app.py

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 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 LayoutLMv3Processor, LayoutLMv3ForTokenClassification
from datasets import load_dataset
from PIL import Image, ImageDraw, ImageFont, ImageColor

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")
#image = Image.open(dataset[0]["image_path"]).convert("RGB")
image = Image.open("./test0.jpeg")
# define id2label, label2color
labels = dataset.features['ner_tags'].feature.names
id2label = {v: k for v, k in enumerate(labels)}

#Need to get discrete colors for each labels
label_ints = np.random.randint(0, len(ImageColor.colormap.items()), 61)
label_color_pil = [k for k,_ in 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])
        draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
    
    return image


title = "Extracting Receipts: LayoutLMv3"
description = """<p> Demo for Microsoft's LayoutLMv3, a Transformer for state-of-the-art document image understanding tasks. </p>  <p> This particular model is fine-tuned from 
<a href="https://github.com/clovaai/cord">CORD</a> on the Consolidated Receipt Dataset, a dataset of receipts. If you search the 🤗 Hugging Face hub you will see other related models fine-tuned for other documents. This model is trained using fine-tuning to look for entities around menu items, subtotal, and total prices. To perform your own fine-tuning, take a look at the 
<a href="https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3">notebook by Niels</a>. </p>  <p> To try it out, simply upload an image or use the example image below and click 'Submit'. Results will show up in a few seconds. To see the output bigger, right-click on it, select 'Open image in new tab', and use your browser's zoom feature. </p>"""

article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2204.08387' target='_blank'>LayoutLMv3: Multi-modal Pre-training for Visually-Rich Document Understanding</a> | <a href='https://github.com/microsoft/unilm' target='_blank'>Github Repo</a></p>"
examples =[['test0.jpeg'],['test1.jpeg'],['test2.jpeg']]

css = "{ text-decoration: none; border-bottom:1px solid red; }"
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,
                     css = css,
                     examples=examples)
iface.launch(debug=True)