OCR/backend/app/services/pdf_generator_service.py

"""
Layout-Preserving PDF Generation Service
Generates PDF files that preserve the original document layout using OCR JSON data
"""

import json
import logging
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
from datetime import datetime

from reportlab.lib.pagesizes import A4, letter
from reportlab.lib.units import mm
from reportlab.pdfgen import canvas
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
from reportlab.platypus import Table, TableStyle
from reportlab.lib import colors
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_RIGHT
from reportlab.platypus import Paragraph
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from PIL import Image
from html.parser import HTMLParser

from app.core.config import settings

# Import UnifiedDocument for dual-track support
try:
    from app.models.unified_document import (
        UnifiedDocument, DocumentElement, ElementType,
        BoundingBox, TableData, ProcessingTrack
    )
    UNIFIED_DOCUMENT_AVAILABLE = True
except ImportError:
    UNIFIED_DOCUMENT_AVAILABLE = False
    UnifiedDocument = None

logger = logging.getLogger(__name__)


class HTMLTableParser(HTMLParser):
    """Parse HTML table to extract structure and data"""

    def __init__(self):
        super().__init__()
        self.tables = []
        self.current_table = None
        self.current_row = None
        self.current_cell = None
        self.in_table = False

    def handle_starttag(self, tag, attrs):
        attrs_dict = dict(attrs)

        if tag == 'table':
            self.in_table = True
            self.current_table = {'rows': []}

        elif tag == 'tr' and self.in_table:
            self.current_row = {'cells': []}

        elif tag in ('td', 'th') and self.in_table and self.current_row is not None:
            colspan = int(attrs_dict.get('colspan', 1))
            rowspan = int(attrs_dict.get('rowspan', 1))
            self.current_cell = {
                'text': '',
                'is_header': tag == 'th',
                'colspan': colspan,
                'rowspan': rowspan
            }

    def handle_endtag(self, tag):
        if tag == 'table' and self.in_table:
            if self.current_table and self.current_table['rows']:
                self.tables.append(self.current_table)
            self.current_table = None
            self.in_table = False

        elif tag == 'tr' and self.current_row is not None:
            if self.current_table is not None:
                self.current_table['rows'].append(self.current_row)
            self.current_row = None

        elif tag in ('td', 'th') and self.current_cell is not None:
            if self.current_row is not None:
                self.current_row['cells'].append(self.current_cell)
            self.current_cell = None

    def handle_data(self, data):
        if self.current_cell is not None:
            self.current_cell['text'] += data.strip() + ' '


class PDFGeneratorService:
    """Service for generating layout-preserving PDFs from OCR JSON data"""

    # Font mapping from common fonts to PDF standard fonts
    FONT_MAPPING = {
        'Arial': 'Helvetica',
        'Arial Black': 'Helvetica-Bold',
        'Times New Roman': 'Times-Roman',
        'Times': 'Times-Roman',
        'Courier New': 'Courier',
        'Courier': 'Courier',
        'Calibri': 'Helvetica',
        'Cambria': 'Times-Roman',
        'Georgia': 'Times-Roman',
        'Verdana': 'Helvetica',
        'Tahoma': 'Helvetica',
        'Trebuchet MS': 'Helvetica',
        'Comic Sans MS': 'Helvetica',
        'Impact': 'Helvetica-Bold',
        'Lucida Console': 'Courier',
        'Palatino': 'Times-Roman',
        'Garamond': 'Times-Roman',
        'Bookman': 'Times-Roman',
        'Century Gothic': 'Helvetica',
        'Franklin Gothic': 'Helvetica',
    }

    # Style flags for text formatting
    STYLE_FLAG_BOLD = 1
    STYLE_FLAG_ITALIC = 2
    STYLE_FLAG_UNDERLINE = 4
    STYLE_FLAG_STRIKETHROUGH = 8

    def __init__(self):
        """Initialize PDF generator with font configuration"""
        self.font_name = 'NotoSansSC'
        self.font_path = None
        self.font_registered = False
        self.current_processing_track = None  # Track type for current document

        self._register_chinese_font()

    def _register_chinese_font(self):
        """Register Chinese font for PDF generation"""
        try:
            # Get font path from settings
            font_path = Path(settings.chinese_font_path)

            # Try relative path from project root
            if not font_path.is_absolute():
                # Adjust path - settings.chinese_font_path starts with ./backend/
                project_root = Path(__file__).resolve().parent.parent.parent.parent
                font_path = project_root / font_path

            if not font_path.exists():
                logger.error(f"Chinese font not found at {font_path}")
                return

            # Register font
            pdfmetrics.registerFont(TTFont(self.font_name, str(font_path)))
            self.font_path = font_path
            self.font_registered = True
            logger.info(f"Chinese font registered: {self.font_name} from {font_path}")

        except Exception as e:
            logger.error(f"Failed to register Chinese font: {e}")
            self.font_registered = False

    def _parse_color(self, color_value) -> Tuple[float, float, float]:
        """
        Parse color value to RGB tuple.

        Args:
            color_value: Color as hex string (#RRGGBB), RGB tuple, or color name

        Returns:
            RGB tuple with values 0-1 for ReportLab
        """
        if not color_value:
            return (0, 0, 0)  # Default to black

        try:
            # Handle hex color (#RRGGBB or #RGB)
            if isinstance(color_value, str) and color_value.startswith('#'):
                hex_color = color_value.lstrip('#')

                # Expand short form (#RGB -> #RRGGBB)
                if len(hex_color) == 3:
                    hex_color = ''.join([c*2 for c in hex_color])

                if len(hex_color) == 6:
                    r = int(hex_color[0:2], 16) / 255.0
                    g = int(hex_color[2:4], 16) / 255.0
                    b = int(hex_color[4:6], 16) / 255.0
                    return (r, g, b)

            # Handle RGB tuple or list
            elif isinstance(color_value, (tuple, list)) and len(color_value) >= 3:
                r, g, b = color_value[0:3]
                # Normalize to 0-1 if values are 0-255
                if any(v > 1 for v in [r, g, b]):
                    return (r/255.0, g/255.0, b/255.0)
                return (r, g, b)

        except (ValueError, TypeError) as e:
            logger.warning(f"Failed to parse color {color_value}: {e}")

        # Default to black
        return (0, 0, 0)

    def _map_font(self, font_name: Optional[str]) -> str:
        """
        Map font name to PDF standard font.

