Args:
        results: Dictionary or list of OCR results
        output_dir: Optional output directory
        zip_name: Optional zip file name
        
    Returns:
        Path to the created zip file
    """
    # Create temporary output directory if not provided
    if output_dir is None:
        output_dir = Path.cwd() / "output"
        output_dir.mkdir(exist_ok=True)
    else:
        output_dir = Path(output_dir)
        output_dir.mkdir(exist_ok=True)
    
    # Check if results is a list or a dictionary
    is_list = isinstance(results, list)
    
    # Generate zip name if not provided
    if zip_name is None:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        
        if is_list:
            # For a list of results, create a more descriptive name based on the content
            file_count = len(results)
            
            # Count document types
            pdf_count = sum(1 for r in results if r.get('file_name', '').lower().endswith('.pdf'))
            img_count = sum(1 for r in results if r.get('file_name', '').lower().endswith(('.jpg', '.jpeg', '.png')))
            
            # Create descriptive name based on contents
            if pdf_count > 0 and img_count > 0:
                zip_name = f"historical_ocr_mixed_{pdf_count}pdf_{img_count}img_{timestamp}.zip"
            elif pdf_count > 0:
                zip_name = f"historical_ocr_pdf_documents_{pdf_count}_{timestamp}.zip"
            elif img_count > 0:
                zip_name = f"historical_ocr_images_{img_count}_{timestamp}.zip"
            else:
                zip_name = f"historical_ocr_results_{file_count}_{timestamp}.zip"
        else:
            # For single result, create descriptive filename
            base_name = results.get("file_name", "document").split('.')[0]
            
            # Add document type if available
            if 'topics' in results and results['topics']:
                topic = results['topics'][0].lower().replace(' ', '_')
                base_name = f"{base_name}_{topic}"
            
            # Add language if available
            if 'languages' in results and results['languages']:
                lang = results['languages'][0].lower()
                # Only add if it's not already in the filename
                if lang not in base_name.lower():
                    base_name = f"{base_name}_{lang}"
            
            # For PDFs, add page information
            if 'total_pages' in results and 'processed_pages' in results:
                base_name = f"{base_name}_p{results['processed_pages']}of{results['total_pages']}"
            
            # Add timestamp
            zip_name = f"{base_name}_{timestamp}.zip"
    
    try:
        # Get zip data in memory first
        zip_data = create_results_zip_in_memory(results)
        
        # Save to file
        zip_path = output_dir / zip_name
        with open(zip_path, 'wb') as f:
            f.write(zip_data)
        
        return zip_path
    except Exception as e:
        # Create an empty zip file as fallback
        zip_path = output_dir / zip_name
        with zipfile.ZipFile(zip_path, 'w') as zipf:
            zipf.writestr("info.txt", "Could not create complete archive")
        
        return zip_path


# Advanced image preprocessing functions

def preprocess_image_for_ocr(image_path: Union[str, Path]) -> Tuple[Image.Image, str]:
    """
    Preprocess an image for optimal OCR performance with enhanced speed and memory optimization.
    Enhanced to handle large newspaper and document images.
    
    Args:
        image_path: Path to the image file
        
    Returns:
        Tuple of (processed PIL Image, base64 string)
    """
    # Fast path: Skip all processing if PIL not available
    if not PILLOW_AVAILABLE:
        logger.info("PIL not available, skipping image preprocessing")
        return None, encode_image_for_api(image_path)
    
    # Convert to Path object if string
    image_file = Path(image_path) if isinstance(image_path, str) else image_path
    
    # Thread-safe caching with early exit for already processed images
    try:
        # Fast stat calls for file metadata - consolidate to reduce I/O
        file_stat = image_file.stat()
        file_size = file_stat.st_size
        file_size_mb = file_size / (1024 * 1024)
        mod_time = file_stat.st_mtime
        
        # Create a cache key based on essential file properties
        cache_key = f"{image_file.name}_{file_size}_{mod_time}"
        
        # Fast path: Return cached result if available
        if hasattr(preprocess_image_for_ocr, "_cache") and cache_key in preprocess_image_for_ocr._cache:
            logger.debug(f"Using cached preprocessing result for {image_file.name}")
            return preprocess_image_for_ocr._cache[cache_key]
            
        # Optimization: Skip heavy processing for very small files
        # Small images (less than 100KB) likely don't need preprocessing
        if file_size < 100000:  # 100KB
            logger.info(f"Image {image_file.name} is small ({file_size/1024:.1f}KB), using minimal processing")
            with Image.open(image_file) as img:
                # Normalize mode only
                if img.mode not in ('RGB', 'L'):
                    img = img.convert('RGB')
                
                # Save with light optimization
                buffer = io.BytesIO()
                img.save(buffer, format="JPEG", quality=95, optimize=True)
                buffer.seek(0)
                
                # Get base64
                encoded_image = base64.b64encode(buffer.getvalue()).decode()
                base64_data_url = f"data:image/jpeg;base64,{encoded_image}"
                
                # Cache and return
                result = (img, base64_data_url)
                if not hasattr(preprocess_image_for_ocr, "_cache"):
                    preprocess_image_for_ocr._cache = {}
                
                # Clean cache if needed
                if len(preprocess_image_for_ocr._cache) > 20:  # Increased cache size for better performance
                    # Remove oldest 5 entries for better batch processing
                    for _ in range(5):
                        if preprocess_image_for_ocr._cache:
                            preprocess_image_for_ocr._cache.pop(next(iter(preprocess_image_for_ocr._cache)))
                    
                preprocess_image_for_ocr._cache[cache_key] = result
                return result
            
