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101 lines
3.2 KiB
Python
101 lines
3.2 KiB
Python
import json
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import time
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from pathlib import Path
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import yaml
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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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VlmPipelineOptions,
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granite_vision_vlm_conversion_options,
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smoldocling_vlm_conversion_options,
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smoldocling_vlm_mlx_conversion_options,
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)
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from docling.datamodel.settings import settings
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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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"tests/data/2305.03393v1-pg9-img.png",
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]
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## Use experimental VlmPipeline
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pipeline_options = VlmPipelineOptions()
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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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## On GPU systems, enable flash_attention_2 with CUDA:
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# pipeline_options.accelerator_options.device = AcceleratorDevice.CUDA
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# pipeline_options.accelerator_options.cuda_use_flash_attention2 = True
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## Pick a VLM model. We choose SmolDocling-256M by default
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# pipeline_options.vlm_options = smoldocling_vlm_conversion_options
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## Pick a VLM model. Fast Apple Silicon friendly implementation for SmolDocling-256M via MLX
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pipeline_options.vlm_options = smoldocling_vlm_mlx_conversion_options
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## Alternative VLM models:
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# pipeline_options.vlm_options = granite_vision_vlm_conversion_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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## Set up pipeline for PDF or image inputs
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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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for page in res.pages:
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print("")
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print("Predicted page in DOCTAGS:")
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print(page.predictions.vlm_response.text)
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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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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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