OfficeReader-MCP

""" Excel to Markdown converter with image extraction. """ import base64 from datetime import datetime from pathlib import Path from typing import Optional from io import BytesIO from openpyxl import load_workbook from openpyxl.drawing.image import Image as OpenpyxlImage from PIL import Image from .image_optimizer import ImageOptimizer class ExcelConverter: """Convert Excel files (.xlsx, .xls) to Markdown format.""" def __init__(self, cache_dir: Path, image_optimizer: ImageOptimizer): """ Initialize the Excel converter. Args: cache_dir: Directory for caching outputs image_optimizer: Image optimization utility """ self.cache_dir = cache_dir self.image_optimizer = image_optimizer def convert_file( self, file_path: str, extract_images: bool = True, image_format: str = "file", output_name: Optional[str] = None, conversion_dir: Path = None, images_dir: Path = None, ) -> dict: """ Convert Excel file to Markdown. Args: file_path: Path to Excel file extract_images: Whether to extract embedded images image_format: "file", "base64", or "both" output_name: Custom output name conversion_dir: Directory for this conversion images_dir: Directory for extracted images Returns: dict with markdown content, images, and metadata """ file_path = Path(file_path) if not file_path.exists(): raise FileNotFoundError(f"File not found: {file_path}") suffix = file_path.suffix.lower() if suffix not in [".xlsx", ".xls"]: raise ValueError(f"Unsupported file format: {suffix}") # Load workbook wb = load_workbook(file_path, data_only=True) markdown_parts = [] all_images = [] image_counter = 0 # Process each sheet for sheet_name in wb.sheetnames: ws = wb[sheet_name] # Add sheet header markdown_parts.append(f"# Sheet: {sheet_name}\n") # Extract data from sheet data = [] for row in ws.iter_rows(values_only=True): # Filter out completely empty rows if any(cell is not None and str(cell).strip() for cell in row): data.append(row) if data: # Convert to Markdown table md_table = self._create_markdown_table(data) markdown_parts.append(md_table) else: markdown_parts.append("*Empty sheet*\n") # Extract images from sheet if extract_images and hasattr(ws, "_images"): for img in ws._images: image_counter += 1 img_name = f"excel_image_{image_counter:03d}" # Get image data img_bytes = img._data() pil_img = Image.open(BytesIO(img_bytes)) # Optimize image optimized_data, ext = self.image_optimizer.optimize_image( pil_img, img_name ) img_filename = f"{img_name}{ext}" img_path = images_dir / img_filename if image_format in ["file", "both"]: with open(img_path, "wb") as f: f.write(optimized_data) all_images.append(str(img_path)) markdown_parts.append(f"\n\n") if image_format in ["base64", "both"]: b64_data = base64.b64encode(optimized_data).decode("utf-8") content_type = f"image/{ext.lstrip('.')}" if image_format == "base64": markdown_parts.append( f"\n\n" ) markdown_parts.append("\n---\n") # Extract metadata metadata = self._extract_metadata(wb, file_path) markdown = "\n".join(markdown_parts) return { "markdown": markdown, "images": all_images, "metadata": metadata, } def _create_markdown_table(self, data: list) -> str: """ Convert 2D data array to Markdown table. Args: data: List of row tuples Returns: Markdown formatted table """ if not data: return "" # Find maximum column count max_cols = max(len(row) for row in data) # Normalize rows to same length normalized_data = [] for row in data: normalized_row = list(row) + [None] * (max_cols - len(row)) normalized_data.append(normalized_row) md_rows = [] for i, row in enumerate(normalized_data): cells = [] for cell in row: # Convert cell value to string cell_text = str(cell) if cell is not None else "" # Escape pipe characters cell_text = cell_text.replace("|", "\\|").replace("\n", " ") cells.append(cell_text) md_rows.append("| " + " | ".join(cells) + " |") # Add header separator after first row if i == 0: separator = "| " + " | ".join(["---"] * len(cells)) + " |" md_rows.append(separator) return "\n".join(md_rows) + "\n" def _extract_metadata(self, workbook, file_path: Path) -> dict: """Extract Excel file metadata.""" props = workbook.properties return { "source": str(file_path), "converted_at": datetime.now().isoformat(), "title": props.title or "", "creator": props.creator or "", "created": str(props.created) if props.created else "", "modified": str(props.modified) if props.modified else "", "sheet_count": len(workbook.sheetnames), "sheet_names": workbook.sheetnames, }