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97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
import json
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import os
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import time
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from pathlib import Path
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from urllib.parse import urlparse
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import yaml
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from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.pipeline_options import PdfPipelineOptions, SmolDoclingOptions
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from docling.document_converter import DocumentConverter, PdfFormatOption
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from docling.pipeline.vlm_pipeline import VlmPipeline
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sources = [
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# "https://arxiv.org/pdf/2408.09869",
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# "tests/data/2305.03393v1-pg9-img.png",
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"tests/data/2305.03393v1-pg9.pdf",
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]
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pipeline_options = PdfPipelineOptions()
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pipeline_options.generate_page_images = True
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# If force_backend_text = True, text from backend will be used instead of generated text
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pipeline_options.force_backend_text = False
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pipeline_options.artifacts_path = "model_artifacts/SmolDocling_2.7_DT_0.7"
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vlm_options = SmolDoclingOptions(
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artifacts_path="model_artifacts/SmolDocling_2.7_DT_0.7",
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question="Perform Layout Analysis.",
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load_in_8bit=True,
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llm_int8_threshold=6.0,
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quantized=False,
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)
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pipeline_options.vlm_options = vlm_options
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from docling_core.types.doc import DocItemLabel, ImageRefMode
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from docling_core.types.doc.document import DEFAULT_EXPORT_LABELS
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converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(
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pipeline_cls=VlmPipeline,
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pipeline_options=pipeline_options,
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),
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InputFormat.IMAGE: PdfFormatOption(
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pipeline_cls=VlmPipeline,
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pipeline_options=pipeline_options,
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),
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}
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)
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out_path = Path("scratch")
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out_path.mkdir(parents=True, exist_ok=True)
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for source in sources:
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start_time = time.time()
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print("================================================")
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print("Processing... {}".format(source))
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print("================================================")
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print("")
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res = converter.convert(source)
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print("------------------------------------------------")
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print("MD:")
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print("------------------------------------------------")
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print("")
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print(res.document.export_to_markdown())
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# with (out_path / f"{res.input.file.stem}.html").open("w") as fp:
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# fp.write(res.document.export_to_html())
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res.document.save_as_html(
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filename=Path("{}/{}.html".format(out_path, res.input.file.stem)),
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image_mode=ImageRefMode.REFERENCED,
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labels=[*DEFAULT_EXPORT_LABELS, DocItemLabel.FOOTNOTE],
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)
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with (out_path / f"{res.input.file.stem}.json").open("w") as fp:
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fp.write(json.dumps(res.document.export_to_dict()))
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with (out_path / f"{res.input.file.stem}.yaml").open("w") as fp:
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fp.write(yaml.safe_dump(res.document.export_to_dict()))
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pg_num = res.document.num_pages()
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print("")
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inference_time = time.time() - start_time
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print(
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f"Total document prediction time: {inference_time:.2f} seconds, pages: {pg_num}"
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)
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print("================================================")
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print("done!")
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print("================================================")
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