        Args:
            font_name: Original font name

        Returns:
            PDF standard font name
        """
        if not font_name:
            return 'Helvetica'

        # Direct lookup
        if font_name in self.FONT_MAPPING:
            return self.FONT_MAPPING[font_name]

        # Case-insensitive lookup
        font_lower = font_name.lower()
        for orig_font, pdf_font in self.FONT_MAPPING.items():
            if orig_font.lower() == font_lower:
                return pdf_font

        # Partial match for common patterns
        if 'arial' in font_lower:
            return 'Helvetica'
        elif 'times' in font_lower:
            return 'Times-Roman'
        elif 'courier' in font_lower:
            return 'Courier'

        # Default fallback
        logger.debug(f"Font '{font_name}' not found in mapping, using Helvetica")
        return 'Helvetica'

    def _apply_text_style(self, c: canvas.Canvas, style_info, default_size: float = 12):
        """
        Apply text styling from StyleInfo to PDF canvas.

        Args:
            c: ReportLab canvas object
            style_info: StyleInfo object or dict with font, size, color, flags
            default_size: Default font size if not specified
        """
        if not style_info:
            # Apply default styling
            c.setFont('Helvetica', default_size)
            c.setFillColorRGB(0, 0, 0)
            return

        try:
            # Extract style attributes
            if hasattr(style_info, '__dict__'):
                # StyleInfo object
                font_family = getattr(style_info, 'font', None)
                font_size = getattr(style_info, 'size', default_size)
                color = getattr(style_info, 'color', None)
                flags = getattr(style_info, 'flags', 0)
            elif isinstance(style_info, dict):
                # Dictionary
                font_family = style_info.get('font')
                font_size = style_info.get('size', default_size)
                color = style_info.get('color')
                flags = style_info.get('flags', 0)
            else:
                # Unknown format, use defaults
                c.setFont('Helvetica', default_size)
                c.setFillColorRGB(0, 0, 0)
                return

            # Map font name
            base_font = self._map_font(font_family) if font_family else 'Helvetica'

            # Apply bold/italic modifiers
            if flags:
                is_bold = bool(flags & self.STYLE_FLAG_BOLD)
                is_italic = bool(flags & self.STYLE_FLAG_ITALIC)

                if is_bold and is_italic:
                    # Try bold-italic variant
                    if 'Helvetica' in base_font:
                        base_font = 'Helvetica-BoldOblique'
                    elif 'Times' in base_font:
                        base_font = 'Times-BoldItalic'
                    elif 'Courier' in base_font:
                        base_font = 'Courier-BoldOblique'
                elif is_bold:
                    # Try bold variant
                    if 'Helvetica' in base_font:
                        base_font = 'Helvetica-Bold'
                    elif 'Times' in base_font:
                        base_font = 'Times-Bold'
                    elif 'Courier' in base_font:
                        base_font = 'Courier-Bold'
                elif is_italic:
                    # Try italic variant
                    if 'Helvetica' in base_font:
                        base_font = 'Helvetica-Oblique'
                    elif 'Times' in base_font:
                        base_font = 'Times-Italic'
                    elif 'Courier' in base_font:
                        base_font = 'Courier-Oblique'

            # Apply font and size
            actual_size = font_size if font_size and font_size > 0 else default_size
            try:
                c.setFont(base_font, actual_size)
            except KeyError:
                # Font not available, fallback
                logger.warning(f"Font '{base_font}' not available, using Helvetica")
                c.setFont('Helvetica', actual_size)

            # Apply color
            if color:
                r, g, b = self._parse_color(color)
                c.setFillColorRGB(r, g, b)
            else:
                c.setFillColorRGB(0, 0, 0)  # Default black

        except Exception as e:
            logger.error(f"Failed to apply text style: {e}")
            # Fallback to defaults
            c.setFont('Helvetica', default_size)
            c.setFillColorRGB(0, 0, 0)

    def load_ocr_json(self, json_path: Path) -> Optional[Dict]:
        """
        Load and parse OCR JSON result file

        Args:
            json_path: Path to JSON file

        Returns:
            Parsed JSON data or None if failed
        """
        try:
            with open(json_path, 'r', encoding='utf-8') as f:
                data = json.load(f)

            logger.info(f"Loaded OCR JSON: {json_path.name}")
            return data

        except Exception as e:
            logger.error(f"Failed to load JSON {json_path}: {e}")
            return None

    def _get_image_path(self, element) -> Optional[str]:
        """
        Get image path with fallback logic.

        Checks multiple locations in order:
        1. element.content["saved_path"] - Direct track saved path
        2. element.content["path"] - Legacy path
        3. element.content["image_path"] - Alternative path
        4. element.saved_path - Direct attribute
        5. element.metadata["path"] - Metadata fallback

        Args:
            element: DocumentElement object

        Returns:
            Path to image file or None if not found
        """
        # Check content dictionary
        if isinstance(element.content, dict):
            for key in ['saved_path', 'path', 'image_path']:
                if key in element.content:
                    return element.content[key]

        # Check direct attribute
        if hasattr(element, 'saved_path') and element.saved_path:
            return element.saved_path

        # Check metadata
        if element.metadata and isinstance(element.metadata, dict):
            if 'path' in element.metadata:
                return element.metadata['path']
            if 'saved_path' in element.metadata:
                return element.metadata['saved_path']

        return None

    def convert_unified_document_to_ocr_data(self, unified_doc: 'UnifiedDocument') -> Dict:
        """
        Convert UnifiedDocument to OCR data format for PDF generation.

        This method transforms the UnifiedDocument structure into the legacy
        OCR data format that the PDF generator expects, supporting both
        OCR and DIRECT processing tracks.