        # Special handling for large newspaper-style documents
        if file_size_mb > 5 and image_file.name.lower().endswith(('.jpg', '.jpeg', '.png')):
            logger.info(f"Large image detected ({file_size_mb:.2f}MB), checking for newspaper format")
            try:
                # Quickly check dimensions without loading full image
                with Image.open(image_file) as img:
                    width, height = img.size
                    aspect_ratio = width / height
                    
                    # Newspaper-style documents typically have width > height or are very large
                    is_newspaper_format = (aspect_ratio > 1.2 and width > 2000) or (width > 3000 or height > 3000)
                    
                    if is_newspaper_format:
                        logger.info(f"Newspaper format detected: {width}x{height}, applying specialized processing")
                
            except Exception as dim_err:
                logger.debug(f"Error checking dimensions: {str(dim_err)}")
                is_newspaper_format = False
        else:
            is_newspaper_format = False
                
    except Exception as e:
        # If stat or cache handling fails, log and continue with processing
        logger.debug(f"Cache handling failed for {image_path}: {str(e)}")
        # Ensure we have a valid file_size_mb for later decisions
        try:
            file_size_mb = image_file.stat().st_size / (1024 * 1024)
        except:
            file_size_mb = 0  # Default if we can't determine size
        
        # Default to not newspaper format on error
        is_newspaper_format = False
        
    try:
        # Process start time for performance logging
        start_time = time.time()
        
        # Open and process the image with minimal memory footprint
        with Image.open(image_file) as img:
            # Normalize image mode
            if img.mode not in ('RGB', 'L'):
                img = img.convert('RGB')
            
            # Fast path: Quick check of image properties to determine appropriate processing
            width, height = img.size
            image_area = width * height
            
            # Detect document type only for medium to large images to save processing time
            is_document = False
            is_newspaper = False
            
            # More aggressive document type detection for larger images
            if image_area > 500000:  # Approx 700x700 or larger
                # Store image for document detection
                _detect_document_type_impl._current_img = img
                is_document = _detect_document_type_impl(None)
                
                # Additional check for newspaper format
                if is_document:
                    # Newspapers typically have wide formats or very large dimensions
                    aspect_ratio = width / height
                    is_newspaper = (aspect_ratio > 1.2 and width > 2000) or (width > 3000 or height > 3000)
                
                logger.debug(f"Document type detection for {image_file.name}: " +
                           f"{'newspaper' if is_newspaper else 'document' if is_document else 'photo'}")
            
            # Special processing for very large images (newspapers and large documents)
            if is_newspaper:
                # For newspaper format, we need more specialized processing
                logger.info(f"Processing newspaper format image: {width}x{height}")
                
                # For newspapers, we prioritize text clarity over file size
                # Use higher target resolution to preserve small text common in newspapers
                # But still need to resize if extremely large to avoid API limits
                max_dimension = max(width, height)
                
                if max_dimension > 6000:  # Extremely large
                    scale_factor = 0.4   # Preserve more resolution for newspapers (increased from 0.35)
                elif max_dimension > 4000:
                    scale_factor = 0.6   # Higher resolution for better text extraction (increased from 0.5)
                else:
                    scale_factor = 0.8   # Minimal reduction for moderate newspaper size (increased from 0.7)
                
                # Calculate new dimensions - maintain higher resolution
                new_width = int(width * scale_factor)
                new_height = int(height * scale_factor)
                
                # Use high-quality resampling to preserve text clarity in newspapers
                processed_img = img.resize((new_width, new_height), Image.LANCZOS)
                logger.debug(f"Resized newspaper image from {width}x{height} to {new_width}x{new_height}")
                
                # For newspapers, we also want to enhance the contrast and sharpen the image 
                # before the main OCR processing for better text extraction
                if img.mode in ('RGB', 'RGBA'):
                    # For color newspapers, enhance both the overall image and then convert to grayscale
                    # This helps with mixed content newspapers that have both text and images
                    enhancer = ImageEnhance.Contrast(processed_img)
                    processed_img = enhancer.enhance(1.3)  # Boost contrast but not too aggressively
                    
                    # Also enhance saturation to make colored text more visible
                    enhancer_sat = ImageEnhance.Color(processed_img)
                    processed_img = enhancer_sat.enhance(1.2)
                
            # Standard processing for other large images
            elif file_size_mb > IMAGE_PREPROCESSING["max_size_mb"] or max(width, height) > 3000:
                # Calculate target dimensions directly instead of using the heavier resize function
                target_width, target_height = width, height
                max_dimension = max(width, height)
                
                # Use a sliding scale for reduction based on image size
                if max_dimension > 5000:
                    scale_factor = 0.3   # Slightly less aggressive reduction (was 0.25)
                elif max_dimension > 3000:
                    scale_factor = 0.45  # Slightly less aggressive reduction (was 0.4)
                else:
                    scale_factor = 0.65  # Slightly less aggressive reduction (was 0.6)
                
                # Calculate new dimensions
                new_width = int(width * scale_factor)
                new_height = int(height * scale_factor)
                
                # Use direct resize with optimized resampling filter based on image size
                if image_area > 3000000:  # Very large, use faster but lower quality
                    processed_img = img.resize((new_width, new_height), Image.BILINEAR)
                else:  # Medium size, use better quality
                    processed_img = img.resize((new_width, new_height), Image.LANCZOS)
                    
                logger.debug(f"Resized image from {width}x{height} to {new_width}x{new_height}")
            else:
                # Skip resizing for smaller images
                processed_img = img
            
