slowly

app.py

@@ -20,13 +20,14 @@ processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base")
 model = LayoutLMv3ForTokenClassification.from_pretrained("nielsr/layoutlmv3-finetuned-cord")
 
 # load image example
-#dataset = load_dataset("nielsr/cord-layoutlmv3", split="test")
+dataset = load_dataset("nielsr/cord-layoutlmv3", split="test")
 #image = Image.open(dataset[0]["image_path"]).convert("RGB")
 image = Image.open("./test0.jpeg")
 #image.save("document.png")
 # 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'}
 
 def unnormalize_box(bbox, width, height):