import os os.system('pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu') 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("oussama/Layoutlm_Form_information_extraction") # load image example dataset = load_dataset("darentang/generated", 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 = {'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), ] def iob_to_label(label): return label def process_image(image): print(type(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 = "Extraction d'informations de factures en utilisant le modèle LayoutLMv3" description = "J'utilise LayoutLMv3 de Microsoft formé sur un ensemble de données de factures pour prédire le nom de l'émetteur de factures, l'adresse de l'émetteur de factures, le code postal de l'émetteur de factures, la date d'échéance, la TPS, la date de facturation, le numéro de facture, le sous-total et le total. Pour l'utiliser, il suffit de télécharger une image ou d'utiliser l'exemple d'image ci-dessous. Les résultats seront affichés en quelques secondes." 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=False, debug=False)