        Args:
            unified_doc: UnifiedDocument object from either processing track

        Returns:
            Dictionary in OCR data format with text_regions, images_metadata, layout_data
        """
        text_regions = []
        images_metadata = []
        layout_elements = []

        for page in unified_doc.pages:
            page_num = page.page_number  # 1-based

            for element in page.elements:
                # Convert BoundingBox to polygon format [[x,y], [x,y], [x,y], [x,y]]
                bbox_polygon = [
                    [element.bbox.x0, element.bbox.y0],  # top-left
                    [element.bbox.x1, element.bbox.y0],  # top-right
                    [element.bbox.x1, element.bbox.y1],  # bottom-right
                    [element.bbox.x0, element.bbox.y1],  # bottom-left
                ]

                # Handle text elements
                if element.is_text or element.type in [
                    ElementType.TEXT, ElementType.TITLE, ElementType.HEADER,
                    ElementType.FOOTER, ElementType.PARAGRAPH, ElementType.CAPTION,
                    ElementType.LIST_ITEM, ElementType.FOOTNOTE, ElementType.REFERENCE
                ]:
                    text_content = element.get_text()
                    if text_content:
                        text_region = {
                            'text': text_content,
                            'bbox': bbox_polygon,
                            'confidence': element.confidence or 1.0,
                            'page': page_num
                        }

                        # Include style information if available (for Direct track)
                        if hasattr(element, 'style') and element.style:
                            text_region['style'] = element.style

                        text_regions.append(text_region)

                # Handle table elements
                elif element.type == ElementType.TABLE:
                    # Convert TableData to HTML for layout_data
                    if isinstance(element.content, TableData):
                        html_content = element.content.to_html()
                    elif isinstance(element.content, dict):
                        html_content = element.content.get('html', str(element.content))
                    else:
                        html_content = str(element.content)

                    layout_elements.append({
                        'type': 'table',
                        'content': html_content,
                        'bbox': [element.bbox.x0, element.bbox.y0,
                                element.bbox.x1, element.bbox.y1],
                        'page': page_num - 1  # layout uses 0-based
                    })

                    # Add bbox to images_metadata for text overlap filtering
                    # (no actual image file, just bbox for filtering)
                    images_metadata.append({
                        'image_path': None,  # No fake table image
                        'bbox': bbox_polygon,
                        'page': page_num - 1,  # 0-based for images_metadata
                        'type': 'table',
                        'element_id': element.element_id
                    })

                # Handle image/visual elements
                elif element.is_visual or element.type in [
                    ElementType.IMAGE, ElementType.FIGURE, ElementType.CHART,
                    ElementType.DIAGRAM, ElementType.LOGO
                ]:
                    # Get image path using fallback logic
                    image_path = self._get_image_path(element)

                    # Only add if we found a valid path
                    if image_path:
                        images_metadata.append({
                            'image_path': image_path,
                            'bbox': bbox_polygon,
                            'page': page_num - 1,  # 0-based
                            'type': element.type.value
                        })
                        logger.debug(f"Found image path: {image_path} for element {element.element_id}")
                    else:
                        logger.warning(f"No image path found for visual element {element.element_id}")

        # Build OCR data structure
        ocr_data = {
            'text_regions': text_regions,
            'images_metadata': images_metadata,
            'layout_data': {
                'elements': layout_elements,
                'total_elements': len(layout_elements)
            },
            'total_pages': unified_doc.page_count,
            'ocr_dimensions': {
                'width': unified_doc.pages[0].dimensions.width if unified_doc.pages else 0,
                'height': unified_doc.pages[0].dimensions.height if unified_doc.pages else 0
            },
            # Metadata for tracking
            '_from_unified_document': True,
            '_processing_track': unified_doc.metadata.processing_track.value
        }

        logger.info(f"Converted UnifiedDocument to OCR data: "
                   f"{len(text_regions)} text regions, "
                   f"{len(images_metadata)} images, "
                   f"{len(layout_elements)} layout elements, "
                   f"track={unified_doc.metadata.processing_track.value}")

        return ocr_data

    def generate_from_unified_document(
        self,
        unified_doc: 'UnifiedDocument',
        output_path: Path,
        source_file_path: Optional[Path] = None
    ) -> bool:
        """
        Generate layout-preserving PDF directly from UnifiedDocument.

        This method supports both OCR and DIRECT processing tracks,
        preserving layout and coordinate information from either source.

        Args:
            unified_doc: UnifiedDocument object
            output_path: Path to save generated PDF
            source_file_path: Optional path to original source file

        Returns:
            True if successful, False otherwise
        """
        if not UNIFIED_DOCUMENT_AVAILABLE:
            logger.error("UnifiedDocument support not available")
            return False

        try:
            # Detect processing track for track-specific rendering
            self.current_processing_track = None
            if hasattr(unified_doc, 'metadata') and unified_doc.metadata:
                if hasattr(unified_doc.metadata, 'processing_track'):
                    self.current_processing_track = unified_doc.metadata.processing_track
                    logger.info(f"Processing track detected: {self.current_processing_track}")
                elif isinstance(unified_doc.metadata, dict):
                    self.current_processing_track = unified_doc.metadata.get('processing_track')
                    logger.info(f"Processing track detected: {self.current_processing_track}")

            # Convert UnifiedDocument to OCR data format
            ocr_data = self.convert_unified_document_to_ocr_data(unified_doc)

            # Use internal generation with pre-loaded data
            result = self._generate_pdf_from_data(
                ocr_data=ocr_data,
                output_path=output_path,
                source_file_path=source_file_path
            )

            # Reset track after generation
            self.current_processing_track = None
            return result

        except Exception as e:
            logger.error(f"Failed to generate PDF from UnifiedDocument: {e}")
            import traceback
            traceback.print_exc()
            self.current_processing_track = None
            return False

    def _generate_pdf_from_data(
        self,
        ocr_data: Dict,
        output_path: Path,
        source_file_path: Optional[Path] = None,
        json_parent_dir: Optional[Path] = None
    ) -> bool:
        """
        Internal method to generate PDF from OCR data dictionary.