            # Apply appropriate processing based on document type and size
            if is_document:
                # Process as document with optimized path based on size
                if image_area > 1000000:  # Full processing for larger documents
                    preprocess_document_image._current_img = processed_img
                    processed = _preprocess_document_image_impl()
                else:  # Lightweight processing for smaller documents
                    # Just enhance contrast for small documents to save time
                    enhancer = ImageEnhance.Contrast(processed_img)
                    processed = enhancer.enhance(1.3)
            else:
                # Process as photo with optimized path based on size
                if image_area > 1000000:  # Full processing for larger photos
                    preprocess_general_image._current_img = processed_img
                    processed = _preprocess_general_image_impl()
                else:  # Skip processing for smaller photos
                    processed = processed_img
            
            # Optimize memory handling during encoding
            buffer = io.BytesIO()
            
            # Adjust quality based on image size to optimize API payload
            if file_size_mb > 5:
                quality = 85  # Lower quality for large files
            else:
                quality = IMAGE_PREPROCESSING["compression_quality"]
                
            # Save with optimized parameters
            processed.save(buffer, format="JPEG", quality=quality, optimize=True)
            buffer.seek(0)
            
            # Get base64 with minimal memory footprint
            encoded_image = base64.b64encode(buffer.getvalue()).decode()
            base64_data_url = f"data:image/jpeg;base64,{encoded_image}"
            
            # Update cache thread-safely
            result = (processed, base64_data_url)
            if not hasattr(preprocess_image_for_ocr, "_cache"):
                preprocess_image_for_ocr._cache = {}
            
            # LRU-like cache management with improved clearing
            if len(preprocess_image_for_ocr._cache) > 20:
                try:
                    # Remove several entries to avoid frequent cache clearing
                    for _ in range(5):
                        if preprocess_image_for_ocr._cache:
                            preprocess_image_for_ocr._cache.pop(next(iter(preprocess_image_for_ocr._cache)))
                except:
                    # If removal fails, just continue
                    pass
            
            # Add to cache
            try:
                preprocess_image_for_ocr._cache[cache_key] = result
            except Exception:
                # If caching fails, just proceed
                pass
            
            # Log performance metrics
            processing_time = time.time() - start_time
            logger.debug(f"Image preprocessing completed in {processing_time:.3f}s for {image_file.name}")
            
            # Return both processed image and base64 string
            return result
            
    except Exception as e:
        # If preprocessing fails, log error and use original image
        logger.warning(f"Image preprocessing failed: {str(e)}. Using original image.")
        return None, encode_image_for_api(image_path)

# Removed caching decorator to fix unhashable type error
def detect_document_type(img: Image.Image) -> bool:
    """
    Detect if an image is likely a document (text-heavy) vs. a photo.
    
    Args:
        img: PIL Image object
        
    Returns:
        True if likely a document, False otherwise
    """
    # Direct implementation without caching
    return _detect_document_type_impl(None)

def _detect_document_type_impl(img_hash=None) -> bool:
    """
    Optimized implementation of document type detection for faster processing.
    The img_hash parameter is unused but kept for backward compatibility.
    
    Enhanced to better detect handwritten documents and newspaper formats.
    """
    # Fast path: Get the image from thread-local storage
    if not hasattr(_detect_document_type_impl, "_current_img"):
        return False  # Fail safe in case image is not set
        
    img = _detect_document_type_impl._current_img
    
    # Skip processing for tiny images - just classify as non-documents
    width, height = img.size
    if width * height < 100000:  # Approx 300x300 or smaller
        return False
    
    # Convert to grayscale for analysis (using faster conversion)
    gray_img = img.convert('L')
    
    # PIL-only path for systems without OpenCV
    if not CV2_AVAILABLE:
        # Faster method: Sample a subset of the image for edge detection
        # Downscale image for faster processing
        sample_size = min(width, height, 1000)
        scale_factor = sample_size / max(width, height)
        
        if scale_factor < 0.9:  # Only resize if significant reduction
            sample_img = gray_img.resize(
                (int(width * scale_factor), int(height * scale_factor)), 
                Image.NEAREST  # Fastest resampling method
            )
        else:
            sample_img = gray_img
            
        # Fast edge detection on sample
        edges = sample_img.filter(ImageFilter.FIND_EDGES)
        
        # Count edge pixels using threshold (faster than summing individual pixels)
        edge_data = edges.getdata()
        edge_threshold = 40  # Lowered threshold to better detect handwritten texts
        
        # Use list comprehension for better performance
        edge_count = sum(1 for p in edge_data if p > edge_threshold)
        total_pixels = len(edge_data)
        edge_ratio = edge_count / total_pixels
        
        # Check if bright areas exist - simple approximation of text/background contrast
        bright_count = sum(1 for p in gray_img.getdata() if p > 200)
        bright_ratio = bright_count / (width * height)
        
        # Documents typically have more edges (text boundaries) and bright areas (background)
        # Lowered edge threshold to better detect handwritten documents
        return edge_ratio > 0.035 or bright_ratio > 0.4
    
    # OpenCV path - optimized for speed and enhanced for handwritten documents
    img_np = np.array(gray_img)
    
    # 1. Fast check: Variance of pixel values
    # Documents typically have high variance (text on background)
    # Handwritten documents may have less contrast than printed text
    std_dev = np.std(img_np)
    if std_dev > 45:  # Lowered threshold to better detect handwritten documents
        return True
        
    # 2. Quick check using downsampled image for edges
    # Downscale for faster processing on large images
    if max(img_np.shape) > 1000:
        scale = 1000 / max(img_np.shape)
        small_img = cv2.resize(img_np, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)
    else:
        small_img = img_np
    
    # Use adaptive edge detection parameters for handwritten documents
    # Lowered threshold to better detect fainter handwritten text
    edges = cv2.Canny(small_img, 30, 130, L2gradient=False)
    edge_ratio = np.count_nonzero(edges) / edges.size
    
