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* add coverage calculation and push Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * new codecov version and usage of token Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * enable ruff formatter instead of black and isort Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * apply ruff lint fixes Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * apply ruff unsafe fixes Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * add removed imports Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * runs 1 on linter issues Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * finalize linter fixes Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * Update pyproject.toml Co-authored-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> Signed-off-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> --------- Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> Signed-off-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Co-authored-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>
176 lines
5.8 KiB
Python
176 lines
5.8 KiB
Python
import json
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import logging
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import time
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from pathlib import Path
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.pipeline_options import (
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AcceleratorDevice,
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AcceleratorOptions,
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PdfPipelineOptions,
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)
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from docling.document_converter import DocumentConverter, PdfFormatOption
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_log = logging.getLogger(__name__)
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def main():
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logging.basicConfig(level=logging.INFO)
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input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
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###########################################################################
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# The following sections contain a combination of PipelineOptions
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# and PDF Backends for various configurations.
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# Uncomment one section at the time to see the differences in the output.
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# PyPdfium without EasyOCR
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# --------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = False
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = False
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(
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# pipeline_options=pipeline_options, backend=PyPdfiumDocumentBackend
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# )
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# }
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# )
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# PyPdfium with EasyOCR
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# -----------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(
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# pipeline_options=pipeline_options, backend=PyPdfiumDocumentBackend
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# )
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# }
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# )
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# Docling Parse without EasyOCR
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# -------------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = False
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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# Docling Parse with EasyOCR
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# ----------------------
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pipeline_options = PdfPipelineOptions()
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pipeline_options.do_ocr = True
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pipeline_options.do_table_structure = True
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pipeline_options.table_structure_options.do_cell_matching = True
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pipeline_options.ocr_options.lang = ["es"]
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pipeline_options.accelerator_options = AcceleratorOptions(
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num_threads=4, device=AcceleratorDevice.AUTO
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)
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doc_converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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}
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)
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# Docling Parse with EasyOCR (CPU only)
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# ----------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.ocr_options.use_gpu = False # <-- set this.
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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# Docling Parse with Tesseract
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# ----------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# pipeline_options.ocr_options = TesseractOcrOptions()
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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# Docling Parse with Tesseract CLI
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# ----------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# pipeline_options.ocr_options = TesseractCliOcrOptions()
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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# Docling Parse with ocrmac(Mac only)
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# ----------------------
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# pipeline_options = PdfPipelineOptions()
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# pipeline_options.do_ocr = True
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# pipeline_options.do_table_structure = True
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# pipeline_options.table_structure_options.do_cell_matching = True
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# pipeline_options.ocr_options = OcrMacOptions()
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# doc_converter = DocumentConverter(
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# format_options={
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# InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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# }
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# )
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###########################################################################
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start_time = time.time()
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conv_result = doc_converter.convert(input_doc_path)
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end_time = time.time() - start_time
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_log.info(f"Document converted in {end_time:.2f} seconds.")
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## Export results
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output_dir = Path("scratch")
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output_dir.mkdir(parents=True, exist_ok=True)
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doc_filename = conv_result.input.file.stem
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# Export Deep Search document JSON format:
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with (output_dir / f"{doc_filename}.json").open("w", encoding="utf-8") as fp:
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fp.write(json.dumps(conv_result.document.export_to_dict()))
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# Export Text format:
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with (output_dir / f"{doc_filename}.txt").open("w", encoding="utf-8") as fp:
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fp.write(conv_result.document.export_to_text())
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# Export Markdown format:
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with (output_dir / f"{doc_filename}.md").open("w", encoding="utf-8") as fp:
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fp.write(conv_result.document.export_to_markdown())
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# Export Document Tags format:
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with (output_dir / f"{doc_filename}.doctags").open("w", encoding="utf-8") as fp:
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fp.write(conv_result.document.export_to_document_tokens())
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if __name__ == "__main__":
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main()
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