        This is the core generation logic extracted for reuse by both
        JSON-based and UnifiedDocument-based generation paths.

        Args:
            ocr_data: OCR data dictionary
            output_path: Path to save generated PDF
            source_file_path: Optional path to original source file
            json_parent_dir: Directory containing images (for JSON-based generation)

        Returns:
            True if successful, False otherwise
        """
        try:
            # Check if PDF already exists (caching)
            if output_path.exists():
                logger.info(f"PDF already exists: {output_path.name}")
                return True

            # Get text regions
            text_regions = ocr_data.get('text_regions', [])
            if not text_regions:
                logger.warning("No text regions found in data")
                # Don't fail - might have only tables/images

            # Get images metadata
            images_metadata = ocr_data.get('images_metadata', [])

            # Get layout data
            layout_data = ocr_data.get('layout_data', {})

            # Step 1: Get OCR processing dimensions
            ocr_width, ocr_height = self.calculate_page_dimensions(ocr_data, source_file_path=None)
            logger.info(f"OCR 處理時使用的座標系尺寸: {ocr_width:.1f} x {ocr_height:.1f}")

            # Step 2: Get target PDF dimensions
            if source_file_path:
                target_dims = self.get_original_page_size(source_file_path)
                if target_dims:
                    target_width, target_height = target_dims
                    logger.info(f"目標 PDF 尺寸（來自原始文件）: {target_width:.1f} x {target_height:.1f}")
                else:
                    target_width, target_height = ocr_width, ocr_height
                    logger.warning(f"無法獲取原始文件尺寸，使用 OCR 尺寸作為目標")
            else:
                target_width, target_height = ocr_width, ocr_height
                logger.info(f"無原始文件，使用 OCR 尺寸作為目標: {target_width:.1f} x {target_height:.1f}")

            # Step 3: Calculate scale factors
            scale_w = target_width / ocr_width if ocr_width > 0 else 1.0
            scale_h = target_height / ocr_height if ocr_height > 0 else 1.0
            logger.info(f"縮放因子: X={scale_w:.3f}, Y={scale_h:.3f}")

            # Create PDF canvas
            pdf_canvas = canvas.Canvas(str(output_path), pagesize=(target_width, target_height))

            # Filter text regions to avoid overlap with tables/images
            regions_to_avoid = images_metadata
            table_count = len([img for img in images_metadata if img.get('type') == 'table'])

            logger.info(f"過濾文字區域: {len(regions_to_avoid)} 個區域需要避免 (含 {table_count} 個表格)")

            filtered_text_regions = self._filter_text_in_regions(text_regions, regions_to_avoid)

            # Group regions by page
            pages_data = {}
            for region in filtered_text_regions:
                page_num = region.get('page', 1)
                if page_num not in pages_data:
                    pages_data[page_num] = []
                pages_data[page_num].append(region)

            # Get table elements from layout_data
            table_elements = []
            if layout_data and layout_data.get('elements'):
                table_elements = [e for e in layout_data['elements'] if e.get('type') == 'table']

            # Process each page
            total_pages = ocr_data.get('total_pages', 1)
            logger.info(f"開始處理 {total_pages} 頁 PDF")

            # Determine image directory
            if json_parent_dir is None:
                json_parent_dir = output_path.parent

            for page_num in range(1, total_pages + 1):
                logger.info(f">>> 處理第 {page_num}/{total_pages} 頁")
                if page_num > 1:
                    pdf_canvas.showPage()

                # Get regions for this page
                page_text_regions = pages_data.get(page_num, [])
                page_table_regions = [t for t in table_elements if t.get('page') == page_num - 1]
                page_image_regions = [
                    img for img in images_metadata
                    if img.get('page') == page_num - 1
                    and img.get('type') != 'table'
                    and img.get('image_path') is not None  # Skip table placeholders
                ]

                # Draw in layers: images → tables → text

                # 1. Draw images (bottom layer)
                for img_meta in page_image_regions:
                    self.draw_image_region(
                        pdf_canvas, img_meta, target_height,
                        json_parent_dir, scale_w, scale_h
                    )

                # 2. Draw tables (middle layer)
                for table_elem in page_table_regions:
                    self.draw_table_region(
                        pdf_canvas, table_elem, images_metadata,
                        target_height, scale_w, scale_h
                    )

                # 3. Draw text (top layer)
                for region in page_text_regions:
                    self.draw_text_region(
                        pdf_canvas, region, target_height,
                        scale_w, scale_h
                    )

                logger.info(f"<<< 第 {page_num} 頁完成")

            # Save PDF
            pdf_canvas.save()

            file_size = output_path.stat().st_size
            logger.info(f"Generated PDF: {output_path.name} ({file_size} bytes)")
            return True

        except Exception as e:
            logger.error(f"Failed to generate PDF: {e}")
            import traceback
            traceback.print_exc()
            return False

    def calculate_page_dimensions(self, ocr_data: Dict, source_file_path: Optional[Path] = None) -> Tuple[float, float]:
        """
        從 OCR JSON 數據中推斷 OCR 處理時的實際頁面尺寸。
        這非常重要，因為 OCR 可能在高解析度影像上運行。

        Args:
            ocr_data: Complete OCR data dictionary with text_regions and layout
            source_file_path: Optional path to source file (fallback only)

        Returns:
            Tuple of (width, height) in points
        """
        max_x = 0
        max_y = 0

        # *** 關鍵修復：檢查所有可能包含 bbox 的字段 ***
        # 不同版本的 OCR 輸出可能使用不同的字段名
        all_regions = []

        # 1. text_regions - 包含所有文字區域（最常見）
        if 'text_regions' in ocr_data and isinstance(ocr_data['text_regions'], list):
            all_regions.extend(ocr_data['text_regions'])

        # 2. image_regions - 包含圖片區域
        if 'image_regions' in ocr_data and isinstance(ocr_data['image_regions'], list):
            all_regions.extend(ocr_data['image_regions'])

