backend/integrations/pdf_processing/pdf_ocr_service.py.bak3

import asyncio
import base64
import io
import logging
import os
from pathlib import Path
import tempfile
from typing import Any, Dict, List, Optional, Tuple, Union

# Optional PIL import for image processing
try:
    from PIL import Image
    PIL_AVAILABLE = True
except ImportError:
    Image = None
    PIL_AVAILABLE = False
    logger = logging.getLogger(__name__)
    logger.warning("PIL (Pillow) not available - image processing features will be limited")

try:
    import PyPDF2
except ImportError:
    PyPDF2 = None

# Optional numpy import
try:
    import numpy as np
    NUMPY_AVAILABLE = True
except ImportError:
    np = None
    NUMPY_AVAILABLE = False

# LLM Service Integration
try:
    from core.llm_service import LLMService
    LLM_SERVICE_AVAILABLE = True
except ImportError:
    LLM_SERVICE_AVAILABLE = False

logger = logging.getLogger(__name__)


class PDFOCRService:
    """
    Enhanced PDF processing service with OCR capabilities and fallback mechanisms.
    Supports both searchable PDFs and scanned/image-based PDFs.
    """

    def __init__(
        self,
        tesseract_path: Optional[str] = None,
        easyocr_languages: List[str] = None,
        tenant_id: str = "default",
    ):
        """
        Initialize the PDF OCR service.

        Args:
            tesseract_path: Path to tesseract executable (if not in PATH)
            easyocr_languages: List of languages for EasyOCR (default: ['en'])
            tenant_id: Tenant ID for metered AI operations
        """
        self.tesseract_path = tesseract_path
        self.easyocr_languages = easyocr_languages or ["en"]
        self.tenant_id = tenant_id

        # Use unified LLMService for AI vision/OCR
        self.llm_service = None
        if LLM_SERVICE_AVAILABLE:
            self.llm_service = LLMService(tenant_id=tenant_id)
            logger.info(f"PDF OCR Service initialized with LLMService for tenant: {tenant_id}")

        # Initialize OCR readers
        self._init_ocr_readers()

        # Service availability flags
        self.service_status = self._check_service_availability()

        logger.info(f"PDF OCR Service initialized - Status: {self.service_status}")

    def _init_ocr_readers(self):
        """Initialize OCR readers based on available libraries."""
        self.ocr_readers = {}

        # Docling (highest priority - advanced document understanding with OCR)
        if DOCLING_AVAILABLE:
            try:
                self.ocr_readers["docling"] = get_docling_processor()
                logger.info("Docling document processor initialized (highest priority)")
            except Exception as e:
                logger.warning(f"Failed to initialize Docling: {e}")

        # Tesseract OCR
        if TESSERACT_AVAILABLE:
            try:
                if self.tesseract_path:
                    pytesseract.pytesseract.tesseract_cmd = self.tesseract_path
                self.ocr_readers["tesseract"] = pytesseract
                logger.info("Tesseract OCR initialized")
            except Exception as e:
                logger.warning(f"Failed to initialize Tesseract: {e}")

        # EasyOCR
        if EASYOCR_AVAILABLE:
            try:
                self.ocr_readers["easyocr"] = easyocr.Reader(self.easyocr_languages)
                logger.info("EasyOCR initialized")
            except Exception as e:
                logger.warning(f"Failed to initialize EasyOCR: {e}")

        # AI Vision (for advanced image comprehension) - Using unified LLMService
        if self.llm_service:
            self.ocr_readers["ai_vision"] = self.llm_service
            logger.info("AI Vision (LLMService) initialized")

    def _check_service_availability(self) -> Dict[str, bool]:
        """Check availability of different OCR services."""
        status = {
            "basic_pdf": True,  # Always available (PyPDF2)
            "docling": "docling" in self.ocr_readers,
            "tesseract": "tesseract" in self.ocr_readers,
            "easyocr": "easyocr" in self.ocr_readers,
            "openai_vision": "ai_vision" in self.ocr_readers,
            "fallback_available": len(self.ocr_readers) > 0,
            "byok_integrated": self.llm_service is not None,
        }
        return status

    async def process_pdf(
        self,
        pdf_data: Union[bytes, str, Path],
        use_ocr: bool = True,
        extract_images: bool = True,
        use_advanced_comprehension: bool = False,
        fallback_strategy: str = "cascade",
    ) -> Dict[str, Any]:
        """
        Process PDF with optional OCR and image comprehension.

