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🐛 update Use this model snippets for PaddleOCR models #1645

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Jul 23, 2025
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🐛 update Use this model snippets for PaddleOCR models #1645

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53 changes: 48 additions & 5 deletions packages/tasks/src/model-libraries-snippets.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -1018,16 +1018,59 @@ export const paddlenlp = (model: ModelData): string[] => {
}
};

export const paddleocr = (model: ModelData): string[] => [
`# pip install paddleocr
from paddleocr import TextDetection
model = TextDetection(model_name="${model.id}")
export const paddleocr = (model: ModelData): string[] => {
const mapping: Record<string, { className: string }> = {
textline_detection: { className: "TextDetection" },
textline_recognition: { className: "TextRecognition" },
seal_text_detection: { className: "SealTextDetection" },
doc_img_unwarping: { className: "TextImageUnwarping" },
doc_img_orientation_classification: { className: "DocImgOrientationClassification" },
textline_orientation_classification: { className: "TextLineOrientationClassification" },
chart_parsing: { className: "ChartParsing" },
formula_recognition: { className: "FormulaRecognition" },
layout_detection: { className: "LayoutDetection" },
table_cells_detection: { className: "TableCellsDetection" },
wired_table_classification: { className: "TableClassification" },
table_structure_recognition: { className: "TableStructureRecognition" },
};

if (model.tags.includes("doc_vlm")) {
return [
`# pip install paddleocr
from paddleocr import DocVLM
model = DocVLM(model_name="${model.id}")
output = model.predict(
input={"image": "path/to/image.png", "query": "Parsing this image and output the content in Markdown format."},
batch_size=1
)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/res.json")`,
];
}

for (const tag of model.tags) {
if (tag in mapping) {
const { className } = mapping[tag];
return [
`# pip install paddleocr
from paddleocr import ${className}
model = ${className}(model_name="${model.id}")
output = model.predict(input="path/to/image.png", batch_size=1)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/res.json")`,
];
];
}
}

return [
`# Please refer to the document for information on how to use the model.
# https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/module_overview.html`,
];
};

export const perception_encoder = (model: ModelData): string[] => {
const clip_model = `# Use PE-Core models as CLIP models
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