    # 3. Fast histogram approximation using bins
    # Instead of calculating full histogram, use bins for dark and light regions
    # Adjusted for handwritten documents which may have more gray values
    dark_mask = img_np < 60  # Increased threshold to capture lighter handwritten text
    light_mask = img_np > 180  # Lowered threshold to account for aged paper
    
    dark_ratio = np.count_nonzero(dark_mask) / img_np.size
    light_ratio = np.count_nonzero(light_mask) / img_np.size
    
    # Special analysis for handwritten documents
    # Check for line-like structures typical in handwritten text
    if CV2_AVAILABLE and edge_ratio > 0.02:  # Lower threshold to capture handwritten documents
        # Try to find line segments that could indicate text lines
        lines = cv2.HoughLinesP(edges, 1, np.pi/180, 
                               threshold=50,  # Lower threshold for detection
                               minLineLength=30,  # Shorter lines for handwriting
                               maxLineGap=20)   # Larger gap for discontinuous handwriting
        
        # If we find enough line segments, it's likely a document with text
        if lines is not None and len(lines) > 10:
            return True
    
    # Combine heuristics for final decision
    # Documents typically have both dark (text) and light (background) regions,
    # and/or well-defined edges
    # Lower thresholds for handwritten documents
    return (dark_ratio > 0.03 and light_ratio > 0.25) or edge_ratio > 0.03

# Removed caching to fix unhashable type error
def preprocess_document_image(img: Image.Image) -> Image.Image:
    """
    Preprocess a document image for optimal OCR.
    
    Args:
        img: PIL Image object
        
    Returns:
        Processed PIL Image
    """
    # Store the image for the implementation function
    preprocess_document_image._current_img = img
    # The actual implementation is separated for cleaner code organization
    return _preprocess_document_image_impl()

def _preprocess_document_image_impl() -> Image.Image:
    """
    Optimized implementation of document preprocessing with adaptive processing based on image size.
    Enhanced for better handwritten document processing and newspaper format.
    """
    # Fast path: Get image from thread-local storage
    if not hasattr(preprocess_document_image, "_current_img"):
        raise ValueError("No image set for document preprocessing")
        
    img = preprocess_document_image._current_img
    
    # Analyze image size to determine processing strategy
    width, height = img.size
    img_size = width * height
    
    # Detect special document types
    is_handwritten = False
    is_newspaper = False
    
    # Check for newspaper format first (takes precedence)
    aspect_ratio = width / height
    if (aspect_ratio > 1.2 and width > 2000) or (width > 3000 or height > 3000):
        is_newspaper = True
        logger.debug(f"Newspaper format detected: {width}x{height}, aspect ratio: {aspect_ratio:.2f}")
    else:
        # If not newspaper, check if handwritten
        try:
            # Simple check for handwritten document characteristics
            # Handwritten documents often have more varied strokes and less stark contrast
            if CV2_AVAILABLE:
                # Convert to grayscale and calculate local variance
                gray_np = np.array(img.convert('L'))
                # Higher variance in edge strengths can indicate handwriting
                edges = cv2.Canny(gray_np, 30, 100)
                if np.count_nonzero(edges) / edges.size > 0.02:  # Low edge threshold for handwriting
                    # Additional check with gradient magnitudes
                    sobelx = cv2.Sobel(gray_np, cv2.CV_64F, 1, 0, ksize=3)
                    sobely = cv2.Sobel(gray_np, cv2.CV_64F, 0, 1, ksize=3)
                    magnitude = np.sqrt(sobelx**2 + sobely**2)
                    # Handwriting typically has more variation in gradient magnitudes
                    if np.std(magnitude) > 20:
                        is_handwritten = True
        except:
            # If detection fails, assume it's not handwritten
            pass
                    
    # Special processing for newspaper format
    if is_newspaper:
        # Convert to grayscale for better text extraction
        gray = img.convert('L')
        
        # For newspapers, we need aggressive text enhancement to make small print readable
        # First enhance contrast more aggressively for newspaper small text
        enhancer = ImageEnhance.Contrast(gray)
        enhanced = enhancer.enhance(2.0)  # More aggressive contrast for newspaper text
        
        # Apply stronger sharpening to make small text more defined
        if IMAGE_PREPROCESSING["sharpen"]:
            # Apply multiple passes of sharpening for newspaper text
            enhanced = enhanced.filter(ImageFilter.SHARPEN)
            enhanced = enhanced.filter(ImageFilter.EDGE_ENHANCE_MORE)  # Stronger edge enhancement
        
        # Enhanced processing for newspapers with OpenCV when available
        if CV2_AVAILABLE:
            try:
                # Convert to numpy array
                img_np = np.array(enhanced)
                
                # For newspaper text extraction, CLAHE (Contrast Limited Adaptive Histogram Equalization)
                # works much better than simple contrast enhancement
                clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
                img_np = clahe.apply(img_np)
                
                # Apply different adaptive thresholding approaches and choose the best one
                
                # 1. Standard adaptive threshold with larger block size for newspaper columns
                binary1 = cv2.adaptiveThreshold(img_np, 255, 
                                            cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                            cv2.THRESH_BINARY, 15, 4)
                
                # 2. Otsu's method for global thresholding - works well for clean newspaper print
                _, binary2 = cv2.threshold(img_np, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
                
                # Try to determine which method preserves text better
                # Count white pixels and edges in each binary version
                white_pixels1 = np.count_nonzero(binary1 > 200)
                white_pixels2 = np.count_nonzero(binary2 > 200)
                
