Delete files/functions.py

files/functions.py

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-import os
-import gradio as gr
-import re
-import string
-import torch
-
-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
-
-
-
-# Tesseract
-print(os.popen(f'cat /etc/debian_version').read())
-print(os.popen(f'cat /etc/issue').read())
-print(os.popen(f'apt search tesseract').read())
-import pytesseract
-
-## Key parameters
-
-# categories colors
-label2color = {
-    'Caption': 'brown',
-    'Footnote': 'orange',
-    'Formula': 'gray',
-    'List-item': 'yellow',
-    'Page-footer': 'red',
-    'Page-header': 'red',
-    'Picture': 'violet',
-    'Section-header': 'orange',
-    'Table': 'green',
-    'Text': 'blue',
-    'Title': 'pink'
-    }
-
-# bounding boxes start and end of a sequence
-cls_box = [0, 0, 0, 0]
-sep_box = cls_box
-
-# model
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-model_id = "NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1"
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForTokenClassification.from_pretrained(model_id);
-model.to(device);
-
-# get labels
-id2label = model.config.id2label
-label2id = model.config.label2id
-num_labels = len(id2label)
-
-# (tokenization) The maximum length of a feature (sequence)
-if str(256) in model_id:
-  max_length = 256 
-elif str(512) in model_id:
-  max_length = 512 
-else:
-  print("Error with max_length of chunks!")
-
-# (tokenization) overlap
-doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
-
-# max PDF page images that will be displayed
-max_imgboxes = 2
-examples_dir = 'files/'
-image_wo_content = examples_dir + "wo_content.png" # image without content
-pdf_blank = examples_dir + "blank.pdf" # blank PDF
-image_blank = examples_dir + "blank.png" # blank image
-
-## get langdetect2Tesseract dictionary
-t = "files/languages_tesseract.csv"
-l = "files/languages_iso.csv"
-
-df_t = pd.read_csv(t)
-df_l = pd.read_csv(l)
-
-langs_t = df_t["Language"].to_list()
-langs_t = [lang_t.lower().strip().translate(str.maketrans('', '', string.punctuation)) for lang_t in langs_t]
-langs_l = df_l["Language"].to_list()
-langs_l = [lang_l.lower().strip().translate(str.maketrans('', '', string.punctuation)) for lang_l in langs_l]
-langscode_t = df_t["LangCode"].to_list()
-langscode_l = df_l["LangCode"].to_list()
-
-Tesseract2langdetect, langdetect2Tesseract = dict(), dict()
-for lang_t, langcode_t in zip(langs_t,langscode_t):
-  try:
-    if lang_t == "Chinese - Simplified".lower().strip().translate(str.maketrans('', '', string.punctuation)): lang_t = "chinese"
-    index = langs_l.index(lang_t)
-    langcode_l = langscode_l[index]
-    Tesseract2langdetect[langcode_t] = langcode_l
-  except: 
-    continue
-
-langdetect2Tesseract = {v:k for k,v in Tesseract2langdetect.items()}
-
-## General
-
-# get text and bounding boxes from an image
-# https://stackoverflow.com/questions/61347755/how-can-i-get-line-coordinates-that-readed-by-tesseract
-# https://medium.com/geekculture/tesseract-ocr-understanding-the-contents-of-documents-beyond-their-text-a98704b7c655
-def get_data(results, factor, conf_min=0):
-
-    data = {}
-    for i in range(len(results['line_num'])):
-        level = results['level'][i]
-        block_num = results['block_num'][i]
-        par_num = results['par_num'][i]
-        line_num = results['line_num'][i]
-        top, left = results['top'][i], results['left'][i]
-        width, height = results['width'][i], results['height'][i]
-        conf = results['conf'][i]
-        text = results['text'][i]
-        
-        if not (text == '' or text.isspace()):
-            if conf >= conf_min:
-                tup = (text, left, top, width, height)
-                data.setdefault(block_num, {}).setdefault(par_num, {}).setdefault(line_num, []).append(tup)
-
-    # get paragraphs dictionary with a list of lines
-    par_data = {}
