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app.py

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+import os
+os.system('pip install pyyaml==5.1')
+# workaround: install old version of pytorch since detectron2 hasn't released packages for pytorch 1.9 (issue: https://github.com/facebookresearch/detectron2/issues/3158)
+#os.system('pip install torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html')
+
+
+
+## install PyTesseract
+os.system('pip install -q pytesseract')
+
+import gradio as gr
+import numpy as np
+from transformers import LayoutLMv3ForTokenClassification
+from datasets import load_dataset
+from PIL import Image, ImageDraw, ImageFont
+
+
+
+
+
+
+processor = LayoutLMv2Processor.from_pretrained("Ammar-alhaj-ali/LayoutLMv3-Fine-Tuning-FUNSD")
+model = LayoutLMv2ForTokenClassification.from_pretrained("Ammar-alhaj-ali/LayoutLMv3-Fine-Tuning-FUNSD")
+
+# load image example
+dataset = load_dataset("nielsr/funsd", split="test")
+image = Image.open(dataset[0]["image_path"]).convert("RGB")
+image = Image.open("./invoice.png")
+image.save("document.png")
+# define id2label, label2color
+labels = dataset.features['ner_tags'].feature.names
+id2label = {v: k for v, k in enumerate(labels)}
+label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}
+
+def unnormalize_box(bbox, width, height):
+     return [
+         width * (bbox[0] / 1000),
+         height * (bbox[1] / 1000),
+         width * (bbox[2] / 1000),
+         height * (bbox[3] / 1000),
+     ]
+
+def iob_to_label(label):
+    label = label[2:]
+    if not label:
+      return 'other'
+    return label
+
+def process_image(image):
+    width, height = image.size
+
+    # encode
+    encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt")
+    offset_mapping = encoding.pop('offset_mapping')
+
+    # forward pass
+    outputs = model(**encoding)
+
+    # get predictions
+    predictions = outputs.logits.argmax(-1).squeeze().tolist()
+    token_boxes = encoding.bbox.squeeze().tolist()
+
+    # only keep non-subword predictions
+    is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
+    true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
+    true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
+
+    # draw predictions over the image
+    draw = ImageDraw.Draw(image)
+    font = ImageFont.load_default()
+    for prediction, box in zip(true_predictions, true_boxes):
+        predicted_label = iob_to_label(prediction).lower()
+        draw.rectangle(box, outline=label2color[predicted_label])
+        draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
+    
+    return image
+
+
+title = "Interactive demo: LayoutLMv3"
+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']]
+
+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;} }"
+# css = ".output_image, .input_image {height: 600px !important}"
+
+css = ".image-preview {height: auto !important;}"
+
+iface = gr.Interface(fn=process_image, 
+                     inputs=gr.inputs.Image(type="pil"), 
+                     outputs=gr.outputs.Image(type="pil", label="annotated image"),
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples,
+                     css=css,
+                     enable_queue=True)
+iface.launch(debug=True)