update Use this model snippets for PaddleOCR's models

Author: TingquanGao

packages/tasks/src/model-libraries-snippets.ts

@@ -1018,16 +1018,60 @@ export const paddlenlp = (model: ModelData): string[] => {
 	}
 };
 
-export const paddleocr = (model: ModelData): string[] => [
-	`# pip install paddleocr
-from paddleocr import TextDetection
-model = TextDetection(model_name="${model.id}")
+export const paddleocr = (model: ModelData): string[] => {
+	const mapping: Record<string, { className: string }> = {
+		textline_detection: { className: "TextDetection" },
+		textline_recognition: { className: "TextRecognition" },
+		seal_text_detection: { className: "SealTextDetection" },
+		doc_img_unwarping: { className: "TextImageUnwarping" },
+		doc_img_orientation_classification: { className: "DocImgOrientationClassification" },
+		textline_orientation_classification: { className: "TextLineOrientationClassification" },
+		doc_vlm: { className: "DocVLM" },
+		chart_parsing: { className: "ChartParsing" },
+		formula_recognition: { className: "FormulaRecognition" },
+		layout_detection: { className: "LayoutDetection" },
+		table_cells_detection: { className: "TableCellsDetection" },
+		wired_table_classification: { className: "TableClassification" },
+		table_structure_recognition: { className: "TableStructureRecognition" },
+  };
+
+	if (model.tags.includes("doc_vlm")) {
+		return [
+			`# pip install paddleocr
+from paddleocr import DocVLM
+model = DocVLM(model_name="${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