        Args:
            pdf_data: PDF file as bytes, file path, or Path object
            use_ocr: Whether to use OCR for scanned PDFs
            extract_images: Whether to extract and process images
            use_advanced_comprehension: Whether to use AI for image understanding
            fallback_strategy: "cascade" (try best first) or "parallel" (try all)

        Returns:
            Dictionary with extracted text, metadata, and processing results
        """
        try:
            # Convert input to bytes if it's a file path
            if isinstance(pdf_data, (str, Path)):
                with open(pdf_data, "rb") as f:
                    pdf_data = f.read()

            # Step 1: Try basic text extraction first
            basic_result = await self._extract_basic_text(pdf_data)

            # Step 2: Check if we need OCR (low text content or specific request)
            needs_ocr = use_ocr and (
                basic_result["text_ratio"] < 0.1
                or len(basic_result["extracted_text"].strip()) < 100
            )

            # Step 3: Process with OCR if needed
            ocr_result = None
            if needs_ocr:
                ocr_result = await self._process_with_ocr(
                    pdf_data, fallback_strategy, use_advanced_comprehension
                )

            # Step 4: Extract and process images if requested
            image_results = None
            if extract_images:
                image_results = await self._extract_and_process_images(
                    pdf_data, use_advanced_comprehension
                )

            # Combine results
            final_result = self._combine_results(
                basic_result, ocr_result, image_results, needs_ocr
            )

            return final_result

        except Exception as e:
            logger.error(f"PDF processing failed: {e}")
            return self._create_error_result(str(e))

    async def _extract_basic_text(self, pdf_data: bytes) -> Dict[str, Any]:
        """Extract text using basic PyPDF2 method."""
        try:
            pdf_file = io.BytesIO(pdf_data)
            pdf_reader = PyPDF2.PdfReader(pdf_file)

            text_content = []
            total_chars = 0
            page_count = len(pdf_reader.pages)

            for page_num, page in enumerate(pdf_reader.pages):
                page_text = page.extract_text()
                text_content.append(
                    {
                        "page": page_num + 1,
                        "text": page_text,
                        "char_count": len(page_text),
                    }
                )
                total_chars += len(page_text)

            # Calculate text ratio (rough estimate of searchable content)
            text_ratio = min(total_chars / (page_count * 1000), 1.0)  # Normalize

            return {
                "method": "basic_pdf",
                "extracted_text": "\n".join([p["text"] for p in text_content]),
                "page_texts": text_content,
                "page_count": page_count,
                "total_chars": total_chars,
                "text_ratio": text_ratio,
                "success": True,
            }

        except Exception as e:
            logger.warning(f"Basic text extraction failed: {e}")
            return {
                "method": "basic_pdf",
                "extracted_text": "",
                "page_texts": [],
                "page_count": 0,
                "total_chars": 0,
                "text_ratio": 0.0,
                "success": False,
                "error": str(e),
            }

    async def _process_with_ocr(
        self, pdf_data: bytes, fallback_strategy: str, use_advanced_comprehension: bool
    ) -> Dict[str, Any]:
        """Process PDF using OCR with fallback strategy."""
        methods_tried = []
        best_result = None

        ocr_methods = self._get_available_ocr_methods(use_advanced_comprehension)

        if fallback_strategy == "cascade":
            # Try methods in order of preference
            for method_name in ocr_methods:
                try:
                    logger.info(f"Trying OCR method: {method_name}")
                    result = await self._run_ocr_method(method_name, pdf_data)
                    methods_tried.append(method_name)

                    if result["success"] and result["total_chars"] > 0:
                        best_result = result
                        break  # Found good result, stop trying

                except Exception as e:
                    logger.warning(f"OCR method {method_name} failed: {e}")
                    methods_tried.append(f"{method_name}_failed")