                # Calculate edge density to help determine which preserves text features better
                edges1 = cv2.Canny(binary1, 100, 200)
                edges2 = cv2.Canny(binary2, 100, 200)
                edge_count1 = np.count_nonzero(edges1)
                edge_count2 = np.count_nonzero(edges2)
                
                # For newspaper text, we want to preserve more edges while maintaining reasonable
                # white space (typical of printed text on paper background)
                if (edge_count1 > edge_count2 * 1.2 and white_pixels1 > white_pixels2 * 0.7) or \
                   (white_pixels1 < white_pixels2 * 0.5):  # If Otsu removed too much content
                    # Adaptive thresholding usually better preserves small text in newspapers
                    logger.debug("Using adaptive thresholding for newspaper text")
                    
                    # Apply optional denoising to clean up small speckles
                    result = cv2.fastNlMeansDenoising(binary1, None, 7, 7, 21)
                    return Image.fromarray(result)
                else:
                    # Otsu method was better
                    logger.debug("Using Otsu thresholding for newspaper text")
                    result = cv2.fastNlMeansDenoising(binary2, None, 7, 7, 21)
                    return Image.fromarray(result)
                    
            except Exception as e:
                logger.debug(f"Advanced newspaper processing failed: {str(e)}")
                # Fall back to PIL processing
                pass
        
        # If OpenCV not available or fails, apply additional PIL enhancements
        # Create a more aggressive binary version to better separate text
        binary_threshold = enhanced.point(lambda x: 0 if x < 150 else 255, '1')
        
        # Return enhanced binary image
        return binary_threshold
    
    # Ultra-fast path for tiny images - just convert to grayscale with contrast enhancement
    if img_size < 300000:  # ~500x600 or smaller
        gray = img.convert('L')
        # Lower contrast enhancement for handwritten documents
        contrast_level = 1.4 if is_handwritten else IMAGE_PREPROCESSING["enhance_contrast"]
        enhancer = ImageEnhance.Contrast(gray)
        return enhancer.enhance(contrast_level)
    
    # Fast path for small images - minimal processing
    if img_size < 1000000:  # ~1000x1000 or smaller
        gray = img.convert('L')
        # Use gentler contrast enhancement for handwritten documents
        contrast_level = 1.4 if is_handwritten else IMAGE_PREPROCESSING["enhance_contrast"]
        enhancer = ImageEnhance.Contrast(gray)
        enhanced = enhancer.enhance(contrast_level)
        
        # Light sharpening only if sharpen is enabled
        # Use milder sharpening for handwritten documents to preserve stroke detail
        if IMAGE_PREPROCESSING["sharpen"]:
            if is_handwritten:
                # Use edge enhancement which is gentler than SHARPEN for handwriting
                enhanced = enhanced.filter(ImageFilter.EDGE_ENHANCE)
            else:
                enhanced = enhanced.filter(ImageFilter.SHARPEN)
        return enhanced
    
    # Standard path for medium images
    # Convert to grayscale (faster processing)
    gray = img.convert('L')
    
    # Adaptive contrast enhancement based on document type
    contrast_level = 1.4 if is_handwritten else IMAGE_PREPROCESSING["enhance_contrast"]
    enhancer = ImageEnhance.Contrast(gray)
    enhanced = enhancer.enhance(contrast_level)
    
    # Apply light sharpening for text clarity - adapt based on document type
    if IMAGE_PREPROCESSING["sharpen"]:
        if is_handwritten:
            # Use edge enhancement which is gentler than SHARPEN for handwriting
            enhanced = enhanced.filter(ImageFilter.EDGE_ENHANCE)
        else:
            enhanced = enhanced.filter(ImageFilter.SHARPEN)
    
    # Advanced processing with OpenCV if available
    if CV2_AVAILABLE and IMAGE_PREPROCESSING["denoise"]:
        try:
            # Convert to numpy array for OpenCV processing
            img_np = np.array(enhanced)
            
            if is_handwritten:
                # Special treatment for handwritten documents
                # Use guided filter which preserves edges better than NLMeans
                # Guided filter works well for handwriting by preserving stroke details
                if img_size > 3000000:  # Large images - downsample first
                    scale_factor = 0.5
                    small_img = cv2.resize(img_np, None, fx=scale_factor, fy=scale_factor, 
                                          interpolation=cv2.INTER_AREA)
                    # Apply bilateral filter which preserves edges while smoothing
                    filtered = cv2.bilateralFilter(small_img, 9, 75, 75)
                    # Resize back
                    filtered = cv2.resize(filtered, (width, height), interpolation=cv2.INTER_LINEAR)
                else:
                    # Use bilateral filter directly for smaller images
                    filtered = cv2.bilateralFilter(img_np, 7, 50, 50)
                
                # Convert back to PIL Image
                enhanced = Image.fromarray(filtered)
                
                # For handwritten docs, avoid binary thresholding which can destroy subtle strokes
                return enhanced
                
            else:
                # Standard document processing - optimized for printed text
                # Optimize denoising parameters based on image size
                if img_size > 4000000:  # Very large images
                    # More aggressive downsampling for very large images
                    scale_factor = 0.5
                    downsample = cv2.resize(img_np, None, fx=scale_factor, fy=scale_factor, 
                                          interpolation=cv2.INTER_AREA)
                    
                    # Lighter denoising for downsampled image
                    h_value = 7  # Strength parameter
                    template_window = 5
                    search_window = 13
                    
                    # Apply denoising on smaller image
                    denoised_np = cv2.fastNlMeansDenoising(downsample, None, h_value, template_window, search_window)
                    
                    # Resize back to original size
                    denoised_np = cv2.resize(denoised_np, (width, height), interpolation=cv2.INTER_LINEAR)
                else:
                    # Direct denoising for medium-large images
                    h_value = 8  # Balanced for speed and quality
                    template_window = 5
                    search_window = 15
                    