-    par_idx = 1
-    for _, b in data.items():
-        for _, p in b.items():
-            line_data = {}
-            line_idx = 1
-            for _, l in p.items():
-                line_data[line_idx] = l
-                line_idx += 1
-            par_data[par_idx] = line_data
-            par_idx += 1
-
-    # get lines of texts, grouped by paragraph
-    lines = list()
-    row_indexes = list()
-    row_index = 0
-    for _, par in par_data.items():
-        count_lines = 0
-        for _, line in par.items():
-            if count_lines == 0:
-                row_indexes.append(row_index)
-            line_text = ' '.join([item[0] for item in line])
-            lines.append(line_text)
-            count_lines += 1
-            row_index += 1
-        row_index += 1
-
-    # get paragraphs boxes (par_boxes)
-    # get lines boxes (line_boxes)
-    par_boxes = list()
-    par_idx = 1
-    line_boxes = list()
-    line_idx = 1
-    for _, par in par_data.items():
-        xmins, ymins, xmaxs, ymaxs = [], [], [], []
-        for _, line in par.items():
-            xmin, ymin = line[0][1], line[0][2]
-            xmax, ymax = (line[-1][1] + line[-1][3]), (line[-1][2] + line[-1][4])
-            line_boxes.append([int(xmin/factor), int(ymin/factor), int(xmax/factor), int(ymax/factor)])
-            xmins.append(xmin)
-            ymins.append(ymin)
-            xmaxs.append(xmax)
-            ymaxs.append(ymax)
-            line_idx += 1
-        xmin, ymin, xmax, ymax = min(xmins), min(ymins), max(xmaxs), max(ymaxs)
-        par_boxes.append([int(xmin/factor), int(ymin/factor), int(xmax/factor), int(ymax/factor)])
-        par_idx += 1
-
-    return lines, row_indexes, par_boxes, line_boxes
-
-
-
-# rescale image to get 300dpi
-def set_image_dpi_resize(image):
-    length_x, width_y = image.shape[1], image.shape[0]
-    factor = min(1, float(1024.0 / length_x))
-    size = (int(factor * length_x), int(factor * width_y))
-    image_resize = cv2.resize(image, size, interpolation=cv2.INTER_LANCZOS4)
-    _, temp_filename = tempfile.mkstemp(suffix='.png')
-    cv2.imwrite(temp_filename, image_resize, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
-    return factor, temp_filename
-
-
-# it is important that each bounding box should be in (upper left, lower right) format.
-# source: https://github.com/NielsRogge/Transformers-Tutorials/issues/129
-def upperleft_to_lowerright(bbox):
-  x0, y0, x1, y1 = tuple(bbox)
-  if bbox[2] < bbox[0]:
-    x0 = bbox[2]
-    x1 = bbox[0] 
-  if bbox[3] < bbox[1]:
-    y0 = bbox[3]
-    y1 = bbox[1] 
-  return [x0, y0, x1, y1]
-
-# convert boundings boxes (left, top, width, height) format to (left, top, left+widght, top+height) format. 
-def convert_box(bbox):
-    x, y, w, h = tuple(bbox) # the row comes in (left, top, width, height) format
-    return [x, y, x+w, y+h] # we turn it into (left, top, left+widght, top+height) to get the actual box 
-
-# LiLT model gets 1000x10000 pixels images
-def normalize_box(bbox, width, height):
-    return [
-        int(1000 * (bbox[0] / width)),
-        int(1000 * (bbox[1] / height)),
-        int(1000 * (bbox[2] / width)),
-        int(1000 * (bbox[3] / height)),
-    ]
-
-# LiLT model gets 1000x10000 pixels images
-def denormalize_box(bbox, width, height):
-    return [
-        int(width * (bbox[0] / 1000)),
-        int(height * (bbox[1] / 1000)),
-        int(width* (bbox[2] / 1000)),
-        int(height * (bbox[3] / 1000)),
-    ]
-
-# get back original size
-def original_box(box, original_width, original_height, coco_width, coco_height):
-    return [
-        int(original_width * (box[0] / coco_width)),
-        int(original_height * (box[1] / coco_height)),
-        int(original_width * (box[2] / coco_width)),
-        int(original_height* (box[3] / coco_height)),
-    ]
-
-def get_blocks(bboxes_block, categories, texts):
-