        # 3. tables - 包含表格區域
        if 'tables' in ocr_data and isinstance(ocr_data['tables'], list):
            all_regions.extend(ocr_data['tables'])

        # 4. layout - 可能包含布局信息（可能是空列表）
        if 'layout' in ocr_data and isinstance(ocr_data['layout'], list):
            all_regions.extend(ocr_data['layout'])

        # 5. layout_data.elements - PP-StructureV3 格式
        if 'layout_data' in ocr_data and isinstance(ocr_data['layout_data'], dict):
            elements = ocr_data['layout_data'].get('elements', [])
            if elements:
                all_regions.extend(elements)

        if not all_regions:
            # 如果 JSON 為空，回退到原始檔案尺寸
            logger.warning("JSON 中沒有找到 text_regions, image_regions, tables, layout 或 layout_data.elements，回退到原始檔案尺寸。")
            if source_file_path:
                dims = self.get_original_page_size(source_file_path)
                if dims:
                    return dims
            return A4

        region_count = 0
        for region in all_regions:
            try:
                bbox = region.get('bbox')
                if not bbox:
                    continue

                region_count += 1

                # *** 關鍵修復：正確處理多邊形 [[x, y], ...] 格式 ***
                if isinstance(bbox[0], (int, float)):
                    # 處理簡單的 [x1, y1, x2, y2] 格式
                    max_x = max(max_x, bbox[2])
                    max_y = max(max_y, bbox[3])
                elif isinstance(bbox[0], (list, tuple)):
                    # 處理多邊形 [[x, y], ...] 格式
                    x_coords = [p[0] for p in bbox if isinstance(p, (list, tuple)) and len(p) >= 2]
                    y_coords = [p[1] for p in bbox if isinstance(p, (list, tuple)) and len(p) >= 2]
                    if x_coords and y_coords:
                        max_x = max(max_x, max(x_coords))
                        max_y = max(max_y, max(y_coords))

            except Exception as e:
                logger.warning(f"Error processing bbox {bbox}: {e}")

        if max_x > 0 and max_y > 0:
            logger.info(f"從 {region_count} 個區域中推斷出的 OCR 座標系尺寸: {max_x:.1f} x {max_y:.1f}")
            return (max_x, max_y)
        else:
            # 如果所有 bbox 都解析失敗，才回退
            logger.warning("無法從 bbox 推斷尺寸，回退到原始檔案尺寸。")
            if source_file_path:
                dims = self.get_original_page_size(source_file_path)
                if dims:
                    return dims
            return A4

    def get_original_page_size(self, file_path: Path) -> Optional[Tuple[float, float]]:
        """
        Extract page dimensions from original source file

        Args:
            file_path: Path to original file (image or PDF)

        Returns:
            Tuple of (width, height) in points or None
        """
        try:
            if not file_path.exists():
                return None

            # For images, get dimensions from PIL
            if file_path.suffix.lower() in ['.png', '.jpg', '.jpeg', '.bmp', '.tiff']:
                img = Image.open(file_path)
                # Use pixel dimensions directly as points (1:1 mapping)
                # This matches how PaddleOCR reports coordinates
                width_pt = float(img.width)
                height_pt = float(img.height)
                logger.info(f"Extracted dimensions from image: {width_pt:.1f} x {height_pt:.1f} points (1:1 pixel mapping)")
                return (width_pt, height_pt)

            # For PDFs, extract dimensions using PyPDF2
            if file_path.suffix.lower() == '.pdf':
                try:
                    from PyPDF2 import PdfReader
                    reader = PdfReader(file_path)
                    if len(reader.pages) > 0:
                        page = reader.pages[0]
                        # MediaBox gives [x1, y1, x2, y2] in points
                        mediabox = page.mediabox
                        width_pt = float(mediabox.width)
                        height_pt = float(mediabox.height)
                        logger.info(f"Extracted dimensions from PDF: {width_pt:.1f} x {height_pt:.1f} points")
                        return (width_pt, height_pt)
                except ImportError:
                    logger.warning("PyPDF2 not available, cannot extract PDF dimensions")
                except Exception as e:
                    logger.warning(f"Failed to extract PDF dimensions: {e}")

        except Exception as e:
            logger.warning(f"Failed to get page size from {file_path}: {e}")

        return None

    def _get_bbox_coords(self, bbox: Union[List[List[float]], List[float]]) -> Optional[Tuple[float, float, float, float]]:
        """將任何 bbox 格式 (多邊形或 [x1,y1,x2,y2]) 轉換為 [x_min, y_min, x_max, y_max]"""
        try:
            if isinstance(bbox[0], (list, tuple)):
                # 處理多邊形 [[x, y], ...]
                x_coords = [p[0] for p in bbox if isinstance(p, (list, tuple)) and len(p) >= 2]
                y_coords = [p[1] for p in bbox if isinstance(p, (list, tuple)) and len(p) >= 2]
                if not x_coords or not y_coords:
                    return None
                return min(x_coords), min(y_coords), max(x_coords), max(y_coords)
            elif isinstance(bbox[0], (int, float)) and len(bbox) == 4:
                # 處理 [x1, y1, x2, y2]
                return bbox[0], bbox[1], bbox[2], bbox[3]
            else:
                logger.warning(f"未知的 bbox 格式: {bbox}")
                return None
        except Exception as e:
            logger.error(f"解析 bbox {bbox} 時出錯: {e}")
            return None

    def _is_bbox_inside(self, inner_bbox_data: Dict, outer_bbox_data: Dict, tolerance: float = 5.0) -> bool:
        """
        檢查 'inner_bbox' 是否在 'outer_bbox' 內部（帶有容錯）。
        此版本可處理多邊形和矩形。
        """
        inner_coords = self._get_bbox_coords(inner_bbox_data.get('bbox'))
        outer_coords = self._get_bbox_coords(outer_bbox_data.get('bbox'))

        if not inner_coords or not outer_coords:
            return False

        inner_x1, inner_y1, inner_x2, inner_y2 = inner_coords
        outer_x1, outer_y1, outer_x2, outer_y2 = outer_coords

