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import os |
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os.system('pip install -q torch==1.10.0+cu111 torchvision==0.11+cu111 -f https://download.pytorch.org/whl/torch_stable.html') |
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os.system('pip install git+https://github.com/facebookresearch/detectron2.git') |
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import detectron2 |
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from detectron2.utils.logger import setup_logger |
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setup_logger() |
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import gradio as gr |
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import re |
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import string |
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from operator import itemgetter |
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import collections |
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import pypdf |
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from pypdf import PdfReader |
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from pypdf.errors import PdfReadError |
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import pdf2image |
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from pdf2image import convert_from_path |
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import langdetect |
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from langdetect import detect_langs |
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import pandas as pd |
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import numpy as np |
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import random |
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import tempfile |
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import itertools |
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from matplotlib import font_manager |
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from PIL import Image, ImageDraw, ImageFont |
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import cv2 |
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import sys |
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sys.path.insert(0, 'files/') |
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import functions |
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from functions import * |
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os.system('python -m pip install --upgrade pip') |
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model_id_lilt = "pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384" |
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model_id_layoutxlm = "pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384" |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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import transformers |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer_lilt = AutoTokenizer.from_pretrained(model_id_lilt) |
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model_lilt = AutoModelForTokenClassification.from_pretrained(model_id_lilt); |
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model_lilt.to(device); |
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from transformers import LayoutLMv2ForTokenClassification |
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model_layoutxlm = LayoutLMv2ForTokenClassification.from_pretrained(model_id_layoutxlm); |
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model_layoutxlm.to(device); |
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from transformers import LayoutLMv2FeatureExtractor |
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feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False) |
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from transformers import AutoTokenizer |
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tokenizer_layoutxlm = AutoTokenizer.from_pretrained(tokenizer_id_layoutxlm) |
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id2label_lilt = model_lilt.config.id2label |
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label2id_lilt = model_lilt.config.label2id |
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num_labels_lilt = len(id2label_lilt) |
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id2label_layoutxlm = model_layoutxlm.config.id2label |
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label2id_layoutxlm = model_layoutxlm.config.label2id |
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num_labels_layoutxlm = len(id2label_layoutxlm) |
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def app_outputs_by_model(uploaded_pdf, model_id, model, tokenizer, max_length, id2label, cls_box, sep_box): |
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filename, msg, images = pdf_to_images(uploaded_pdf) |
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num_images = len(images) |
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if not msg.startswith("Error with the PDF"): |
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dataset, lines, row_indexes, par_boxes, line_boxes = extraction_data_from_image(images) |
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prepare_inference_features_partial = partial(prepare_inference_features, tokenizer=tokenizer, max_length=max_length, cls_box=cls_box, sep_box=sep_box) |
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encoded_dataset = dataset.map(prepare_inference_features_partial, batched=True, batch_size=64, remove_columns=dataset.column_names) |
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custom_encoded_dataset = CustomDataset(encoded_dataset, tokenizer) |
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outputs, images_ids_list, chunk_ids, input_ids, bboxes = predictions_token_level(images, custom_encoded_dataset, model_id, model) |
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probs_bbox, bboxes_list_dict, input_ids_dict_dict, probs_dict_dict, df = predictions_line_level(max_length, tokenizer, id2label, dataset, outputs, images_ids_list, chunk_ids, input_ids, bboxes, cls_box, sep_box) |
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images = get_labeled_images(id2label, dataset, images_ids_list, bboxes_list_dict, probs_dict_dict) |
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img_files = list() |
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for i in range(num_images): |
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if filename != "files/blank.png": img_file = f"img_{i}_" + filename.replace(".pdf", ".png") |
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else: img_file = filename.replace(".pdf", ".png") |
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img_file = img_file.replace("/", "_") |
