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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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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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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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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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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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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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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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def iob_to_label(label): |
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return label |
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def process_image(image): |
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print(type(image)) |
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width, height = image.size |
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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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outputs = model(**encoding) |
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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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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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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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return image |
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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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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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examples =[['example1.png'],['example2.png'],['example3.png']] |
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css = """.output_image, .input_image {height: 600px !important}""" |
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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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iface.launch(inline=False, share=False, debug=False) |