- # get list of unique block boxes
-    bbox_block_dict, bboxes_block_list, bbox_block_prec = dict(), list(), list()
-    for count_block, bbox_block in enumerate(bboxes_block):
-      if bbox_block != bbox_block_prec:
-        bbox_block_indexes = [i for i, bbox in enumerate(bboxes_block) if bbox == bbox_block]
-        bbox_block_dict[count_block] = bbox_block_indexes
-        bboxes_block_list.append(bbox_block)
-      bbox_block_prec = bbox_block
-
-    # get list of categories and texts by unique block boxes
-    category_block_list, text_block_list = list(), list()
-    for bbox_block in bboxes_block_list:
-      count_block = bboxes_block.index(bbox_block)
-      bbox_block_indexes = bbox_block_dict[count_block]
-      category_block = np.array(categories, dtype=object)[bbox_block_indexes].tolist()[0]
-      category_block_list.append(category_block)
-      text_block = np.array(texts, dtype=object)[bbox_block_indexes].tolist()
-      text_block = [text.replace("\n","").strip() for text in text_block]
-      if id2label[category_block] == "Text" or id2label[category_block] == "Caption" or id2label[category_block] == "Footnote":
-        text_block = ' '.join(text_block)
-      else:
-        text_block = '\n'.join(text_block)
-      text_block_list.append(text_block)
-
-    return bboxes_block_list, category_block_list, text_block_list
-
-# function to sort bounding boxes
-def get_sorted_boxes(bboxes):
-
-  # sort by y from page top to bottom 
-  sorted_bboxes = sorted(bboxes, key=itemgetter(1), reverse=False)
-  y_list = [bbox[1] for bbox in sorted_bboxes]
-
-  # sort by x from page left to right when boxes with same y
-  if len(list(set(y_list))) != len(y_list):
-    y_list_duplicates_indexes = dict()
-    y_list_duplicates = [item for item, count in collections.Counter(y_list).items() if count > 1]
-    for item in y_list_duplicates:
-      y_list_duplicates_indexes[item] = [i for i, e in enumerate(y_list) if e == item]
-      bbox_list_y_duplicates = sorted(np.array(sorted_bboxes, dtype=object)[y_list_duplicates_indexes[item]].tolist(), key=itemgetter(0), reverse=False)
-      np_array_bboxes = np.array(sorted_bboxes)
-      np_array_bboxes[y_list_duplicates_indexes[item]] = np.array(bbox_list_y_duplicates)
-      sorted_bboxes = np_array_bboxes.tolist()
-
-  return sorted_bboxes
-
-# sort data from y = 0 to end of page (and after, x=0 to end of page when necessary)
-def sort_data(bboxes, categories, texts):
-
-    sorted_bboxes = get_sorted_boxes(bboxes)
-    sorted_bboxes_indexes = [bboxes.index(bbox) for bbox in sorted_bboxes]
-    sorted_categories = np.array(categories, dtype=object)[sorted_bboxes_indexes].tolist()
-    sorted_texts = np.array(texts, dtype=object)[sorted_bboxes_indexes].tolist()
-
-    return sorted_bboxes, sorted_categories, sorted_texts
-
-# sort data from y = 0 to end of page (and after, x=0 to end of page when necessary)
-def sort_data_wo_labels(bboxes, texts):
-
-    sorted_bboxes = get_sorted_boxes(bboxes)
-    sorted_bboxes_indexes = [bboxes.index(bbox) for bbox in sorted_bboxes]
-    sorted_texts = np.array(texts, dtype=object)[sorted_bboxes_indexes].tolist()
-
-    return sorted_bboxes, sorted_texts
-
-## PDF processing
-
-import os
-from PyPDF2 import PdfReader, PdfReadError
-from pdf2image import convert_from_path
-
-def pdf_to_images(uploaded_pdf):
-
-    if uploaded_pdf is None:
-        path_to_file = pdf_blank
-        filename = os.path.basename(path_to_file)
-        msg = "Invalid PDF file."
-        images = [cv2.imread(image_blank)]
-    else:
-        # path to the uploaded PDF
-        path_to_file = uploaded_pdf.name
-        filename = os.path.basename(path_to_file)
-
-        try:
-            PdfReader(path_to_file)
-        except PdfReadError:
-            path_to_file = pdf_blank
-            filename = os.path.basename(path_to_file)
-            msg = "Invalid PDF file."
