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  1. app.py +114 -0
  2. packages.txt +6 -0
  3. requirements.txt +5 -0
app.py ADDED
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+ import os
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+ os.system('pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu')
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+
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+ import gradio as gr
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+ import numpy as np
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+ from transformers import AutoModelForTokenClassification
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+ from datasets.features import ClassLabel
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+ from transformers import AutoProcessor
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+ from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
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+ import torch
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+ from datasets import load_metric
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+ from transformers import LayoutLMv3ForTokenClassification
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+ from transformers.data.data_collator import default_data_collator
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+
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+
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+ from transformers import AutoModelForTokenClassification
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+ from datasets import load_dataset
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+ from PIL import Image, ImageDraw, ImageFont
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+
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+
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+ processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=True)
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+ model = AutoModelForTokenClassification.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-invoice")
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+
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+
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+
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+ # load image example
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+ dataset = load_dataset("darentang/generated", split="test")
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+ Image.open(dataset[2]["image_path"]).convert("RGB").save("example1.png")
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+ Image.open(dataset[1]["image_path"]).convert("RGB").save("example2.png")
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+ Image.open(dataset[0]["image_path"]).convert("RGB").save("example3.png")
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+ # define id2label, label2color
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+ labels = dataset.features['ner_tags'].feature.names
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+ id2label = {v: k for v, k in enumerate(labels)}
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+ label2color = {
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+ "B-ABN": 'blue',
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+ "B-BILLER": 'blue',
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+ "B-BILLER_ADDRESS": 'green',
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+ "B-BILLER_POST_CODE": 'orange',
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+ "B-DUE_DATE": "blue",
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+ "B-GST": 'green',
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+ "B-INVOICE_DATE": 'violet',
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+ "B-INVOICE_NUMBER": 'orange',
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+ "B-SUBTOTAL": 'green',
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+ "B-TOTAL": 'blue',
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+ "I-BILLER_ADDRESS": 'blue',
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+ "O": 'orange'
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+ }
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+
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+ def unnormalize_box(bbox, width, height):
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+ return [
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+ width * (bbox[0] / 1000),
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+ height * (bbox[1] / 1000),
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+ width * (bbox[2] / 1000),
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+ height * (bbox[3] / 1000),
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+ ]
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+
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+
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+ def iob_to_label(label):
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+ return label
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+
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+
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+
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+ def process_image(image):
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+
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+ print(type(image))
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+ width, height = image.size
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+
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+ # encode
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+ encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt")
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+ offset_mapping = encoding.pop('offset_mapping')
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+
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+ # forward pass
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+ outputs = model(**encoding)
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+
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+ # get predictions
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+ predictions = outputs.logits.argmax(-1).squeeze().tolist()
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+ token_boxes = encoding.bbox.squeeze().tolist()
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+
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+ # only keep non-subword predictions
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+ is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
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+ true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
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+ true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
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+
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+ # draw predictions over the image
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+ draw = ImageDraw.Draw(image)
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+ font = ImageFont.load_default()
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+ for prediction, box in zip(true_predictions, true_boxes):
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+ predicted_label = iob_to_label(prediction)
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+ draw.rectangle(box, outline=label2color[predicted_label])
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+ draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
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+
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+ return image
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+
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+
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+ title = "Invoice Information extraction using LayoutLMv3 model"
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+ description = "Invoice Information Extraction - We use Microsoft's LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
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+
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+ article="<b>References</b><br>[1] Y. Xu et al., “LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking.” 2022. <a href='https://arxiv.org/abs/2204.08387'>Paper Link</a><br>[2] <a href='https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3'>LayoutLMv3 training and inference</a>"
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+
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+ examples =[['example1.png'],['example2.png'],['example3.png']]
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+
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+ css = """.output_image, .input_image {height: 600px !important}"""
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+
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+ iface = gr.Interface(fn=process_image,
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+ inputs=gr.inputs.Image(type="pil"),
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+ outputs=gr.outputs.Image(type="pil", label="annotated image"),
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+ title=title,
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+ description=description,
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+ article=article,
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+ examples=examples,
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+ css=css,
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+ analytics_enabled = True, enable_queue=True)
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+
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+ iface.launch(inline=False, share=False, debug=False)
packages.txt ADDED
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+ ffmpeg
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+ libsm6
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+ libxext6 -y
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+ libgl1
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+ -y libgl1-mesa-glx
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+ tesseract-ocr
requirements.txt ADDED
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+ git+https://github.com/huggingface/transformers.git
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+ PyYAML==6.0
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+ pytesseract==0.3.9
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+ datasets==2.2.2
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+ seqeval==1.2.2