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import os |
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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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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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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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model_id = "pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForTokenClassification.from_pretrained(model_id); |
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model.to(device); |
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def app_outputs(uploaded_pdf): |
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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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encoded_dataset = dataset.map(prepare_inference_features, 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) |
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probs_bbox, bboxes_list_dict, input_ids_dict_dict, probs_dict_dict, df = predictions_line_level(dataset, outputs, images_ids_list, chunk_ids, input_ids, bboxes) |
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images = get_labeled_images(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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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_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], img_files[1], images[0], images[1], csv_files[0], csv_files[1], df[0], df[1] |
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with gr.Blocks(title="Inference APP for Document Understanding at line level (v1)", 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)</h1></div> |
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<div style="margin-top: 40px"><p>(02/12/2023) This Inference APP uses the <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">model LiLT base combined with XLM-RoBERTa base and finetuned on the dataset DocLayNet base</a> at line level (chunk size of 384 tokens).</p></div> |
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<div><p><a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://arxiv.org/abs/2202.13669" target="_blank">LiLT (Language-Independent Layout Transformer)</a> is a Document Understanding model that uses both layout and text in order to detect labels of bounding boxes. Combined with the model <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/xlm-roberta-base" target="_blank">XML-RoBERTa base</a>, this finetuned model has the capacity to understand any language. 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>, it can classifly any bounding box (and its OCR text) to 11 labels (Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title).</p></div> |
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<div><p>It relies on an external OCR engine to get words and bounding boxes from the document image. Thus, let's run in this APP an OCR engine ourselves (<a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://github.com/madmaze/pytesseract#python-tesseract" target="_blank">PyTesseract</a>) as we'll need to do it in real life to get the bounding boxes, then run LiLT (already fine-tuned on the dataset DocLayNet base at line level) on the individual tokens and then, visualize the result at line level!</p></div> |
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<div><p>From any PDF (of any language), it allows to get all pages with bounding boxes labeled at line level and the associated dataframes with labeled data (bounding boxes, texts, labels).</p></div> |
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<div><p>To avoid running this APP for too long, <b>only the first 2 pages are processed by this APP</b>. If you want to update this limit, you can either clone this APP and change the value of the parameter <code>max_imgboxes</code>, or run the corresponding notebook "<a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb" target="_blank">Document AI | Inference at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)</a>" which does not have this limit.</p></div> |
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<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> |
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<ul><li><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">(02/10/2023) Document AI | Document Understanding model at line level with LiLT, Tesseract and DocLayNet dataset</a></li><li><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"> (01/31/2023) Document AI | DocLayNet image viewer APP</a></li><li><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">(01/27/2023) 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"Display first {max_imgboxes} labeled PDF pages") |
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reset_btn = gr.Button(value="Clear") |
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with gr.Row(): |
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output_msg = gr.Textbox(label="Output message") |
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with gr.Row(): |
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fileboxes = [] |
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for num_page in range(max_imgboxes): |
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file_path = gr.File(visible=True, label=f"Image file of the PDF page n°{num_page}") |
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fileboxes.append(file_path) |
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with gr.Row(): |
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imgboxes = [] |
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for num_page in range(max_imgboxes): |
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img = gr.Image(type="pil", label=f"Image of the PDF page n°{num_page}") |
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imgboxes.append(img) |
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with gr.Row(): |
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csvboxes = [] |
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for num_page in range(max_imgboxes): |
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csv = gr.File(visible=True, label=f"CSV file at line level (page {num_page})") |
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csvboxes.append(csv) |
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with gr.Row(): |
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dfboxes = [] |
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for num_page in range(max_imgboxes): |
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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"Data of page {num_page}", |
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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_msg] + 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)] + [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_msg] + 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() |
