Update app.py

app.py

@@ -1,16 +1,81 @@
+import gradio as gr
+import numpy as np
 from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification
-from PIL import Image
+from PIL import Image, ImageDraw, ImageFont
+import PIL
 
-processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
-model = LayoutLMv2ForTokenClassification.from_pretrained("microsoft/layoutlmv2-base-uncased")
+processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
+model = LayoutLMv2ForTokenClassification.from_pretrained("katanaml/layoutlmv2-finetuned-cord")
 
-image = Image.open("Labels-New\OriginalImages_cropped_ocr_results\IMG_1693.bmp").convert("RGB")
-words = ["hello", "world"]
-boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
-word_labels = [0, 1]
+# define id2label
+id2label = model.config.id2label
 
-encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
+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():
+  if v[2:] == '':
+    label2color['o']=label_color[k]
+  else:
+    label2color[v[2:]]=label_color[k]
 
-outputs = model(**encoding)
-loss = outputs.loss
-logits = outputs.logits
+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 'o'
+    return label
+
+def process_image(image):
+    width, height = image.size
+
+    # encode
+    encoding = processor(image, 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: LayoutLMv2 with CORD receipts dataset"
+description = "Demo for Microsoft's LayoutLMv2, a Transformer for state-of-the-art document image understanding tasks. 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. 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'>LayoutLMv2 Github Repo</a> | <a href='https://katanaml.io' target='_blank'>Katana ML</a> | <a href='https://github.com/katanaml/sparrow' target='_blank'>Sparrow Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=abaranovskij_cord' alt='visitor badge'></center>"
+examples =[['test0.jpeg'], ['test1.jpeg'], ['test2.jpeg']]
+
+css = ".output_image, .input_image {height: 40rem !important; width: 100% !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)