app.py

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")
            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
        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></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>(03/05/2023) <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()