Update app.py

app.py

@@ -1,76 +1,71 @@
 import os
-os.system('pip install pip --upgrade')
-os.system('pip install -q git+https://github.com/huggingface/transformers.git')
-os.system('pip install pyyaml==5.1')
+
+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')
+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')
+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')
+os.system("pip install -q pytesseract")
 
 import gradio as gr
 import numpy as np
-
+from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
 from datasets import load_dataset
 from PIL import Image, ImageDraw, ImageFont
 
-from transformers import AutoModelForTokenClassification, AutoProcessor
-
-
-processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
-
-
-model = AutoModelForTokenClassification.from_pretrained("jinhybr/OCR-LayoutLMv3")
-
+processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base")
+model = LayoutLMv3ForTokenClassification.from_pretrained(
+    "jinhybr/OCR-LayoutLMv3"
+)
 
 # load image example
-dataset = load_dataset("nielsr/funsd-layoutlmv3", split="test")
-Image.open(dataset[0]["image_path"]).convert("RGB").save("example_lm3.png")
-
+dataset = load_dataset("nielsr/funsd", split="test")
+image = Image.open(dataset[0]["image_path"]).convert("RGB")
+image = Image.open("./example_lm3.png")
+image.save("document.png")
 
-# define id2label, label2color
-labels = dataset.features['ner_tags'].feature.names
+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'}
-
-
-
-#####
-
-
-
-
-
-
-
-
-
+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),
-     ]
+    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 "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')
+    encoding = processor(
+        image, truncation=True, return_offsets_mapping=True, return_tensors="pt"
+    )
+    offset_mapping = encoding.pop("offset_mapping")
 
     # forward pass
     outputs = model(**encoding)
@@ -80,9 +75,15 @@ def process_image(image):
     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]]
+    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)
@@ -90,29 +91,36 @@ def process_image(image):
     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)
-    
+        draw.text(
+            (box[0] + 10, box[1] - 10),
+            text=predicted_label,
+            fill=label2color[predicted_label],
+            font=font,
+        )
+
     return image
 
 
-title = "Interactive demo: OCR Document Parser"
-description = "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 =[['example_lm3.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'>LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking</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 = "@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 = 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)