        elif fallback_strategy == "parallel":
            # Try all methods and pick the best
            results = []
            for method_name in ocr_methods:
                try:
                    result = await self._run_ocr_method(method_name, pdf_data)
                    methods_tried.append(method_name)
                    if result["success"]:
                        results.append(result)
                except Exception as e:
                    logger.warning(f"OCR method {method_name} failed: {e}")
                    methods_tried.append(f"{method_name}_failed")

            if results:
                # Pick result with most text
                best_result = max(results, key=lambda x: x["total_chars"])

        return {
            "best_result": best_result,
            "methods_tried": methods_tried,
            "success": best_result is not None,
        }

    def _get_available_ocr_methods(self, use_advanced_comprehension: bool) -> List[str]:
        """Get available OCR methods in priority order."""
        methods = []

        # Use BYOK optimization if available
        if self.use_byok and use_advanced_comprehension:
            try:
                optimal_provider = self.byok_manager.get_optimal_provider(
                    "image_comprehension"
                )
                if optimal_provider == "openai" and "openai" in self.ocr_readers:
                    methods.append("openai_vision")
                    logger.info(
                        f"BYOK selected {optimal_provider} for image comprehension"
                    )
            except Exception as e:
                logger.warning(f"BYOK optimization failed: {e}")

        # Docling first (highest priority - best OCR with layout analysis)
        if "docling" in self.ocr_readers:
            methods.insert(0, "docling")

        # Fallback to default logic if BYOK not available or failed
        if not methods and use_advanced_comprehension and "openai" in self.ocr_readers:
            methods.append("openai_vision")

        # Then standard OCR methods
        if "easyocr" in self.ocr_readers:
            methods.append("easyocr")

        if "tesseract" in self.ocr_readers:
            methods.append("tesseract")

        return methods

    async def _run_ocr_method(
        self, method_name: str, pdf_data: bytes
    ) -> Dict[str, Any]:
        """Run specific OCR method on PDF."""
        if method_name == "docling":
            return await self._ocr_with_docling(pdf_data)
        elif method_name == "tesseract":
            return await self._ocr_with_tesseract(pdf_data)
        elif method_name == "easyocr":
            return await self._ocr_with_easyocr(pdf_data)
        elif method_name in ["openai_vision", "ai_vision"]:
            return await self._ocr_with_ai_vision(pdf_data)
        else:
            raise ValueError(f"Unknown OCR method: {method_name}")

    async def _ocr_with_docling(self, pdf_data: bytes) -> Dict[str, Any]:
        """Extract text using Docling with advanced OCR and layout analysis."""
        if "docling" not in self.ocr_readers:
            raise RuntimeError("Docling not available")

        try:
            processor = self.ocr_readers["docling"]
            result = await processor.process_pdf(pdf_data, use_ocr=True)

            if result.get("success"):
                return {
                    "method": "docling",
                    "extracted_text": result.get("extracted_text", ""),
                    "page_texts": result.get("page_texts", []),
                    "page_count": result.get("page_count", 0),
                    "total_chars": result.get("total_chars", 0),
                    "tables": result.get("tables", []),
                    "success": True,
                }
            else:
                raise RuntimeError(result.get("error", "Docling processing failed"))

        except Exception as e:
            logger.error(f"Docling OCR failed: {e}")
            return {
                "method": "docling",
                "extracted_text": "",
                "page_texts": [],
                "page_count": 0,
                "total_chars": 0,
                "success": False,
                "error": str(e),
            }

    async def _ocr_with_tesseract(self, pdf_data: bytes) -> Dict[str, Any]:
        """Extract text using Tesseract OCR."""
        if "tesseract" not in self.ocr_readers:
            raise RuntimeError("Tesseract not available")

        try:
            # Convert PDF to images for OCR
            images = await self._pdf_to_images(pdf_data)
            text_content = []
            total_chars = 0

            for page_num, image in enumerate(images):
                # Convert PIL image to format tesseract expects
                text = pytesseract.image_to_string(image)
                text_content.append(
                    {"page": page_num + 1, "text": text, "char_count": len(text)}
                )
                total_chars += len(text)

