🐛 update Use this model snippets for PaddleOCR models (#1645)

Hi HuggingFace team,

Thank you very much for supporting the PaddleOCR snippets. However, the
code required for PaddleOCR models differs depending on the specific
task. To address this, I have updated the model tags to include task
information. The snippets will also need to be updated accordingly.

Please review this PR, and feel free to let me know if any modifications
are needed.

Thank you for your attention and support.

Author: TingquanGao

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

@@ -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