                    # Apply denoising
                    denoised_np = cv2.fastNlMeansDenoising(img_np, None, h_value, template_window, search_window)
                
                # Convert back to PIL Image
                enhanced = Image.fromarray(denoised_np)
                
                # Apply adaptive thresholding only if it improves text visibility
                # Create a binarized version of the image
                if img_size < 8000000:  # Skip for extremely large images to save processing time
                    binary = cv2.adaptiveThreshold(denoised_np, 255, 
                                                 cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                                 cv2.THRESH_BINARY, 11, 2)
                    
                    # Quick verification that binarization preserves text information
                    # Use simplified check that works well for document images
                    white_pixels_binary = np.count_nonzero(binary > 200)
                    white_pixels_orig = np.count_nonzero(denoised_np > 200)
                    
                    # Check if binary preserves reasonable amount of white pixels (background)
                    if white_pixels_binary > white_pixels_orig * 0.8:
                        # Binarization looks good, use it
                        return Image.fromarray(binary)
                
                return enhanced
                
        except Exception as e:
            # If OpenCV processing fails, continue with PIL-enhanced image
            pass
            
    elif IMAGE_PREPROCESSING["denoise"]:
        # Fallback PIL denoising for systems without OpenCV
        if is_handwritten:
            # Lighter filtering for handwritten text to preserve details
            # Use a smaller median filter for handwritten documents
            enhanced = enhanced.filter(ImageFilter.MedianFilter(1))
        else:
            # Standard filtering for printed documents
            enhanced = enhanced.filter(ImageFilter.MedianFilter(3))
    
    # Return enhanced grayscale image
    return enhanced

# Removed caching to fix unhashable type error
def preprocess_general_image(img: Image.Image) -> Image.Image:
    """
    Preprocess a general image for OCR.
    
    Args:
        img: PIL Image object
        
    Returns:
        Processed PIL Image
    """
    # Store the image for implementation function
    preprocess_general_image._current_img = img
    return _preprocess_general_image_impl()

def _preprocess_general_image_impl() -> Image.Image:
    """
    Optimized implementation of general image preprocessing with size-based processing paths
    """
    # Fast path: Get the image from thread-local storage
    if not hasattr(preprocess_general_image, "_current_img"):
        raise ValueError("No image set for general preprocessing")
        
    img = preprocess_general_image._current_img
    
    # Ultra-fast path: Skip processing completely for small images to improve performance
    width, height = img.size
    img_size = width * height
    if img_size < 300000:  # Skip for tiny images under ~0.3 megapixel
        # Just ensure correct color mode
        if img.mode != 'RGB':
            return img.convert('RGB')
        return img
    
    # Fast path: Minimal processing for smaller images
    if img_size < 600000:  # ~800x750 or smaller
        # Ensure RGB mode
        if img.mode != 'RGB':
            img = img.convert('RGB')
        
        # Very light contrast enhancement only
        enhancer = ImageEnhance.Contrast(img)
        return enhancer.enhance(1.15)  # Lighter enhancement for small images
    
    # Standard path: Apply moderate enhancements for medium images
    # Convert to RGB to ensure compatibility
    if img.mode != 'RGB':
        img = img.convert('RGB')
    
    # Moderate enhancement only
    enhancer = ImageEnhance.Contrast(img)
    enhanced = enhancer.enhance(1.2)  # Less aggressive than document enhancement
    
    # Skip additional processing for medium-sized images
    if img_size < 1000000:  # Skip for images under ~1 megapixel
        return enhanced
    
    # Enhanced path: Additional processing for larger images
    try:
        # Apply optimized enhancement pipeline for large non-document images
        
        # 1. Improve color saturation slightly for better feature extraction
        saturation = ImageEnhance.Color(enhanced)
        enhanced = saturation.enhance(1.1)
        
        # 2. Apply adaptive sharpening based on image size
        if img_size > 2500000:  # Very large images (~1600x1600 or larger)
            # Use EDGE_ENHANCE instead of SHARPEN for more subtle enhancement on large images
            enhanced = enhanced.filter(ImageFilter.EDGE_ENHANCE)
        else:
            # Standard sharpening for regular large images
            enhanced = enhanced.filter(ImageFilter.SHARPEN)
        
        # 3. Apply additional processing with OpenCV if available (for largest images)
        if CV2_AVAILABLE and img_size > 3000000:
            # Convert to numpy array
            img_np = np.array(enhanced)
            
            # Apply subtle enhancement of details (CLAHE)
            try:
                # Convert to LAB color space for better processing
                lab = cv2.cvtColor(img_np, cv2.COLOR_RGB2LAB)
                
                # Only enhance the L channel (luminance)
                l, a, b = cv2.split(lab)
                
                # Create CLAHE object with optimal parameters for photos
                clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
                
                # Apply CLAHE to L channel
                l = clahe.apply(l)
                
                # Merge channels back and convert to RGB
                lab = cv2.merge((l, a, b))
                enhanced_np = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
                
                # Convert back to PIL
                enhanced = Image.fromarray(enhanced_np)
            except:
                # If CLAHE fails, continue with PIL-enhanced image
                pass
    
    except Exception:
        # If any enhancement fails, fall back to basic contrast enhancement
        if img.mode != 'RGB':
            img = img.convert('RGB')
        enhancer = ImageEnhance.Contrast(img)
        enhanced = enhancer.enhance(1.2)
    
    return enhanced

# Removed caching decorator to fix unhashable type error
def resize_image(img: Image.Image, target_dpi: int = 300) -> Image.Image:
    """
    Resize an image to an optimal size for OCR while preserving quality.
    