            return {
                "method": "tesseract",
                "extracted_text": "\n".join([p["text"] for p in text_content]),
                "page_texts": text_content,
                "page_count": len(images),
                "total_chars": total_chars,
                "success": True,
            }

        except Exception as e:
            logger.error(f"Tesseract OCR failed: {e}")
            return {
                "method": "tesseract",
                "extracted_text": "",
                "page_texts": [],
                "page_count": 0,
                "total_chars": 0,
                "success": False,
                "error": str(e),
            }

    async def _ocr_with_easyocr(self, pdf_data: bytes) -> Dict[str, Any]:
        """Extract text using EasyOCR."""
        if "easyocr" not in self.ocr_readers:
            raise RuntimeError("EasyOCR not available")

        try:
            images = await self._pdf_to_images(pdf_data)
            text_content = []
            total_chars = 0

            for page_num, image in enumerate(images):
                # Convert PIL image to numpy array (if numpy is available)
                if not NUMPY_AVAILABLE:
                    logger.error("NumPy is required for EasyOCR but not available")
                    raise ImportError("NumPy is required for EasyOCR")

                image_np = np.array(image)

                # Run OCR
                results = self.ocr_readers["easyocr"].readtext(image_np)

                # Combine text from all detections
                page_text = " ".join([result[1] for result in results])
                text_content.append(
                    {
                        "page": page_num + 1,
                        "text": page_text,
                        "char_count": len(page_text),
                    }
                )
                total_chars += len(page_text)

            return {
                "method": "easyocr",
                "extracted_text": "\n".join([p["text"] for p in text_content]),
                "page_texts": text_content,
                "page_count": len(images),
                "total_chars": total_chars,
                "success": True,
            }

        except Exception as e:
            logger.error(f"EasyOCR failed: {e}")
            return {
                "method": "easyocr",
                "extracted_text": "",
                "page_texts": [],
                "page_count": 0,
                "total_chars": 0,
                "success": False,
                "error": str(e),
            }

    async def _ocr_with_ai_vision(self, pdf_data: bytes) -> Dict[str, Any]:
        """Extract text and comprehend images using AI Vision via unified LLMService."""
        if not self.llm_service:
            raise RuntimeError("AI Vision (LLMService) not available")

        import base64

        try:
            images = await self._pdf_to_images(pdf_data)
            text_content = []
            total_chars = 0
            image_descriptions = []

            for page_num, image in enumerate(images):
                # Convert PIL image to bytes for API
                img_byte_arr = io.BytesIO()
                image.save(img_byte_arr, format="PNG")
                img_byte_arr = img_byte_arr.getvalue()

                # Use LLMService for multimodal vision processing
                response_data = await self.llm_service.generate_completion(
                    messages=[
                        {
                            "role": "user",
                            "content": [
                                {
                                    "type": "text",
                                    "text": "Extract all text from this image and describe any visual elements that might be important for understanding the document.",
                                },
                                {
                                    "type": "image_url",
                                    "image_url": {
                                        "url": f"data:image/png;base64,{base64.b64encode(img_byte_arr).decode('utf-8')}"
                                    },
                                },
                            ],
                        }
                    ],
                    model="auto", # Multimodal capability resolved internally
                    tenant_id=self.tenant_id
                )
                
                if response_data.get("success"):
                    page_text = response_data.get("content", "").strip()
                else:
                    logger.warning(f"AI Vision failed for page {page_num + 1}: {response_data.get('error')}")
                    page_text = ""

                text_content.append(
                    {
                        "page": page_num + 1,
                        "text": page_text,
                        "char_count": len(page_text),
                    }
                )
                total_chars += len(page_text)

                image_descriptions.append(
                    {"page": page_num + 1, "description": page_text}
                )

            return {
                "method": "openai_vision", # Keeping method name for consistency in logs/UI
                "extracted_text": "\n".join([p["text"] for p in text_content]),
                "page_texts": text_content,
                "page_count": len(images),
                "total_chars": total_chars,
                "image_descriptions": image_descriptions,
                "success": True,
            }

        except Exception as e:
            logger.error(f"Unified AI Vision failed: {e}")
            return {
                "method": "openai_vision",
                "extracted_text": "",
                "page_texts": [],
                "page_count": 0,
                "total_chars": 0,
                "success": False,
                "error": str(e),
            }