-            images = [cv2.imread(image_blank)]
-        else:
-            try:
-                images = convert_from_path(path_to_file, last_page=max_imgboxes)
-                num_imgs = len(images)
-                msg = f'The PDF "{filename}" was converted into {num_imgs} images.'
-            except Exception as e:
-                msg = f'Error with the PDF "{filename}": it was not converted into images. Error: {e}'
-                images = [cv2.imread(image_wo_content)]
-
-    return filename, msg, images
-
-
-
-def extraction_data_from_image(images):
-
-    num_imgs = len(images)
-
-    if num_imgs > 0:
-
-        custom_config = r'--oem 3 --psm 3 -l eng'
-        results, lines, row_indexes, par_boxes, line_boxes = dict(), dict(), dict(), dict(), dict()
-        images_ids_list, lines_list, par_boxes_list, line_boxes_list, images_list, page_no_list, num_pages_list = list(), list(), list(), list(), list(), list(), list()
-
-        try:
-            for i, image in enumerate(images):
-                # image preprocessing
-                img = image.copy()
-                factor, path_to_img = set_image_dpi_resize(img)
-                img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-                ret, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
-
-                # OCR PyTesseract | get langs of page
-                txt = pytesseract.image_to_string(img, config=custom_config)
-                txt = txt.strip().lower()
-                txt = re.sub(r" +", " ", txt)
-                txt = re.sub(r"(\n\s*)+\n+", "\n", txt)
-                try:
-                    langs = detect_langs(txt)
-                    langs = [langdetect2Tesseract[langs[i].lang] for i in range(len(langs))]
-                    langs_string = '+'.join(langs)
-                except:
-                    langs_string = "eng"
-                langs_string += '+osd'
-                custom_config = f'--oem 3 --psm 3 -l {langs_string}'
-
-                # OCR PyTesseract | get data
-                results[i] = pytesseract.image_to_data(img, config=custom_config, output_type=pytesseract.Output.DICT)
-
-                lines[i], row_indexes[i], par_boxes[i], line_boxes[i] = get_data(results[i], factor, conf_min=0)
-                lines_list.append(lines[i])
-                par_boxes_list.append(par_boxes[i])
-                line_boxes_list.append(line_boxes[i])
-                images_ids_list.append(i)
-                images_list.append(images[i])
-                page_no_list.append(i)
-                num_pages_list.append(num_imgs)
-
-        except Exception as e:
-            print(f"There was an error within the extraction of PDF text by the OCR: {e}")
-        else:
-            dataset = Dataset.from_dict({"images_ids": images_ids_list, "images": images_list, "page_no": page_no_list, "num_pages": num_pages_list, "texts": lines_list, "bboxes_line": line_boxes_list})
-
-            return dataset, lines, row_indexes, par_boxes, line_boxes
-
-## Inference
-
-def prepare_inference_features(example, cls_box = cls_box, sep_box = sep_box):
-
-  images_ids_list, chunks_ids_list, input_ids_list, attention_mask_list, bb_list = list(), list(), list(), list(), list()
-
-  # get batch 
-  batch_images_ids = example["images_ids"]
-  batch_images = example["images"]
-  batch_bboxes_line = example["bboxes_line"]
-  batch_texts = example["texts"]  
-  batch_images_size = [image.size for image in batch_images]
-
-  batch_width, batch_height = [image_size[0] for image_size in batch_images_size], [image_size[1] for image_size in batch_images_size]
-
-  # add a dimension if not a batch but only one image
-  if not isinstance(batch_images_ids, list): 
-    batch_images_ids = [batch_images_ids]
-    batch_images = [batch_images]
-    batch_bboxes_line = [batch_bboxes_line]
-    batch_texts = [batch_texts]
-    batch_width, batch_height = [batch_width], [batch_height] 
-
-  # process all images of the batch
-  for num_batch, (image_id, boxes, texts, width, height) in enumerate(zip(batch_images_ids, batch_bboxes_line, batch_texts, batch_width, batch_height)):
-    tokens_list = []
-    bboxes_list = []
