hm_ai_qc_report_tool/modules/campaigns/services.py

"""
Campaign PDF Parsing Service.

Handles parsing of campaign presentation PDFs using LlamaParse,
extracting both text content and page images for QC reference.
"""
import os
import logging
from datetime import datetime

logger = logging.getLogger(__name__)


def parse_campaign_pdf(presentation_id: int, app=None):
    """
    Parse a campaign presentation PDF and store extracted content.

    Extracts text and page images from the PDF using LlamaParse.
    Updates the CampaignPresentation record with parsed content.

    Args:
        presentation_id: ID of the CampaignPresentation record to parse
        app: Flask app instance (required when called from background thread)
    """
    from core.models.database import db
    from core.models.campaign_presentation import CampaignPresentation

    # This function expects to be called within an app context
    # (the caller is responsible for wrapping with app.app_context())
    presentation = CampaignPresentation.query.get(presentation_id)
    if not presentation:
        logger.error(f"CampaignPresentation {presentation_id} not found")
        return

    try:
        # Update status to parsing
        presentation.status = 'parsing'
        db.session.commit()

        pdf_path = presentation.pdf_path
        if not os.path.isfile(pdf_path):
            raise FileNotFoundError(f"PDF not found at {pdf_path}")

        # Create page images directory
        campaign_dir = os.path.dirname(pdf_path)
        pages_dir = os.path.join(campaign_dir, 'pages')
        os.makedirs(pages_dir, exist_ok=True)

        # Parse PDF
        logger.info(f"Parsing campaign PDF: {pdf_path}")
        result = _parse_pdf(pdf_path, pages_dir)

        # Update record with parsed content
        presentation.parsed_content = result['extracted_text']
        presentation.page_images_dir = pages_dir
        presentation.parsed_at = datetime.utcnow()
        presentation.status = 'ready'
        presentation.error_message = None
        db.session.commit()

        logger.info(
            f"Campaign PDF parsed successfully: {presentation.campaign_id} "
            f"({len(result['extracted_text'])} chars, "
            f"{result['image_count']} page images)"
        )

    except Exception as e:
        logger.error(f"Failed to parse campaign PDF: {e}", exc_info=True)
        presentation.status = 'error'
        presentation.error_message = str(e)
        db.session.commit()


def _parse_pdf(pdf_path: str, pages_dir: str) -> dict:
    """
    Parse a PDF file extracting text and page images.

    Uses the same LlamaParse approach as modules/hm_qc/checks/legacy/HM_parse.py
    but stores page images to disk instead of keeping them in memory.

    Args:
        pdf_path: Path to the PDF file
        pages_dir: Directory to store extracted page images

    Returns:
        dict with 'extracted_text', 'image_count'
    """
    # Lazy imports — only needed at parse time
    import nest_asyncio
    nest_asyncio.apply()
    from llama_parse import LlamaParse
    from PIL import Image

    # 1) Text extraction
    parser_text = LlamaParse(
        result_type="text",
        add_page_breaks=False,
        parsing_instruction=(
            "Extract all text from the PDF. "
            "Include all typography specifications, font names, sizes, positioning rules, "
            "copy text, and any guidelines or instructions."
        ),
        premium_mode=False,
    )

    documents = parser_text.load_data(pdf_path)
    if not documents:
        raise RuntimeError("No text found or PDF is empty")

    extracted_text = "\n".join(doc.text for doc in documents)

    # Save extracted text
    text_file_path = os.path.join(pages_dir, "extracted_text.txt")
    with open(text_file_path, "w", encoding="utf-8") as f:
        f.write(extracted_text)

    # 2) Page image extraction (multimodal)
    image_count = 0
    try:
        parser_multimodal = LlamaParse(
            result_type="markdown",
            add_page_breaks=False,
            parsing_instruction=(
                "Generate page images of the PDF document, "
                "including all visual layouts, mockups, and graphic examples."
            ),
            use_vendor_multimodal_model=True,
            vendor_multimodal_model_name="openai-gpt4o",
            premium_mode=False,
        )

        md_json_objs = parser_multimodal.get_json_result(pdf_path)
        image_dicts = parser_multimodal.get_images(md_json_objs, download_path=pages_dir)

        # Rename images with sequential page numbers
        for i, img_info in enumerate(image_dicts):
            src_path = img_info["path"]
            dst_path = os.path.join(pages_dir, f"page_{i+1:03d}.jpg")
            try:
                if os.path.exists(src_path):
                    # Ensure it's a valid image, then save as JPG
                    with Image.open(src_path) as img:
                        img.convert('RGB').save(dst_path, 'JPEG', quality=90)
                    # Remove original if different path
                    if os.path.abspath(src_path) != os.path.abspath(dst_path):
                        os.remove(src_path)
                    image_count += 1
            except Exception as e:
                logger.warning(f"Failed to process page image {i+1}: {e}")
                continue

    except Exception as e:
        logger.warning(f"Multimodal parsing failed (text still available): {e}")

    return {
        'extracted_text': extracted_text,
        'image_count': image_count
    }


def get_page_image_paths(page_images_dir: str) -> list:
    """
    Get sorted list of page image paths from a campaign's pages directory.

    Args:
        page_images_dir: Path to the pages directory

    Returns:
        Sorted list of image file paths
    """
    if not page_images_dir or not os.path.isdir(page_images_dir):
        return []

    image_files = []
    for f in sorted(os.listdir(page_images_dir)):
        if f.lower().endswith(('.jpg', '.jpeg', '.png')) and f.startswith('page_'):
            image_files.append(os.path.join(page_images_dir, f))

    return image_files