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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 pypdfium2 as pdfium
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-paragraphlevel-ml512"
model_id_layoutxlm = "pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512"
# tokenizer for LayoutXLM
tokenizer_id_layoutxlm = "xlm-roberta-base"
# 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
# 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, texts_lines, texts_pars, texts_lines_par, row_indexes, par_boxes, line_boxes, lines_par_boxes = extraction_data_from_image(images)
# prepare our data in the format of the model
prepare_inference_features_partial = partial(prepare_inference_features_paragraph, 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_paragraph_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")
img_file = img_file.replace("/", "_")
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_file = csv_file.replace("/", "_")
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
if max_imgboxes >= 2:
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], img_files[1], images[0], images[1], csv_files[0], csv_files[1], df[0], df[1]
else:
img_files[0] = image_blank
images[0] = Image.open(image_blank)
csv_file = "csv_wo_content.csv"
csv_files[0] = gr.File.update(value=csv_file, visible=True)
df, df_empty = dict(), pd.DataFrame()
df[0] = 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 paragraph 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 paragraph level (v1 - LiLT base vs LayoutXLM base)</h1></div>
<div style="margin-top: 40px"><p>(04/01/2023) 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 paragraph level (chunk size of 512 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-paragraphlevel-ml512" 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-paragraphlevel-ml512" 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-paragraphlevel-v1" target="_blank">LiLT base APP (v1 - paragraph 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-paragraphlevel-v2" target="_blank">LayoutXLM base APP (v2 - paragraph 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-comparison-APP-Document-Understanding-at-paragraphlevel-v1" target="_blank">v2 vs v1 (LayoutXLM vs LiLT)</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-v3" target="_blank">Ensemble v1 (LiLT & LayoutXLM)</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>(03/31/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-inference-app-and-fine-tuning-notebook-for-document-understanding-at-paragraph-level-3507af80573d" target="_blank">Document AI | Inference APP and fine-tuning notebook for Document Understanding at paragraph level with LayoutXLM base</a></li><li>(03/25/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-app-to-compare-the-document-understanding-lilt-and-layoutxlm-base-models-at-line-1c53eb481a15" target="_blank">Document AI | APP to compare the Document Understanding LiLT and LayoutXLM (base) models at line level</a></li><li>(03/05/2023) <a style="text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6" href="https://medium.com/@pierre_guillou/document-ai-inference-app-and-fine-tuning-notebook-for-document-understanding-at-line-level-with-b08fdca5f4dc" 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 paragraph level")
csvboxes.append(csv)
with gr.Column():
csv = gr.File(visible=True, label=f"LayoutXLM csv file at paragraph 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() |