    Args:
        img: PIL Image object
        target_dpi: Target DPI (dots per inch)
        
    Returns:
        Resized PIL Image
    """
    # Store the image for implementation function
    resize_image._current_img = img
    return resize_image_impl(target_dpi)

def resize_image_impl(target_dpi: int = 300) -> Image.Image:
    """
    Implementation of resize function that uses thread-local storage.
    
    Args:
        target_dpi: Target DPI (dots per inch)
        
    Returns:
        Resized PIL Image
    """
    # Get the image from thread-local storage (set by the caller)
    if not hasattr(resize_image, "_current_img"):
        raise ValueError("No image set for resizing")
        
    img = resize_image._current_img
    
    # Calculate current dimensions
    width, height = img.size
    
    # Fixed target dimensions based on DPI
    # Using larger dimensions to support newspapers and large documents
    max_width = int(14 * target_dpi)  # Increased from 8.5 to 14 inches
    max_height = int(22 * target_dpi)  # Increased from 11 to 22 inches
    
    # Check if resizing is needed - quick early return
    if width <= max_width and height <= max_height:
        return img  # No resizing needed
    
    # Calculate scaling factor once
    scale_factor = min(max_width / width, max_height / height)
    
    # Calculate new dimensions
    new_width = int(width * scale_factor)
    new_height = int(height * scale_factor)
    
    # Use BICUBIC for better balance of speed and quality
    return img.resize((new_width, new_height), Image.BICUBIC)

def calculate_image_entropy(img: Image.Image) -> float:
    """
    Calculate the entropy (information content) of an image.
    
    Args:
        img: PIL Image object
        
    Returns:
        Entropy value
    """
    # Convert to grayscale
    if img.mode != 'L':
        img = img.convert('L')
    
    # Calculate histogram
    histogram = img.histogram()
    total_pixels = img.width * img.height
    
    # Calculate entropy
    entropy = 0
    for h in histogram:
        if h > 0:
            probability = h / total_pixels
            entropy -= probability * np.log2(probability)
    
    return entropy

def create_html_with_images(result):
    """
    Create an HTML document with embedded images from OCR results.
    Handles serialization of complex OCR objects automatically.
    
    Args:
        result: OCR result dictionary containing pages_data
        
    Returns:
        HTML content as string
    """
    # Ensure result is fully serializable first
    result = serialize_ocr_object(result)
    # Create HTML document structure
    html_content = """
    <!DOCTYPE html>
    <html>
    <head>
        <meta charset="UTF-8">
        <meta name="viewport" content="width=device-width, initial-scale=1.0">
        <title>OCR Document with Images</title>
        <style>
            body { 
                font-family: Georgia, serif; 
                line-height: 1.7; 
                margin: 0 auto;
                max-width: 800px;
                padding: 20px;
            }
            img { 
                max-width: 90%; 
                max-height: 500px;
                object-fit: contain;
                margin: 20px auto; 
                display: block;
                border: 1px solid #ddd;
                border-radius: 4px;
            }
            .image-container {
                margin: 20px 0;
                text-align: center;
            }
            .page-break {
                border-top: 1px solid #ddd;
                margin: 40px 0;
                padding-top: 40px;
            }
            h3 {
                color: #333;
                border-bottom: 1px solid #eee;
                padding-bottom: 10px;
            }
            p {
                margin: 12px 0;
            }
            .page-text-content {
                margin-bottom: 20px;
            }
            .text-block {
                background-color: #f9f9f9;
                padding: 15px;
                border-radius: 4px;
                border-left: 3px solid #546e7a;
                margin-bottom: 15px;
                color: #333;
            }
            .text-block p {
                margin: 8px 0;
                color: #333;
            }
            .metadata {
                background-color: #f5f5f5;
                padding: 10px 15px;
                border-radius: 4px;
                margin-bottom: 20px;
                font-size: 14px;
            }
            .metadata p {
                margin: 5px 0;
            }
        </style>
    </head>
    <body>
    """
    
    # Add document metadata
    html_content += f"""
    <div class="metadata">
        <h2>{result.get('file_name', 'Document')}</h2>
        <p><strong>Processed at:</strong> {result.get('timestamp', '')}</p>
        <p><strong>Languages:</strong> {', '.join(result.get('languages', ['Unknown']))}</p>
        <p><strong>Topics:</strong> {', '.join(result.get('topics', ['Unknown']))}</p>
    </div>
    """
    
    # Check if we have pages_data
    if 'pages_data' in result and result['pages_data']:
        pages_data = result['pages_data']
        
        # Process each page
        for i, page in enumerate(pages_data):
            page_markdown = page.get('markdown', '')
            images = page.get('images', [])
            
            # Add page header if multi-page
            if len(pages_data) > 1:
                html_content += f"<h3>Page {i+1}</h3>"
            
            # Create image dictionary
            image_dict = {}
            for img in images:
                if 'id' in img and 'image_base64' in img:
                    image_dict[img['id']] = img['image_base64']
            
            # Process the markdown content
            if page_markdown:
                # Extract text content (lines without images)
                text_content = []
                image_lines = []
                
                for line in page_markdown.split('\n'):
                    if '![' in line and '](' in line:
                        image_lines.append(line)
                    elif line.strip():
                        text_content.append(line)
                
                # Add text content
                if text_content:
                    html_content += '<div class="text-block">'
                    for line in text_content:
                        html_content += f"<p>{line}</p>"
                    html_content += '</div>'
                
                # Add images
                for line in image_lines:
                    # Extract image ID and alt text using simple parsing
                    try:
                        alt_start = line.find('![') + 2
                        alt_end = line.find(']', alt_start)
                        alt_text = line[alt_start:alt_end]
                        