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images[i].save(img_file) |
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img_files.append(img_file) |
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if num_images < max_imgboxes: |
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img_files += [image_blank]*(max_imgboxes - num_images) |
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images += [Image.open(image_blank)]*(max_imgboxes - num_images) |
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for count in range(max_imgboxes - num_images): |
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df[num_images + count] = pd.DataFrame() |
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else: |
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img_files = img_files[:max_imgboxes] |
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images = images[:max_imgboxes] |
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df = dict(itertools.islice(df.items(), max_imgboxes)) |
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csv_files = list() |
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for i in range(max_imgboxes): |
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csv_file = f"csv_{i}_" + filename.replace(".pdf", ".csv") |
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csv_file = csv_file.replace("/", "_") |
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csv_files.append(gr.File.update(value=csv_file, visible=True)) |
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df[i].to_csv(csv_file, encoding="utf-8", index=False) |
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else: |
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img_files, images, csv_files = [""]*max_imgboxes, [""]*max_imgboxes, [""]*max_imgboxes |
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img_files[0], img_files[1] = image_blank, image_blank |
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images[0], images[1] = Image.open(image_blank), Image.open(image_blank) |
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csv_file = "csv_wo_content.csv" |
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csv_files[0], csv_files[1] = gr.File.update(value=csv_file, visible=True), gr.File.update(value=csv_file, visible=True) |
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df, df_empty = dict(), pd.DataFrame() |
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df[0], df[1] = df_empty.to_csv(csv_file, encoding="utf-8", index=False), df_empty.to_csv(csv_file, encoding="utf-8", index=False) |
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return msg, img_files[0], images[0], csv_files[0], df[0] |
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def app_outputs(uploaded_pdf): |
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msg_lilt, img_files_lilt, images_lilt, csv_files_lilt, df_lilt = app_outputs_by_model(uploaded_pdf, |
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model_id=model_id_lilt, model=model_lilt, tokenizer=tokenizer_lilt, |
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max_length=max_length_lilt, id2label=id2label_lilt, cls_box=cls_box, sep_box=sep_box_lilt) |
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msg_layoutxlm, img_files_layoutxlm, images_layoutxlm, csv_files_layoutxlm, df_layoutxlm = app_outputs_by_model(uploaded_pdf, |
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model_id=model_id_layoutxlm, model=model_layoutxlm, tokenizer=tokenizer_layoutxlm, |
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max_length=max_length_layoutxlm, id2label=id2label_layoutxlm, cls_box=cls_box, sep_box=sep_box_layoutxlm) |
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return msg_lilt, msg_layoutxlm, img_files_lilt, img_files_layoutxlm, images_lilt, images_layoutxlm, csv_files_lilt, csv_files_layoutxlm, df_lilt, df_layoutxlm |
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with gr.Blocks(title="Inference APP for Document Understanding at line level (v1 - LiLT base vs LayoutXLM base)", css=".gradio-container") as demo: |
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gr.HTML(""" |
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<div style="font-family:'Times New Roman', 'Serif'; font-size:26pt; font-weight:bold; text-align:center;"><h1>Inference APP for Document Understanding at line level (v1 - LiLT base vs LayoutXLM base)</h1></div> |
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<div style="margin-top: 40px"><p>(03/08/2023) This Inference APP compares - only on the first PDF page - 2 Document Understanding models finetuned on the dataset <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/datasets/pierreguillou/DocLayNet-base" target="_blank">DocLayNet base</a> at line level (chunk size of 384 tokens): <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384" target="_blank">LiLT base combined with XLM-RoBERTa base</a> and <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384" target="_blank">LayoutXLM base combined with XLM-RoBERTa base</a>.</p></div> |
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<div><p>To test these 2 models separately, use their corresponding APP on Hugging Face Spaces: <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v1" target="_blank">LiLT base APP (v1 - line level)</a> and <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v2" target="_blank">LayoutXLM base APP (v2 - line level)</a>.</p></div><div style="margin-top: 20px"><p>Links to Document Understanding APPs:</p><ul><li>Line level: <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v1" target="_blank">v1 (LiLT base)</a> | <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v2" target="_blank">v2 (LayoutXLM base)</a></li><li>Paragraph level: <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-paragraphlevel-v1" target="_blank">v1 (LiLT base)</a> | <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-paragraphlevel-v2" target="_blank">v2 (LayoutXLM base)</a></li></ul></div><div style="margin-top: 20px"><p>More information about the DocLayNet datasets, the finetuning of the model and this APP in the following blog posts:</p><ul><li>(03/05/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="" target="_blank">Document AI | Inference APP and fine-tuning notebook for Document Understanding at line level with LayoutXLM base</a></li><li>(02/14/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-inference-app-for-document-understanding-at-line-level-a35bbfa98893" target="_blank">Document AI | Inference APP for Document Understanding at line level</a></li><li>(02/10/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-document-understanding-model-at-line-level-with-lilt-tesseract-and-doclaynet-dataset-347107a643b8" target="_blank">Document AI | Document Understanding model at line level with LiLT, Tesseract and DocLayNet dataset</a></li><li>(01/31/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-doclaynet-image-viewer-app-3ac54c19956" target="_blank">Document AI | DocLayNet image viewer APP</a></li><li>(01/27/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-processing-of-doclaynet-dataset-to-be-used-by-layout-models-of-the-hugging-face-hub-308d8bd81cdb" target="_blank">Document AI | Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)</a></li></ul></div> |
