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fixed the MyPy

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Signed-off-by: Peter Staar <taa@zurich.ibm.com>
This commit is contained in:
Peter Staar
2025-05-14 17:51:43 +02:00
parent a3716b1961
commit 7c67d2b2fe
10 changed files with 158 additions and 245 deletions
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54
docs/examples/minimal_vlm_pipeline.py
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@@ -11,10 +11,15 @@ from docling.datamodel.pipeline_options import (
InferenceFramework,
ResponseFormat,
VlmPipelineOptions,
smoldocling_vlm_mlx_conversion_options,
smoldocling_vlm_conversion_options,
granite_vision_vlm_conversion_options,
granite_vision_vlm_mlx_conversion_options,
granite_vision_vlm_ollama_conversion_options,
phi_vlm_conversion_options,
pixtral_12b_vlm_conversion_options,
pixtral_12b_vlm_mlx_conversion_options,
qwen25_vl_3b_vlm_mlx_conversion_options,
smoldocling_vlm_conversion_options,
smoldocling_vlm_mlx_conversion_options,
)
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.pipeline.vlm_pipeline import VlmPipeline
@@ -28,6 +33,7 @@ sources = [
pipeline_options = VlmPipelineOptions()
# If force_backend_text = True, text from backend will be used instead of generated text
pipeline_options.force_backend_text = False
pipeline_options.generate_page_images = True
## On GPU systems, enable flash_attention_2 with CUDA:
# pipeline_options.accelerator_options.device = AcceleratorDevice.CUDA
@@ -37,11 +43,13 @@ pipeline_options.force_backend_text = False
# pipeline_options.vlm_options = smoldocling_vlm_conversion_options
## Pick a VLM model. Fast Apple Silicon friendly implementation for SmolDocling-256M via MLX
pipeline_options.vlm_options = smoldocling_vlm_mlx_conversion_options
# pipeline_options.vlm_options = smoldocling_vlm_mlx_conversion_options
## Alternative VLM models:
# pipeline_options.vlm_options = granite_vision_vlm_conversion_options
pipeline_options.vlm_options = phi_vlm_conversion_options
"""
pixtral_vlm_conversion_options = HuggingFaceVlmOptions(
repo_id="mistralai/Pixtral-12B-Base-2409",
@@ -105,7 +113,7 @@ converter = DocumentConverter(
pipeline_cls=VlmPipeline,
pipeline_options=pipeline_options,
),
}
},
)
out_path = Path("scratch")
@@ -121,39 +129,44 @@ for source in sources:
res = converter.convert(source)
print("")
#print(res.document.export_to_markdown())
# print(res.document.export_to_markdown())
for i,page in enumerate(res.pages):
model_id = pipeline_options.vlm_options.repo_id.replace("/", "_")
fname = f"{model_id}-{res.input.file.stem}"
for i, page in enumerate(res.pages):
print("")
print(f" ---------- Predicted page {i} in {pipeline_options.vlm_options.response_format}:")
print(
f" ---------- Predicted page {i} in {pipeline_options.vlm_options.response_format}:"
)
print(page.predictions.vlm_response.text)
print(f" ---------- ")
print(" ---------- ")
print("===== Final output of the converted document =======")
with (out_path / f"{res.input.file.stem}.json").open("w") as fp:
with (out_path / f"{fname}.json").open("w") as fp:
fp.write(json.dumps(res.document.export_to_dict()))
res.document.save_as_json(
out_path / f"{res.input.file.stem}.json",
out_path / f"{fname}.json",
image_mode=ImageRefMode.PLACEHOLDER,
)
print(f" => produced {out_path / res.input.file.stem}.json")
print(f" => produced {out_path / fname}.json")
res.document.save_as_markdown(
out_path / f"{res.input.file.stem}.md",
out_path / f"{fname}.md",
image_mode=ImageRefMode.PLACEHOLDER,
)
print(f" => produced {out_path / res.input.file.stem}.md")
print(f" => produced {out_path / fname}.md")
res.document.save_as_html(
out_path / f"{res.input.file.stem}.html",
out_path / f"{fname}.html",
image_mode=ImageRefMode.EMBEDDED,
labels=[*DEFAULT_EXPORT_LABELS, DocItemLabel.FOOTNOTE],
# split_page_view=True,
split_page_view=True,
)
print(f" => produced {out_path / res.input.file.stem}.html")
print(f" => produced {out_path / fname}.html")
pg_num = res.document.num_pages()
print("")
inference_time = time.time() - start_time
@@ -161,4 +174,3 @@ for source in sources:
f"Total document prediction time: {inference_time:.2f} seconds, pages: {pg_num}"
)
print("====================================================")