                        img_start = line.find('(', alt_end) + 1
                        img_end = line.find(')', img_start)
                        img_id = line[img_start:img_end]
                        
                        if img_id in image_dict:
                            html_content += f'<div class="image-container">'
                            html_content += f'<img src="{image_dict[img_id]}" alt="{alt_text}">'
                            html_content += f'</div>'
                    except:
                        # If parsing fails, just skip this image
                        continue
            
            # Add page separator if not the last page
            if i < len(pages_data) - 1:
                html_content += '<div class="page-break"></div>'
    
    # Add structured content if available
    if 'ocr_contents' in result and isinstance(result['ocr_contents'], dict):
        html_content += '<h3>Structured Content</h3>'
        
        for section, content in result['ocr_contents'].items():
            if content and section not in ['error', 'raw_text', 'partial_text']:
                html_content += f'<h4>{section.replace("_", " ").title()}</h4>'
                
                if isinstance(content, str):
                    html_content += f'<p>{content}</p>'
                elif isinstance(content, list):
                    html_content += '<ul>'
                    for item in content:
                        html_content += f'<li>{str(item)}</li>'
                    html_content += '</ul>'
                elif isinstance(content, dict):
                    html_content += '<dl>'
                    for k, v in content.items():
                        html_content += f'<dt>{k}</dt><dd>{v}</dd>'
                    html_content += '</dl>'
    
    # Close HTML document
    html_content += """
    </body>
    </html>
    """
    
    return html_content

def generate_document_thumbnail(image_path: Union[str, Path], max_size: int = 300) -> str:
    """
    Generate a thumbnail for document preview.
    
    Args:
        image_path: Path to the image file
        max_size: Maximum dimension for thumbnail
        
    Returns:
        Base64 encoded thumbnail
    """
    if not PILLOW_AVAILABLE:
        return None
    
    try:
        # Open the image
        with Image.open(image_path) as img:
            # Calculate thumbnail size preserving aspect ratio
            width, height = img.size
            if width > height:
                new_width = max_size
                new_height = int(height * (max_size / width))
            else:
                new_height = max_size
                new_width = int(width * (max_size / height))
            
            # Create thumbnail
            thumbnail = img.resize((new_width, new_height), Image.LANCZOS)
            
            # Save to buffer
            buffer = io.BytesIO()
            thumbnail.save(buffer, format="JPEG", quality=85)
            buffer.seek(0)
            
            # Encode as base64
            encoded = base64.b64encode(buffer.getvalue()).decode()
            return f"data:image/jpeg;base64,{encoded}"
    except Exception:
        # Return None if thumbnail generation fails
        return None

def serialize_ocr_object(obj):
    """
    Serialize OCR response objects to JSON serializable format.
    Handles OCRImageObject specifically to prevent serialization errors.
    
    Args:
        obj: The object to serialize
        
    Returns:
        JSON serializable representation of the object
    """
    # Fast path: Handle primitive types directly
    if obj is None or isinstance(obj, (str, int, float, bool)):
        return obj
        
    # Handle collections
    if isinstance(obj, list):
        return [serialize_ocr_object(item) for item in obj]
    elif isinstance(obj, dict):
        return {k: serialize_ocr_object(v) for k, v in obj.items()}
    elif isinstance(obj, OCRImageObject):
        # Special handling for OCRImageObject
        return {
            'id': obj.id if hasattr(obj, 'id') else None,
            'image_base64': obj.image_base64 if hasattr(obj, 'image_base64') else None
        }
    elif hasattr(obj, '__dict__'):
        # For objects with __dict__ attribute
        return {k: serialize_ocr_object(v) for k, v in obj.__dict__.items() 
                if not k.startswith('_')}  # Skip private attributes
    else:
        # Try to convert to string as last resort
        try:
            return str(obj)
        except:
            return None

def try_local_ocr_fallback(image_path: Union[str, Path], base64_data_url: str = None) -> str:
    """
    Attempt to use local pytesseract OCR as a fallback when API fails
    
    Args:
        image_path: Path to the image file
        base64_data_url: Optional base64 data URL if already available
        
    Returns:
        OCR text string if successful, None if failed
    """
    logger.info("Attempting local OCR fallback using pytesseract...")
    
    try:
        import pytesseract
        from PIL import Image
        
        # Load image - either from path or from base64
        if base64_data_url and base64_data_url.startswith('data:image'):
            # Extract image from base64
            image_data = base64_data_url.split(',', 1)[1]
            image_bytes = base64.b64decode(image_data)
            image = Image.open(io.BytesIO(image_bytes))
        else:
            # Load from file path
            image_path = Path(image_path) if isinstance(image_path, str) else image_path
            image = Image.open(image_path)
        
        # Convert to RGB if not already (pytesseract works best with RGB)
        if image.mode != 'RGB':
            image = image.convert('RGB')
            
        # Apply image enhancements for better OCR
        # Convert to grayscale for better text recognition
        image = image.convert('L')
        
        # Enhance contrast
        enhancer = ImageEnhance.Contrast(image)
        image = enhancer.enhance(2.0)  # Higher contrast for better OCR
        
        # Run OCR
        ocr_text = pytesseract.image_to_string(image, lang='eng')
        
        if ocr_text and len(ocr_text.strip()) > 50:
            logger.info(f"Local OCR successful: extracted {len(ocr_text)} characters")
            return ocr_text
        else:
            logger.warning("Local OCR produced minimal or no text")
            return None
    except ImportError:
        logger.warning("Pytesseract not installed - local OCR not available")
        return None
    except Exception as e:
        logger.error(f"Local OCR fallback failed: {str(e)}")
        return None