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""") |
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with gr.Row(): |
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pdf_file = gr.File(label="PDF") |
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with gr.Row(): |
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submit_btn = gr.Button(f"Get layout detection by LiLT and LayoutXLM on the first PDF page") |
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reset_btn = gr.Button(value="Clear") |
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with gr.Row(): |
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output_messages = [] |
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with gr.Column(): |
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output_msg = gr.Textbox(label="LiLT output message") |
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output_messages.append(output_msg) |
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with gr.Column(): |
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output_msg = gr.Textbox(label="LayoutXLM output message") |
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output_messages.append(output_msg) |
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with gr.Row(): |
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fileboxes = [] |
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with gr.Column(): |
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file_path = gr.File(visible=True, label=f"LiLT image file") |
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fileboxes.append(file_path) |
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with gr.Column(): |
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file_path = gr.File(visible=True, label=f"LayoutXLM image file") |
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fileboxes.append(file_path) |
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with gr.Row(): |
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imgboxes = [] |
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with gr.Column(): |
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img = gr.Image(type="pil", label=f"Lilt Image") |
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imgboxes.append(img) |
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with gr.Column(): |
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img = gr.Image(type="pil", label=f"LayoutXLM Image") |
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imgboxes.append(img) |
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with gr.Row(): |
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csvboxes = [] |
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with gr.Column(): |
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csv = gr.File(visible=True, label=f"LiLT csv file at line level") |
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csvboxes.append(csv) |
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with gr.Column(): |
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csv = gr.File(visible=True, label=f"LayoutXLM csv file at line level") |
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csvboxes.append(csv) |
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with gr.Row(): |
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dfboxes = [] |
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with gr.Column(): |
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df = gr.Dataframe( |
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headers=["bounding boxes", "texts", "labels"], |
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datatype=["str", "str", "str"], |
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col_count=(3, "fixed"), |
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visible=True, |
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label=f"LiLT data", |
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type="pandas", |
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wrap=True |
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) |
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dfboxes.append(df) |
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with gr.Column(): |
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df = gr.Dataframe( |
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headers=["bounding boxes", "texts", "labels"], |
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datatype=["str", "str", "str"], |
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col_count=(3, "fixed"), |
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visible=True, |
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label=f"LayoutXLM data", |
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type="pandas", |
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wrap=True |
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) |
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dfboxes.append(df) |
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outputboxes = output_messages + fileboxes + imgboxes + csvboxes + dfboxes |
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submit_btn.click(app_outputs, inputs=[pdf_file], outputs=outputboxes) |
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reset_btn.click( |
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lambda: [pdf_file.update(value=None)] + [output_msg.update(value=None) for output_msg in output_messages] + [filebox.update(value=None) for filebox in fileboxes] + [imgbox.update(value=None) for imgbox in imgboxes] + [csvbox.update(value=None) for csvbox in csvboxes] + [dfbox.update(value=None) for dfbox in dfboxes], |
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inputs=[], |
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outputs=[pdf_file] + output_messages + fileboxes + imgboxes + csvboxes + dfboxes |
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) |
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gr.Examples( |
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[["files/example.pdf"]], |
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[pdf_file], |
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outputboxes, |
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fn=app_outputs, |
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cache_examples=True, |
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) |
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demo.launch() |
