docling/examples/convert.py

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2024-07-15 09:42:42 +02:00
import json
import logging
import time
from pathlib import Path
from typing import Iterable
from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
from docling.datamodel.base_models import ConversionStatus, PipelineOptions
from docling.datamodel.document import ConvertedDocument, DocumentConversionInput
from docling.document_converter import DocumentConverter
_log = logging.getLogger(__name__)
def export_documents(
converted_docs: Iterable[ConvertedDocument],
output_dir: Path,
):
output_dir.mkdir(parents=True, exist_ok=True)
success_count = 0
failure_count = 0
for doc in converted_docs:
if doc.status == ConversionStatus.SUCCESS:
success_count += 1
doc_filename = doc.input.file.stem
# Export Deep Search document JSON format:
with (output_dir / f"{doc_filename}.json").open("w") as fp:
fp.write(json.dumps(doc.render_as_dict()))
# Export Markdown format:
with (output_dir / f"{doc_filename}.md").open("w") as fp:
fp.write(doc.render_as_markdown())
else:
_log.info(f"Document {doc.input.file} failed to convert.")
failure_count += 1
_log.info(
f"Processed {success_count + failure_count} docs, of which {failure_count} failed"
)
def main():
logging.basicConfig(level=logging.INFO)
input_doc_paths = [
Path("./test/data/2206.01062.pdf"),
Path("./test/data/2203.01017v2.pdf"),
Path("./test/data/2305.03393v1.pdf"),
]
artifacts_path = DocumentConverter.download_models_hf()
doc_converter = DocumentConverter(artifacts_path=artifacts_path)
input = DocumentConversionInput.from_paths(input_doc_paths)
start_time = time.time()
converted_docs = doc_converter.convert(input)
export_documents(converted_docs, output_dir=Path("./scratch"))
end_time = time.time() - start_time
_log.info(f"All documents were converted in {end_time:.2f} seconds.")
if __name__ == "__main__":
main()