import os os.system('pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu') os.system('pip install -q git+https://github.com/huggingface/transformers.git') os.system('pip install pytesseract') import gradio as gr import numpy as np from transformers import AutoModelForTokenClassification from datasets.features import ClassLabel from transformers import AutoProcessor from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D import torch from datasets import load_metric from transformers import LayoutLMv3ForTokenClassification from transformers.data.data_collator import default_data_collator from transformers import AutoModelForTokenClassification from datasets import load_dataset from PIL import Image, ImageDraw, ImageFont processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=True) model = AutoModelForTokenClassification.from_pretrained("layoutlmv3-finetuned-invoice") # load image example dataset = load_dataset("balabis/invoices", use_auth_token=True, split="test") Image.open(dataset[2]["image_path"]).convert("RGB").save("example1.png") Image.open(dataset[1]["image_path"]).convert("RGB").save("example2.png") Image.open(dataset[0]["image_path"]).convert("RGB").save("example3.png") # define id2label, label2color labels = dataset.features['ner_tags'].feature.names id2label = {v: k for v, k in enumerate(labels)} label2color = { "INVOICE_NUM": 'blue', "INVOICE_DATE": 'blue', "BUYER": 'green', "SELLER": 'orange', "TOTAL_SUM": "blue", "TOTAL_VAT": 'green', "PAY_UNTIL": 'violet' } 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): 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) 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 = "Invoice Information extraction using LayoutLMv3 model" 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." article="References
[1] Y. Xu et al., “LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking.” 2022. Paper Link
[2] LayoutLMv3 training and inference" examples =[['example1.png'],['example2.png'],['example3.png']] css = """.output_image, .input_image {height: 600px !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, analytics_enabled = True, enable_queue=True) iface.launch(inline=False, share=True, debug=True)