import os
import gradio as gr
import re
import string
from operator import itemgetter
import collections
import pypdf
from pypdf import PdfReader
from pypdf.errors import PdfReadError
import pdf2image
from pdf2image import convert_from_path
import langdetect
from langdetect import detect_langs
import pandas as pd
import numpy as np
import random
import tempfile
import itertools
from matplotlib import font_manager
from PIL import Image, ImageDraw, ImageFont
import cv2
## files
import sys
sys.path.insert(0, 'files/')
import functions
from functions import *
# update pip
os.system('python -m pip install --upgrade pip')
# APP outputs
def app_outputs(uploaded_pdf):
filename, msg, images = pdf_to_images(uploaded_pdf)
num_images = len(images)
if not msg.startswith("Error with the PDF"):
# Extraction of image data (text and bounding boxes)
dataset, lines, row_indexes, par_boxes, line_boxes = extraction_data_from_image(images)
# prepare our data in the format of the model
encoded_dataset = dataset.map(prepare_inference_features, batched=True, batch_size=64, remove_columns=dataset.column_names)
custom_encoded_dataset = CustomDataset(encoded_dataset, tokenizer)
# Get predictions (token level)
outputs, images_ids_list, chunk_ids, input_ids, bboxes = predictions_token_level(images, custom_encoded_dataset)
# Get predictions (line level)
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)
# Get labeled images with lines bounding boxes
images = get_labeled_images(dataset, images_ids_list, bboxes_list_dict, probs_dict_dict)
img_files = list()
# get image of PDF without bounding boxes
for i in range(num_images):
if filename != "files/blank.png": img_file = f"img_{i}_" + filename.replace(".pdf", ".png")
else: img_file = filename.replace(".pdf", ".png")
images[i].save(img_file)
img_files.append(img_file)
if num_images < max_imgboxes:
img_files += [image_blank]*(max_imgboxes - num_images)
images += [Image.open(image_blank)]*(max_imgboxes - num_images)
for count in range(max_imgboxes - num_images):
df[num_images + count] = pd.DataFrame()
else:
img_files = img_files[:max_imgboxes]
images = images[:max_imgboxes]
df = dict(itertools.islice(df.items(), max_imgboxes))
# save
csv_files = list()
for i in range(max_imgboxes):
csv_file = f"csv_{i}_" + filename.replace(".pdf", ".csv")
csv_files.append(gr.File.update(value=csv_file, visible=True))
df[i].to_csv(csv_file, encoding="utf-8", index=False)
else:
img_files, images, csv_files = [""]*3,[""]*3,[""]*3
img_files[0], img_files[1], img_files[2] = image_blank, image_blank, image_blank
images[0], images[1], images[2] = Image.open(image_blank), Image.open(image_blank), Image.open(image_blank)
csv_file = "csv_wo_content.csv"
csv_files[0], csv_files[1], csv_files[2] = gr.File.update(value=csv_file, visible=True), gr.File.update(value=csv_file, visible=True), gr.File.update(value=csv_file, visible=True)
df, df_empty = dict(), pd.DataFrame()
df[0], df[1], df[2] = df_empty.to_csv(csv_file, encoding="utf-8", index=False), df_empty.to_csv(csv_file, encoding="utf-8", index=False), df_empty.to_csv(csv_file, encoding="utf-8", index=False)
return msg, img_files[0], img_files[1], img_files[2], images[0], images[1], images[2], csv_files[0], csv_files[1], csv_files[2], df[0], df[1], df[2]
# gradio APP
with gr.Blocks(title="Inference APP for Document Understanding at line level (v1)", css=".gradio-container") as demo:
gr.HTML("""
Inference APP for Document Understanding at line level (v1)
LiLT (Language-Independent Layout Transformer) is a Document Understanding model that uses both layout and text in order to detect labels of bounding boxes. Combined with the model XML-RoBERTa base, this finetuned model has the capacity to understand any language. Finetuned on the dataset DocLayNet base, 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).
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 (PyTesseract) 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!
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).
More information about the DocLayNet datasets, the finetuning of the model and this APP in the following blog posts: