added color code

app.py

@@ -27,8 +27,15 @@ image = Image.open("./test0.jpeg")
 # define id2label, label2color
 labels = dataset.features['ner_tags'].feature.names
 id2label = {v: k for v, k in enumerate(labels)}
-print(id2label)
-label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}
+
+label_ints = np.random.randint(0, len(PIL.ImageColor.colormap.items()), 60)
+label_color_pil = [k for k,_ in PIL.ImageColor.colormap.items()]
+label_color = [label_color_pil[i] for i in label_ints]
+label2color = {}
+for k,v in id2label.items():
+    label2color[v[2:]]=label_color[k]
+
+#label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}
 
 def unnormalize_box(bbox, width, height):
      return [
@@ -74,10 +81,10 @@ def process_image(image):
     return image
 
 
-title = "Interactive demo: LayoutLMv2"
-description = "Demo for Microsoft's LayoutLMv2, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on FUNSD, a dataset of manually annotated forms. It annotates the words appearing in the image as QUESTION/ANSWER/HEADER/OTHER. To use it, simply upload an image or use the example image below and click 'Submit'. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select 'Open image in new tab'."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2012.14740' target='_blank'>LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding</a> | <a href='https://github.com/microsoft/unilm' target='_blank'>Github Repo</a></p>"
-examples =[['document.png']]
+title = "Interactive demo: LayoutLMv3"
+description = "Demo for Microsoft's LayoutLMv3, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on FUNSD, a dataset of manually annotated forms. It annotates the words appearing in the image as QUESTION/ANSWER/HEADER/OTHER. To use it, simply upload an image or use the example image below and click 'Submit'. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select 'Open image in new tab'."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2204.08387' target='_blank'>LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding</a> | <a href='https://github.com/microsoft/unilm' target='_blank'>Github Repo</a></p>"
+examples =[['test0.jpeg','test1.jpeg','test2.jpeg']]
 
 css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
 #css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"