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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 = "NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1" |
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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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path = os.path.dirname(__file__) |
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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 != path+"/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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if not os.path.exists(img_file): |
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os.makedirs(os.path.dirname(img_file)) |
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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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if not os.path.exists(csv_file): |
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os.makedirs(os.path.dirname(csv_file)) |
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csv_files.append(gr.File(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="Application for document layout analysis at line level (v1 - LiLT 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 application for Documents layout analysis at line level (v1 - LiLT base)</h1></div> |
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<div style="margin-top: 40px"><p>(02/12/2023) This application uses the <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://huggingface.co/NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1" target="_blank">model LiLT base combined with XLM-RoBERTa base and finetuned on the dataset DocLayNet base at line level</a> (chunk size of 256 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 <b>understand any language</b>. 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 <b>classifly any bounding box (and its OCR text) to 11 labels</b> (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 (<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>) 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><b>It allows to get all pages of any PDF (of any language) with bounding boxes labeled at line level and the associated dataframes with labeled data (bounding boxes, texts, labels) :-)</b></p></div> |
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<div><p>However, the inference time per page can be high when running the model on CPU due to the number of line predictions to be made. Therefore, to avoid running this APP for too long, <b>only the first 2 pages are processed by this APP</b> |
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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() |