        # 檢查 inner 是否在 outer 內部 (加入 tolerance)
        is_inside = (
            (inner_x1 >= outer_x1 - tolerance) and
            (inner_y1 >= outer_y1 - tolerance) and
            (inner_x2 <= outer_x2 + tolerance) and
            (inner_y2 <= outer_y2 + tolerance)
        )
        return is_inside

    def _bbox_overlaps(self, bbox1_data: Dict, bbox2_data: Dict, tolerance: float = 5.0) -> bool:
        """
        檢查兩個 bbox 是否有重疊（帶有容錯）。
        如果有任何重疊，返回 True。

        Args:
            bbox1_data: 第一個 bbox 數據
            bbox2_data: 第二個 bbox 數據
            tolerance: 容錯值（像素）

        Returns:
            True 如果兩個 bbox 有重疊
        """
        coords1 = self._get_bbox_coords(bbox1_data.get('bbox'))
        coords2 = self._get_bbox_coords(bbox2_data.get('bbox'))

        if not coords1 or not coords2:
            return False

        x1_min, y1_min, x1_max, y1_max = coords1
        x2_min, y2_min, x2_max, y2_max = coords2

        # 擴展 bbox2（表格/圖片區域）的範圍
        x2_min -= tolerance
        y2_min -= tolerance
        x2_max += tolerance
        y2_max += tolerance

        # 檢查是否有重疊：如果沒有重疊，則必定滿足以下條件之一
        no_overlap = (
            x1_max < x2_min or  # bbox1 在 bbox2 左側
            x1_min > x2_max or  # bbox1 在 bbox2 右側
            y1_max < y2_min or  # bbox1 在 bbox2 上方
            y1_min > y2_max     # bbox1 在 bbox2 下方
        )

        return not no_overlap

    def _filter_text_in_regions(self, text_regions: List[Dict], regions_to_avoid: List[Dict], tolerance: float = 10.0) -> List[Dict]:
        """
        過濾掉與 'regions_to_avoid'（例如表格、圖片）重疊的文字區域。

        Args:
            text_regions: 文字區域列表
            regions_to_avoid: 需要避免的區域列表（表格、圖片）
            tolerance: 容錯值（像素），增加到 10.0 以更好地處理邊界情況

        Returns:
            過濾後的文字區域列表
        """
        filtered_text = []
        filtered_count = 0

        for text_region in text_regions:
            should_filter = False

            for avoid_region in regions_to_avoid:
                # 使用重疊檢測：只要有任何重疊就過濾掉
                if self._bbox_overlaps(text_region, avoid_region, tolerance=tolerance):
                    should_filter = True
                    filtered_count += 1
                    logger.debug(f"過濾掉重疊文字: {text_region.get('text', '')[:20]}...")
                    break  # 找到一個重疊區域就足夠了

            if not should_filter:
                filtered_text.append(text_region)

        logger.info(f"原始文字區域: {len(text_regions)}, 過濾後: {len(filtered_text)}, 移除: {filtered_count}")
        return filtered_text

    def draw_text_region(
        self,
        pdf_canvas: canvas.Canvas,
        region: Dict,
        page_height: float,
        scale_w: float = 1.0,
        scale_h: float = 1.0
    ):
        """
        Draw a text region at precise coordinates

        Args:
            pdf_canvas: ReportLab canvas object
            region: Text region dict with text, bbox, confidence
            page_height: Height of page (for coordinate transformation)
            scale_w: Scale factor for X coordinates (PDF width / OCR width)
            scale_h: Scale factor for Y coordinates (PDF height / OCR height)
        """
        text = region.get('text', '')
        bbox = region.get('bbox', [])
        confidence = region.get('confidence', 1.0)

        if not text or not bbox or len(bbox) < 4:
            return

        try:
            # bbox from OCR: [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
            # Points: top-left, top-right, bottom-right, bottom-left
            # OCR coordinates: origin (0,0) at top-left, Y increases downward
            ocr_x_left = bbox[0][0]    # Left X
            ocr_y_top = bbox[0][1]     # Top Y in OCR coordinates
            ocr_x_right = bbox[2][0]   # Right X
            ocr_y_bottom = bbox[2][1]  # Bottom Y in OCR coordinates

            logger.info(f"[文字] '{text[:20]}...' OCR原始座標: L={ocr_x_left:.0f}, T={ocr_y_top:.0f}, R={ocr_x_right:.0f}, B={ocr_y_bottom:.0f}")

            # Apply scale factors to convert from OCR space to PDF space
            scaled_x_left = ocr_x_left * scale_w
            scaled_y_top = ocr_y_top * scale_h
            scaled_x_right = ocr_x_right * scale_w
            scaled_y_bottom = ocr_y_bottom * scale_h

            logger.info(f"[文字] '{text[:20]}...' 縮放後(scale={scale_w:.3f},{scale_h:.3f}): L={scaled_x_left:.1f}, T={scaled_y_top:.1f}, R={scaled_x_right:.1f}, B={scaled_y_bottom:.1f}")

            # Calculate bbox dimensions (after scaling)
            bbox_width = abs(scaled_x_right - scaled_x_left)
            bbox_height = abs(scaled_y_bottom - scaled_y_top)

            # Calculate font size using heuristics
            # Font size is typically 70-90% of bbox height
            # Testing shows 0.75 works well for most cases
            font_size = bbox_height * 0.75
            font_size = max(min(font_size, 72), 4)  # Clamp between 4pt and 72pt

            # Transform coordinates: OCR (top-left origin) → PDF (bottom-left origin)
            # CRITICAL: Y-axis flip!
            pdf_x = scaled_x_left
            pdf_y = page_height - scaled_y_bottom  # Flip Y-axis using bottom coordinate

            logger.info(f"[文字] '{text[:30]}' → PDF位置: ({pdf_x:.1f}, {pdf_y:.1f}), 字體:{font_size:.1f}pt, 寬x高:{bbox_width:.0f}x{bbox_height:.0f}")

