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import os
# workaround: install old version of pytorch since detectron2 hasn't released packages for pytorch 1.9 (issue: https://github.com/facebookresearch/detectron2/issues/3158)
# os.system('pip install torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html')
os.system('pip install -q torch==1.10.0+cu111 torchvision==0.11+cu111 -f https://download.pytorch.org/whl/torch_stable.html')
# install detectron2 that matches pytorch 1.8
# See https://detectron2.readthedocs.io/tutorials/install.html for instructions
#os.system('pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html')
os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
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')
## model / feature extractor / tokenizer
# models
model_id_lilt = "pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384"
model_id_layoutxlm = "pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384"
# get device
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
## model LiLT
import transformers
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer_lilt = AutoTokenizer.from_pretrained(model_id_lilt)
model_lilt = AutoModelForTokenClassification.from_pretrained(model_id_lilt);
model_lilt.to(device);
## model LayoutXLM
from transformers import LayoutLMv2ForTokenClassification # LayoutXLMTokenizerFast,
model_layoutxlm = LayoutLMv2ForTokenClassification.from_pretrained(model_id_layoutxlm);
model_layoutxlm.to(device);
# feature extractor
from transformers import LayoutLMv2FeatureExtractor
feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False)
# tokenizer
from transformers import AutoTokenizer
tokenizer_layoutxlm = AutoTokenizer.from_pretrained(tokenizer_id_layoutxlm)
# get labels
id2label_lilt = model_lilt.config.id2label
label2id_lilt = model_lilt.config.label2id
num_labels_lilt = len(id2label_lilt)
id2label_layoutxlm = model_layoutxlm.config.id2label
label2id_layoutxlm = model_layoutxlm.config.label2id
num_labels_layoutxlm = len(id2label_layoutxlm)
# APP outputs by model
def app_outputs_by_model(uploaded_pdf, model_id, model, tokenizer, max_length, id2label, cls_box, sep_box):
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
prepare_inference_features_partial = partial(prepare_inference_features, tokenizer=tokenizer, max_length=max_length, cls_box=cls_box, sep_box=sep_box)
encoded_dataset = dataset.map(prepare_inference_features_partial, 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, model_id, model)
# Get predictions (line level)
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)
# Get labeled images with lines bounding boxes
images = get_labeled_images(id2label, 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 = [""]*max_imgboxes, [""]*max_imgboxes, [""]*max_imgboxes
img_files[0], img_files[1] = image_blank, image_blank
images[0], images[1] = Image.open(image_blank), Image.open(image_blank)
csv_file = "csv_wo_content.csv"
csv_files[0], csv_files[1] = 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_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], images[0], csv_files[0], df[0]
def app_outputs(uploaded_pdf):
msg_lilt, img_files_lilt, images_lilt, csv_files_lilt, df_lilt = app_outputs_by_model(uploaded_pdf,
model_id=model_id_lilt, model=model_lilt, tokenizer=tokenizer_lilt,
max_length=max_length_lilt, id2label=id2label_lilt, cls_box=cls_box, sep_box=sep_box_lilt)
msg_layoutxlm, img_files_layoutxlm, images_layoutxlm, csv_files_layoutxlm, df_layoutxlm = app_outputs_by_model(uploaded_pdf,
model_id=model_id_layoutxlm, model=model_layoutxlm, tokenizer=tokenizer_layoutxlm,
max_length=max_length_layoutxlm, id2label=id2label_layoutxlm, cls_box=cls_box, sep_box=sep_box_layoutxlm)
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
# gradio APP
with gr.Blocks(title="Inference APP for Document Understanding at line level (v1 - LiLT base vs LayoutXLM base)", css=".gradio-container") as demo:
gr.HTML("""
<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>
<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>
<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> | <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-LiLT-base-LayoutXLM-base-v1" target="_blank">v1 (LilT base vs 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></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>
""")
with gr.Row():
pdf_file = gr.File(label="PDF")
with gr.Row():
submit_btn = gr.Button(f"Get layout detection by LiLT and LayoutXLM on the first PDF page")
reset_btn = gr.Button(value="Clear")
with gr.Row():
output_messages = []
with gr.Column():
output_msg = gr.Textbox(label="LiLT output message")
output_messages.append(output_msg)
with gr.Column():
output_msg = gr.Textbox(label="LayoutXLM output message")
output_messages.append(output_msg)
with gr.Row():
fileboxes = []
with gr.Column():
file_path = gr.File(visible=True, label=f"LiLT image file")
fileboxes.append(file_path)
with gr.Column():
file_path = gr.File(visible=True, label=f"LayoutXLM image file")
fileboxes.append(file_path)
with gr.Row():
imgboxes = []
with gr.Column():
img = gr.Image(type="pil", label=f"Lilt Image")
imgboxes.append(img)
with gr.Column():
img = gr.Image(type="pil", label=f"LayoutXLM Image")
imgboxes.append(img)
with gr.Row():
csvboxes = []
with gr.Column():
csv = gr.File(visible=True, label=f"LiLT csv file at line level")
csvboxes.append(csv)
with gr.Column():
csv = gr.File(visible=True, label=f"LayoutXLM csv file at line level")
csvboxes.append(csv)
with gr.Row():
dfboxes = []
with gr.Column():
df = gr.Dataframe(
headers=["bounding boxes", "texts", "labels"],
datatype=["str", "str", "str"],
col_count=(3, "fixed"),
visible=True,
label=f"LiLT data",
type="pandas",
wrap=True
)
dfboxes.append(df)
with gr.Column():
df = gr.Dataframe(
headers=["bounding boxes", "texts", "labels"],
datatype=["str", "str", "str"],
col_count=(3, "fixed"),
visible=True,
label=f"LayoutXLM data",
type="pandas",
wrap=True
)
dfboxes.append(df)
outputboxes = output_messages + fileboxes + imgboxes + csvboxes + dfboxes
submit_btn.click(app_outputs, inputs=[pdf_file], outputs=outputboxes)
# https://github.com/gradio-app/gradio/pull/2044/files#diff-a91dd2749f68bb7d0099a0f4079a4fd2d10281e299e7b451cb1bb876a7c21975R91
reset_btn.click(
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],
inputs=[],
outputs=[pdf_file] + output_messages + fileboxes + imgboxes + csvboxes + dfboxes
)
gr.Examples(
[["files/example.pdf"]],
[pdf_file],
outputboxes,
fn=app_outputs,
cache_examples=True,
)
demo.launch()