Upload app.py

app.py

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+import os
+# os.system('pip install gradio --upgrade')
+# os.system('pip install git+https://github.com/huggingface/transformers.git --upgrade')
+# 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 detectron2 that matches pytorch 1.8
+# # See https://detectron2.readthedocs.io/tutorials/install.html for instructions
+# os.system('pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html')
+# ## install PyTesseract
+# os.system('pip install -q pytesseract')
+import gradio as gr
+import numpy as np
+from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification
+from datasets import load_dataset
+from PIL import Image, ImageDraw, ImageFont
+import PIL
+
+
+processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
+model = LayoutLMv2ForTokenClassification.from_pretrained("MarkusDressel/cord")
+id2label = model.config.id2label
+
+label_ints = np.random.randint(0,len(PIL.ImageColor.colormap.items()),30)
+
+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]
+
+
+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], width=5)
+        draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
+    
+    return image
+title = "Cord demo: LayoutLMv2"
+description = "Demo for Microsoft's LayoutLMv2.This particular model is fine-tuned on CORD, a dataset of manually annotated receipts. It annotates the words appearing in the image in up to 30 classes. 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 =[['receipt_00189.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}"
+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)