    # BYOK Integration Methods

    def _get_openai_api_key(self) -> Optional[str]:
        """Get OpenAI API key from BYOK system or fallback."""
        if self.use_byok and self.byok_manager:
            try:
                # Try to get API key from BYOK system
                api_key = self.byok_manager.get_api_key("openai")
                if api_key:
                    return api_key
            except Exception as e:
                logger.warning(f"Failed to get OpenAI key from BYOK: {e}")

        # Fallback to constructor parameter or environment variable
        if self.openai_api_key:
            return self.openai_api_key

        return os.getenv("OPENAI_API_KEY")

    async def _optimize_provider_selection(
        self, use_advanced_comprehension: bool, fallback_strategy: str
    ) -> Dict[str, Any]:
        """Optimize provider selection using BYOK system."""
        if not self.use_byok or not self.byok_manager:
            return {"optimized": False, "reason": "BYOK not available"}

        try:
            # Determine task type based on requirements
            if use_advanced_comprehension:
                task_type = "image_comprehension"
            else:
                task_type = "pdf_ocr"

            # Get optimal provider
            optimal_provider = self.byok_manager.get_optimal_provider(task_type)

            return {
                "optimized": True,
                "task_type": task_type,
                "optimal_provider": optimal_provider,
                "fallback_strategy": fallback_strategy,
                "available_providers": list(self.ocr_readers.keys()),
            }

        except Exception as e:
            logger.error(f"Provider optimization failed: {e}")
            return {"optimized": False, "error": str(e)}

    async def _track_byok_usage(
        self, ocr_result: Dict[str, Any], use_advanced_comprehension: bool
    ):
        """Track usage with BYOK system."""
        if not self.use_byok or not self.byok_manager:
            return

        try:
            # Determine which provider was used
            best_method = ocr_result.get("best_result", {}).get("method", "")
            provider_id = self._map_method_to_provider(best_method)

            if not provider_id:
                return

            # Estimate tokens used (rough calculation)
            total_chars = ocr_result.get("best_result", {}).get("total_chars", 0)
            estimated_tokens = max(
                total_chars // 4, 100
            )  # Rough estimate: ~4 chars per token

            # Track successful usage
            self.byok_manager.track_usage(
                provider_id=provider_id, success=True, tokens_used=estimated_tokens
            )

            logger.debug(
                f"Tracked BYOK usage: {provider_id}, {estimated_tokens} tokens"
            )

        except Exception as e:
            logger.warning(f"Failed to track BYOK usage: {e}")

    def _map_method_to_provider(self, method: str) -> Optional[str]:
        """Map OCR method to BYOK provider ID."""
        method_to_provider = {
            "openai_vision": "openai",
            "tesseract": "openai",  # Tesseract doesn't have BYOK provider, map to default
            "easyocr": "openai",  # EasyOCR doesn't have BYOK provider, map to default
            "basic_pdf": None,  # No BYOK tracking for basic extraction
        }
        return method_to_provider.get(method)

    async def _pdf_to_images(self, pdf_data: bytes) -> List[Image.Image]:
        """Convert PDF to list of PIL Images."""
        try:
            # Try using pdf2image if available (best quality)
            try:
                from pdf2image import convert_from_bytes

                logger.debug("Using pdf2image for PDF to image conversion")
                # Convert PDF to list of images at 200 DPI for OCR quality
                images = await asyncio.to_thread(
                    convert_from_bytes, pdf_data, dpi=200, fmt="jpeg"
                )
                logger.info(f"Converted {len(images)} pages using pdf2image")
                return images

            except ImportError:
                logger.warning(
                    "pdf2image not available, using fallback method. "
                    "Install with: pip install pdf2image"
                )

