Document Translation

def translate(text: str, src: str = "en", dest: str = "de") -> str:
    """
    Translate text using your preferred translation service.
    
    Replace this with your actual translation implementation:
    - Google Translate API
    - DeepL API
    - OpenAI API
    - Azure Translator
    - etc.
    """
    # Example with googletrans (uncomment to use):
    # from googletrans import Translator
    # translator = Translator()
    # translated = translator.translate(text, src=src, dest=dest)
    # return translated.text
    
    # Placeholder - replace with actual translation
    print("Warning: Using placeholder translation function")
    return text

conv_doc = conv_res.document
doc_filename = conv_res.input.file.name

# Save original
md_filename = output_dir / f"{doc_filename}-orig.md"
conv_doc.save_as_markdown(md_filename, image_mode=ImageRefMode.EMBEDDED)

# Translate all text elements
for element, _level in conv_res.document.iterate_items():
    if isinstance(element, TextItem):
        element.orig = element.text  # Store original
        element.text = translate(text=element.text, src="en", dest="de")
    
    elif isinstance(element, TableItem):
        for cell in element.data.table_cells:
            cell.text = translate(text=cell.text, src="en", dest="de")

# Save translated version
md_filename = output_dir / f"{doc_filename}-translated.md"
conv_doc.save_as_markdown(md_filename, image_mode=ImageRefMode.EMBEDDED)

from pathlib import Path
from docling_core.types.doc import ImageRefMode, TableItem, TextItem
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption

def translate(text: str, src: str = "en", dest: str = "de") -> str:
    """Replace with your translation implementation."""
    # Your translation code here
    return text

def translate_document(input_path: Path, output_dir: Path, src: str = "en", dest: str = "de"):
    """Translate a PDF document."""
    output_dir.mkdir(parents=True, exist_ok=True)
    
    # Configure pipeline
    pipeline_options = PdfPipelineOptions()
    pipeline_options.images_scale = 2.0
    pipeline_options.generate_page_images = True
    pipeline_options.generate_picture_images = True
    
    # Convert document
    doc_converter = DocumentConverter(
        format_options={
            InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
        }
    )
    
    conv_res = doc_converter.convert(input_path)
    doc_filename = conv_res.input.file.name
    
    # Save original
    orig_path = output_dir / f"{doc_filename}-{src}.md"
    conv_res.document.save_as_markdown(orig_path, image_mode=ImageRefMode.EMBEDDED)
    print(f"Saved original: {orig_path}")
    
    # Translate
    for element, _level in conv_res.document.iterate_items():
        if isinstance(element, TextItem):
            element.text = translate(element.text, src=src, dest=dest)
        elif isinstance(element, TableItem):
            for cell in element.data.table_cells:
                cell.text = translate(cell.text, src=src, dest=dest)
    
    # Save translated
    trans_path = output_dir / f"{doc_filename}-{dest}.md"
    conv_res.document.save_as_markdown(trans_path, image_mode=ImageRefMode.EMBEDDED)
    print(f"Saved translated: {trans_path}")

if __name__ == "__main__":
    translate_document(
        input_path=Path("document.pdf"),
        output_dir=Path("scratch"),
        src="en",
        dest="de"
    )

# Install Docling
pip install docling

# Install translation library of choice
pip install googletrans==4.0.0rc1  # Google Translate
# or
pip install deepl  # DeepL
# or
pip install openai  # OpenAI