            # Set font with track-specific styling
            style_info = region.get('style')
            is_direct_track = (self.current_processing_track == 'direct' or
                              self.current_processing_track == ProcessingTrack.DIRECT)

            if style_info and is_direct_track:
                # Direct track: Apply rich styling from StyleInfo
                self._apply_text_style(pdf_canvas, style_info, default_size=font_size)
                # Get current font for width calculation
                font_name = pdf_canvas._fontname
                font_size = pdf_canvas._fontsize
                logger.debug(f"Applied Direct track style: font={font_name}, size={font_size}")
            else:
                # OCR track or no style: Use simple font selection
                font_name = self.font_name if self.font_registered else 'Helvetica'
                pdf_canvas.setFont(font_name, font_size)

            # Calculate text width to prevent overflow
            text_width = pdf_canvas.stringWidth(text, font_name, font_size)

            # If text is too wide for bbox, scale down font
            if text_width > bbox_width:
                scale_factor = bbox_width / text_width
                font_size = font_size * scale_factor * 0.95  # 95% to add small margin
                font_size = max(font_size, 3)  # Minimum 3pt
                pdf_canvas.setFont(font_name, font_size)

            # Draw text at calculated position
            pdf_canvas.drawString(pdf_x, pdf_y, text)

            # Debug: Draw bounding box (optional)
            if settings.pdf_enable_bbox_debug:
                pdf_canvas.setStrokeColorRGB(1, 0, 0, 0.3)  # Red, semi-transparent
                pdf_canvas.setLineWidth(0.5)
                # Transform all bbox points to PDF coordinates (apply scaling first)
                pdf_points = [(p[0] * scale_w, page_height - p[1] * scale_h) for p in bbox]
                # Draw bbox rectangle
                for i in range(4):
                    x1, y1 = pdf_points[i]
                    x2, y2 = pdf_points[(i + 1) % 4]
                    pdf_canvas.line(x1, y1, x2, y2)

        except Exception as e:
            logger.warning(f"Failed to draw text region '{text[:20]}...': {e}")

    def draw_table_region(
        self,
        pdf_canvas: canvas.Canvas,
        table_element: Dict,
        images_metadata: List[Dict],
        page_height: float,
        scale_w: float = 1.0,
        scale_h: float = 1.0
    ):
        """
        Draw a table region by parsing HTML and rebuilding with ReportLab Table

        Args:
            pdf_canvas: ReportLab canvas object
            table_element: Table element dict with HTML content
            images_metadata: List of image metadata to find table bbox
            page_height: Height of page
            scale_w: Scale factor for X coordinates (PDF width / OCR width)
            scale_h: Scale factor for Y coordinates (PDF height / OCR height)
        """
        try:
            html_content = table_element.get('content', '')
            if not html_content:
                return

            # Parse HTML to extract table structure
            parser = HTMLTableParser()
            parser.feed(html_content)

            if not parser.tables:
                logger.warning("No tables found in HTML content")
                return

            # Get the first table (PP-StructureV3 usually provides one table per element)
            table_data = parser.tables[0]
            rows = table_data['rows']

            if not rows:
                return

            # Get bbox directly from table element
            table_bbox = table_element.get('bbox')

            # If no bbox directly, check for bbox_polygon
            if not table_bbox:
                bbox_polygon = table_element.get('bbox_polygon')
                if bbox_polygon and len(bbox_polygon) >= 4:
                    # Convert polygon format to simple bbox [x0, y0, x1, y1]
                    table_bbox = [
                        bbox_polygon[0][0],  # x0
                        bbox_polygon[0][1],  # y0
                        bbox_polygon[2][0],  # x1
                        bbox_polygon[2][1]   # y1
                    ]

            if not table_bbox:
                logger.warning(f"No bbox found for table element")
                return

            # Handle different bbox formats
            if isinstance(table_bbox, list) and len(table_bbox) == 4:
                # Simple bbox format [x0, y0, x1, y1]
                if isinstance(table_bbox[0], (int, float)):
                    ocr_x_left_raw = table_bbox[0]
                    ocr_y_top_raw = table_bbox[1]
                    ocr_x_right_raw = table_bbox[2]
                    ocr_y_bottom_raw = table_bbox[3]
                # Polygon format [[x,y], [x,y], [x,y], [x,y]]
                elif isinstance(table_bbox[0], list):
                    ocr_x_left_raw = table_bbox[0][0]
                    ocr_y_top_raw = table_bbox[0][1]
                    ocr_x_right_raw = table_bbox[2][0]
                    ocr_y_bottom_raw = table_bbox[2][1]
                else:
                    logger.error(f"Unexpected bbox format: {table_bbox}")
                    return
            else:
                logger.error(f"Invalid table_bbox format: {table_bbox}")
                return

            logger.info(f"[表格] OCR原始座標: L={ocr_x_left_raw:.0f}, T={ocr_y_top_raw:.0f}, R={ocr_x_right_raw:.0f}, B={ocr_y_bottom_raw:.0f}")

            # Apply scaling
            ocr_x_left = ocr_x_left_raw * scale_w
            ocr_y_top = ocr_y_top_raw * scale_h
            ocr_x_right = ocr_x_right_raw * scale_w
            ocr_y_bottom = ocr_y_bottom_raw * scale_h

            table_width = abs(ocr_x_right - ocr_x_left)
            table_height = abs(ocr_y_bottom - ocr_y_top)

            # Transform coordinates
            pdf_x = ocr_x_left
            pdf_y = page_height - ocr_y_bottom

            # Build table data for ReportLab
            # Convert parsed structure to simple 2D array
            max_cols = max(len(row['cells']) for row in rows)

            logger.info(f"[表格] {len(rows)}行x{max_cols}列 → PDF位置: ({pdf_x:.1f}, {pdf_y:.1f}), 寬x高: {table_width:.0f}x{table_height:.0f}")
            reportlab_data = []

            for row in rows:
                row_data = []
                for cell in row['cells']:
                    text = cell['text'].strip()
                    row_data.append(text)
                # Pad row if needed
                while len(row_data) < max_cols:
                    row_data.append('')
                reportlab_data.append(row_data)