                # Fallback: Try to render PDF pages using PyMuPDF (fitz) if available
                try:
                    import fitz

                    logger.debug("Using PyMuPDF (fitz) for PDF to image conversion")
                    pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
                    images = []

                    for page_num in range(pdf_document.page_count):
                        page = pdf_document[page_num]
                        # Render page to pixmap (zoom=2 for better quality)
                        pix = page.get_pixmap(matrix=fitz.Matrix(2, 2))
                        img_data = pix.tobytes("jpeg")
                        img = Image.open(io.BytesIO(img_data))
                        images.append(img)

                    pdf_document.close()
                    logger.info(f"Converted {len(images)} pages using PyMuPDF")
                    return images

                except ImportError:
                    logger.warning(
                        "PyMuPDF not available. Install with: pip install PyMuPDF"
                    )

                    # Final fallback: Create placeholder images with page info
                    pdf_file = io.BytesIO(pdf_data)
                    pdf_reader = PyPDF2.PdfReader(pdf_file)
                    page_count = len(pdf_reader.pages)

                    logger.warning(
                        f"Using placeholder images for {page_count} pages. "
                        "Install pdf2image or PyMuPDF for proper conversion."
                    )

                    images = []
                    for i, page in enumerate(pdf_reader.pages):
                        # Try to extract page dimensions
                        try:
                            mediabox = page.mediabox
                            width = int(mediabox.width)
                            height = int(mediabox.height)
                            # Limit max size to avoid memory issues
                            width = min(width, 2000)
                            height = min(height, 2000)
                        except (AttributeError, ValueError, TypeError) as e:
                            logger.debug(f"Could not extract page dimensions, using defaults: {e}")
                            width, height = 800, 1000
                        except Exception as e:
                            logger.warning(f"Unexpected error extracting page dimensions: {e}", exc_info=True)
                            width, height = 800, 1000

                        # Create a white image with extracted text overlay if possible
                        img = Image.new("RGB", (width, height), color="white")

                        # Try to extract text and add to image (basic rendering)
                        try:
                            text = page.extract_text()
                            if text and text.strip():
                                from PIL import ImageDraw, ImageFont

                                draw = ImageDraw.Draw(img)
                                # Use default font
                                try:
                                    font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 12)
                                except (IOError, OSError) as e:
                                    logger.debug(f"Custom font not available, using default: {e}")
                                    font = ImageFont.load_default()
                                except Exception as e:
                                    logger.warning(f"Unexpected error loading font: {e}", exc_info=True)
                                    font = ImageFont.load_default()

                                # Draw text (first 500 chars to avoid overflow)
                                lines = text[:500].split("\n")
                                y_offset = 20
                                for line in lines[:30]:  # Max 30 lines
                                    if line.strip():
                                        draw.text((20, y_offset), line, fill="black", font=font)
                                        y_offset += 20
                        except Exception as e:
                            logger.debug(f"Could not add text to placeholder image: {e}")

                        images.append(img)

                    return images

        except Exception as e:
            logger.error(f"PDF to image conversion failed: {e}")
            return []

    async def _extract_and_process_images(
        self, pdf_data: bytes, use_advanced_comprehension: bool
    ) -> Dict[str, Any]:
        """Extract and process images from PDF."""
        try:
            images_found = 0
            image_descriptions = []

            # Try using PyMuPDF (fitz) which has excellent image extraction
            try:
                import fitz

                logger.debug("Using PyMuPDF for image extraction")
                pdf_document = fitz.open(stream=pdf_data, filetype="pdf")

                for page_num in range(pdf_document.page_count):
                    page = pdf_document[page_num]
                    image_list = page.get_images(full=True)

                    for img_index, img in enumerate(image_list):
                        xref = img[0]
                        base_image = pdf_document.extract_image(xref)

                        if base_image:
                            images_found += 1
                            image_info = {
                                "page": page_num + 1,
                                "index": img_index,
                                "format": base_image.get("ext", "unknown"),
                                "width": base_image.get("width", 0),
                                "height": base_image.get("height", 0),
                                "size_bytes": len(base_image.get("image", b"")),
                            }

                            # Basic description based on dimensions
                            if base_image.get("width", 0) > 500:
                                image_info["description"] = "Large image (possibly photo or chart)"
                            elif base_image.get("width", 0) > 200:
                                image_info["description"] = "Medium image (possibly icon or diagram)"
                            else:
                                image_info["description"] = "Small image (possibly icon or bullet point)"

