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refactor: allow the usage of backends in the enrich models and generalize the interface (#742)
* fix get image with cropbox Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * allow the usage of backends in the enrich models and generalize the interface Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * move logic in BaseTextImageEnrichmentModel Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * renaming Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> --------- Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
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docs/examples/develop_formula_understanding.py
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88
docs/examples/develop_formula_understanding.py
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import logging
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from pathlib import Path
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from typing import Iterable
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from docling_core.types.doc import DocItemLabel, DoclingDocument, NodeItem, TextItem
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from docling.datamodel.base_models import InputFormat, ItemAndImageEnrichmentElement
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from docling.datamodel.pipeline_options import PdfPipelineOptions
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from docling.document_converter import DocumentConverter, PdfFormatOption
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from docling.models.base_model import BaseItemAndImageEnrichmentModel
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from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline
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class ExampleFormulaUnderstandingPipelineOptions(PdfPipelineOptions):
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do_formula_understanding: bool = True
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# A new enrichment model using both the document element and its image as input
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class ExampleFormulaUnderstandingEnrichmentModel(BaseItemAndImageEnrichmentModel):
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images_scale = 2.6
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def __init__(self, enabled: bool):
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self.enabled = enabled
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def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
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return (
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self.enabled
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and isinstance(element, TextItem)
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and element.label == DocItemLabel.FORMULA
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)
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def __call__(
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self,
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doc: DoclingDocument,
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element_batch: Iterable[ItemAndImageEnrichmentElement],
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) -> Iterable[NodeItem]:
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if not self.enabled:
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return
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for enrich_element in element_batch:
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enrich_element.image.show()
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yield enrich_element.item
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# How the pipeline can be extended.
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class ExampleFormulaUnderstandingPipeline(StandardPdfPipeline):
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def __init__(self, pipeline_options: ExampleFormulaUnderstandingPipelineOptions):
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super().__init__(pipeline_options)
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self.pipeline_options: ExampleFormulaUnderstandingPipelineOptions
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self.enrichment_pipe = [
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ExampleFormulaUnderstandingEnrichmentModel(
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enabled=self.pipeline_options.do_formula_understanding
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)
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]
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if self.pipeline_options.do_formula_understanding:
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self.keep_backend = True
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@classmethod
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def get_default_options(cls) -> ExampleFormulaUnderstandingPipelineOptions:
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return ExampleFormulaUnderstandingPipelineOptions()
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# Example main. In the final version, we simply have to set do_formula_understanding to true.
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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/2203.01017v2.pdf")
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pipeline_options = ExampleFormulaUnderstandingPipelineOptions()
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pipeline_options.do_formula_understanding = 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_cls=ExampleFormulaUnderstandingPipeline,
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pipeline_options=pipeline_options,
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)
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}
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)
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result = doc_converter.convert(input_doc_path)
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if __name__ == "__main__":
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main()
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