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Runtime error
| 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("Theivaprakasham/layoutlmv3-finetuned-invoice") | |
| # 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 = { | |
| "B-ABN": 'blue', | |
| "B-BILLER": 'blue', | |
| "B-BILLER_ADDRESS": 'green', | |
| "B-BILLER_POST_CODE": 'orange', | |
| "B-DUE_DATE": "blue", | |
| "B-GST": 'green', | |
| "B-INVOICE_DATE": 'violet', | |
| "B-INVOICE_NUMBER": 'orange', | |
| "B-SUBTOTAL": 'green', | |
| "B-TOTAL": 'blue', | |
| "I-BILLER_ADDRESS": 'blue', | |
| "O": 'orange' | |
| } | |
| 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 = "Official Document Layout Scanner for a2i competition" | |
| description = "This is a web app for scanning official documents that will extract the layout from the documents automatically." | |
| 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, | |
| examples=examples, | |
| css=css, | |
| analytics_enabled = True, enable_queue=True) | |
| iface.launch(inline=False, share=False, debug=False) |