                            image_descriptions.append(image_info)

                            # Advanced comprehension if requested and BYOK available
                            if use_advanced_comprehension and self.use_byok and self.byok_manager:
                                try:
                                    # Save image to temp file for processing
                                    import tempfile

                                    with tempfile.NamedTemporaryFile(
                                        delete=False, suffix=f".{base_image.get('ext', 'png')}"
                                    ) as tmp:
                                        tmp.write(base_image["image"])
                                        tmp_path = tmp.name

                                    # Use vision model to describe image
                                    from PIL import Image as PILImage
                                    import base64
                                    import io

                                    img_pil = PILImage.open(tmp_path)

                                    # Convert PIL image to base64 for vision API
                                    buffered = io.BytesIO()
                                    img_pil.save(buffered, format="PNG")
                                    img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')

                                    # Get vision description using BYOK handler
                                    try:
                                        byok_handler = self.byok_manager.get_handler(
                                            tenant_id="default",  # System-level operation
                                            db=None
                                        )

                                        # Use coordinated vision description
                                        vision_description = await byok_handler._get_coordinated_vision_description(
                                            image_payload=img_base64,
                                            tenant_plan="free",
                                            is_managed=True
                                        )

                                        if vision_description:
                                            image_info["ai_description"] = vision_description
                                            logger.info(f"Generated AI description for image on page {page_num + 1}")

                                    except Exception as vision_error:
                                        logger.warning(f"Vision API call failed: {vision_error}")
                                        # Fall back to basic description (already set above)

                                    # Clean up temp file
                                    os.unlink(tmp_path)

                                except Exception as e:
                                    logger.debug(f"Advanced image comprehension failed: {e}")

                pdf_document.close()

            except ImportError:
                logger.warning("PyMuPDF not available for image extraction")

                # Fallback: Try using PyPDF2 to count images
                try:
                    pdf_file = io.BytesIO(pdf_data)
                    pdf_reader = PyPDF2.PdfReader(pdf_file)

                    for page_num, page in enumerate(pdf_reader.pages):
                        if "/XObject" in page["/Resources"]:
                            xObject = page["/Resources"]["/XObject"].get_object()

                            for obj in xObject:
                                if xObject[obj]["/Subtype"] == "/Image":
                                    images_found += 1
                                    image_descriptions.append(
                                        {
                                            "page": page_num + 1,
                                            "description": "Image detected (limited info without PyMuPDF)",
                                        }
                                    )

                except Exception as e:
                    logger.debug(f"PyPDF2 image extraction failed: {e}")

            logger.info(f"Extracted {images_found} images from PDF")
            return {
                "images_found": images_found,
                "image_descriptions": image_descriptions,
                "success": True,
            }

        except Exception as e:
            logger.error(f"Image extraction failed: {e}")
            return {
                "images_found": 0,
                "image_descriptions": [],
                "success": False,
                "error": str(e),
            }

    def _combine_results(
        self,
        basic_result: Dict[str, Any],
        ocr_result: Optional[Dict[str, Any]],
        image_results: Optional[Dict[str, Any]],
        used_ocr: bool,
    ) -> Dict[str, Any]:
        """Combine results from different processing methods."""
        # Determine which text to use
        if used_ocr and ocr_result and ocr_result["success"]:
            best_text_result = ocr_result["best_result"]
        else:
            best_text_result = basic_result

        # Combine all information
        combined_result = {
            "processing_summary": {
                "used_ocr": used_ocr,
                "ocr_methods_tried": ocr_result["methods_tried"] if ocr_result else [],
                "best_method": best_text_result["method"],
                "total_pages": best_text_result["page_count"],
                "total_characters": best_text_result["total_chars"],
            },
            "extracted_content": {
                "text": best_text_result["extracted_text"],
                "page_breakdown": best_text_result["page_texts"],
                "images": image_results or {},
            },
            "service_status": self.service_status,
            "success": basic_result["success"] or (ocr_result and ocr_result["success"])
            if ocr_result
            else basic_result["success"],
        }