            # Calculate column widths (equal distribution)
            col_widths = [table_width / max_cols] * max_cols

            # Create ReportLab Table
            # Use smaller font size to fit in bbox
            font_size = min(table_height / len(rows) * 0.5, 10)
            font_size = max(font_size, 6)

            # Create table with font
            table = Table(reportlab_data, colWidths=col_widths)

            # Apply table style
            style = TableStyle([
                ('FONT', (0, 0), (-1, -1), self.font_name if self.font_registered else 'Helvetica', font_size),
                ('GRID', (0, 0), (-1, -1), 0.5, colors.black),
                ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
                ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
                ('LEFTPADDING', (0, 0), (-1, -1), 2),
                ('RIGHTPADDING', (0, 0), (-1, -1), 2),
                ('TOPPADDING', (0, 0), (-1, -1), 2),
                ('BOTTOMPADDING', (0, 0), (-1, -1), 2),
            ])

            # Add header style if first row has headers
            if rows and rows[0]['cells'] and rows[0]['cells'][0].get('is_header'):
                style.add('BACKGROUND', (0, 0), (-1, 0), colors.lightgrey)
                style.add('FONT', (0, 0), (-1, 0), self.font_name if self.font_registered else 'Helvetica-Bold', font_size)

            table.setStyle(style)

            # Calculate table size
            table.wrapOn(pdf_canvas, table_width, table_height)

            # Draw table at position
            table.drawOn(pdf_canvas, pdf_x, pdf_y)

            logger.info(f"Drew table at ({pdf_x:.0f}, {pdf_y:.0f}) size {table_width:.0f}x{table_height:.0f} with {len(rows)} rows")

        except Exception as e:
            logger.warning(f"Failed to draw table region: {e}")
            import traceback
            traceback.print_exc()

    def draw_image_region(
        self,
        pdf_canvas: canvas.Canvas,
        region: Dict,
        page_height: float,
        result_dir: Path,
        scale_w: float = 1.0,
        scale_h: float = 1.0
    ):
        """
        Draw an image region by embedding the extracted image

        Handles images extracted by PP-StructureV3 (tables, figures, charts, etc.)

        Args:
            pdf_canvas: ReportLab canvas object
            region: Image metadata dict with image_path and bbox
            page_height: Height of page (for coordinate transformation)
            result_dir: Directory containing result files
            scale_w: Scale factor for X coordinates (PDF width / OCR width)
            scale_h: Scale factor for Y coordinates (PDF height / OCR height)
        """
        try:
            image_path_str = region.get('image_path', '')
            if not image_path_str:
                return

            # Construct full path to image
            image_path = result_dir / image_path_str

            if not image_path.exists():
                logger.warning(f"Image not found: {image_path}")
                return

            # Get bbox for positioning
            bbox = region.get('bbox', [])
            if not bbox or len(bbox) < 4:
                # If no bbox, skip for now
                logger.warning(f"No bbox for image {image_path_str}")
                return

            # bbox from OCR: [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
            # OCR coordinates: origin (0,0) at top-left, Y increases downward
            ocr_x_left_raw = bbox[0][0]
            ocr_y_top_raw = bbox[0][1]
            ocr_x_right_raw = bbox[2][0]
            ocr_y_bottom_raw = bbox[2][1]

            logger.info(f"[圖片] '{image_path_str}' OCR原始座標: L={ocr_x_left_raw:.0f}, T={ocr_y_top_raw:.0f}, R={ocr_x_right_raw:.0f}, B={ocr_y_bottom_raw:.0f}")

            # Apply scaling
            ocr_x_left = ocr_x_left_raw * scale_w
            ocr_y_top = ocr_y_top_raw * scale_h
            ocr_x_right = ocr_x_right_raw * scale_w
            ocr_y_bottom = ocr_y_bottom_raw * scale_h

            # Calculate bbox dimensions (after scaling)
            bbox_width = abs(ocr_x_right - ocr_x_left)
            bbox_height = abs(ocr_y_bottom - ocr_y_top)

            # Transform coordinates: OCR (top-left origin) → PDF (bottom-left origin)
            # CRITICAL: Y-axis flip!
            # For images, we position at bottom-left corner
            pdf_x_left = ocr_x_left
            pdf_y_bottom = page_height - ocr_y_bottom  # Flip Y-axis

            logger.info(f"[圖片] '{image_path_str}' → PDF位置: ({pdf_x_left:.1f}, {pdf_y_bottom:.1f}), 寬x高: {bbox_width:.0f}x{bbox_height:.0f}")

            # Draw image using ReportLab
            # drawImage expects: (path, x, y, width, height)
            # where (x, y) is the bottom-left corner of the image
            pdf_canvas.drawImage(
                str(image_path),
                pdf_x_left,
                pdf_y_bottom,
                width=bbox_width,
                height=bbox_height,
                preserveAspectRatio=True,
                mask='auto'  # Handle transparency
            )

            logger.info(f"[圖片] ✓ 成功繪製 '{image_path_str}'")

        except Exception as e:
            logger.warning(f"Failed to draw image region: {e}")

    def generate_layout_pdf(
        self,
        json_path: Path,
        output_path: Path,
        source_file_path: Optional[Path] = None
    ) -> bool:
        """
        Generate layout-preserving PDF from OCR JSON data

        Args:
            json_path: Path to OCR JSON file
            output_path: Path to save generated PDF
            source_file_path: Optional path to original source file for dimension extraction

        Returns:
            True if successful, False otherwise
        """
        try:
            # Load JSON data
            ocr_data = self.load_ocr_json(json_path)
            if not ocr_data:
                return False

            # Use internal generation with pre-loaded data
            return self._generate_pdf_from_data(
                ocr_data=ocr_data,
                output_path=output_path,
                source_file_path=source_file_path,
                json_parent_dir=json_path.parent
            )

        except Exception as e:
            logger.error(f"Failed to generate PDF: {e}")
            import traceback
            traceback.print_exc()
            return False


# Singleton instance
pdf_generator_service = PDFGeneratorService()