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86 lines
2.7 KiB
Python
86 lines
2.7 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
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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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# source = "https://arxiv.org/pdf/2408.09869" # document per local path or URL
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# source = "tests/data/2305.03393v1-pg9-img.png"
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# source = "tests/data/2305.03393v1-pg9.pdf"
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# source = "demo_data/page.png"
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# source = "demo_data/original_tables.pdf"
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sources = [
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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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# "demo_data/page.png",
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# "demo_data/original_tables.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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pipeline_options.artifacts_path = "model_artifacts"
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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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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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