-
-    # add a dimension if only on image
-    if not isinstance(texts, list):
-      texts, boxes = [texts], [boxes]
-
-    # convert boxes to original
-    normalize_bboxes_line = [normalize_box(upperleft_to_lowerright(box), width, height) for box in boxes]
-
-    # sort boxes with texts
-    # we want sorted lists from top to bottom of the image
-    boxes, texts = sort_data_wo_labels(normalize_bboxes_line, texts)
-
-    count = 0
-    for box, text in zip(boxes, texts):
-      tokens = tokenizer.tokenize(text)
-      num_tokens = len(tokens) # get number of tokens
-      tokens_list.extend(tokens)
-
-      bboxes_list.extend([box] * num_tokens) # number of boxes must be the same as the number of tokens
-
-    # use of return_overflowing_tokens=True / stride=doc_stride
-    # to get parts of image with overlap
-    # source: https://huggingface.co/course/chapter6/3b?fw=tf#handling-long-contexts
-    encodings = tokenizer(" ".join(texts), 
-                          truncation=True,
-                          padding="max_length", 
-                          max_length=max_length, 
-                          stride=doc_stride, 
-                          return_overflowing_tokens=True, 
-                          return_offsets_mapping=True
-                          )
-
-    otsm = encodings.pop("overflow_to_sample_mapping")
-    offset_mapping = encodings.pop("offset_mapping")
-
-    # Let's label those examples and get their boxes   
-    sequence_length_prev = 0   
-    for i, offsets in enumerate(offset_mapping):
-      # truncate tokens, boxes and labels based on length of chunk - 2 (special tokens <s> and </s>)
-      sequence_length = len(encodings.input_ids[i]) - 2
-      if i == 0: start = 0
-      else: start += sequence_length_prev - doc_stride
-      end = start + sequence_length
-      sequence_length_prev = sequence_length
-
-      # get tokens, boxes and labels of this image chunk
-      bb = [cls_box] + bboxes_list[start:end] + [sep_box]
-
-      # as the last chunk can have a length < max_length
-      # we must to add [tokenizer.pad_token] (tokens), [sep_box] (boxes) and [-100] (labels)
-      if len(bb) < max_length:
-        bb = bb + [sep_box] * (max_length - len(bb))
-
-      # append results
-      input_ids_list.append(encodings["input_ids"][i])
-      attention_mask_list.append(encodings["attention_mask"][i])
-      bb_list.append(bb)
-      images_ids_list.append(image_id)
-      chunks_ids_list.append(i)
-        
-  return {
-      "images_ids": images_ids_list,
-      "chunk_ids": chunks_ids_list,
-      "input_ids": input_ids_list,
-      "attention_mask": attention_mask_list,
-      "normalized_bboxes": bb_list,
-  }
-
-from torch.utils.data import Dataset
-
-class CustomDataset(Dataset):
-  def __init__(self, dataset, tokenizer):
-    self.dataset = dataset
-    self.tokenizer = tokenizer
-
-  def __len__(self):
-    return len(self.dataset)
-
-  def __getitem__(self, idx):
-    # get item
-    example = self.dataset[idx]
-    encoding = dict()
-    encoding["images_ids"] = example["images_ids"]
-    encoding["chunk_ids"] = example["chunk_ids"]
-    encoding["input_ids"] = example["input_ids"]
-    encoding["attention_mask"] = example["attention_mask"]
-    encoding["bbox"] = example["normalized_bboxes"]
-    
-    return encoding
-
-import torch.nn.functional as F
-
-# get predictions at token level
-def predictions_token_level(images, custom_encoded_dataset):
-
-    num_imgs = len(images)
-    if num_imgs > 0:
-
-        chunk_ids, input_ids, bboxes, outputs, token_predictions  = dict(), dict(), dict(), dict(), dict()
-        images_ids_list = list()
-
-        for i,encoding in enumerate(custom_encoded_dataset):
-
-            # get custom encoded data
-            image_id = encoding['images_ids']
-            chunk_id = encoding['chunk_ids']
-            input_id = torch.tensor(encoding['input_ids'])[None]
-            attention_mask = torch.tensor(encoding['attention_mask'])[None]
-            bbox = torch.tensor(encoding['bbox'])[None]
-
-            # save data in dictionnaries
-            if image_id not in images_ids_list: images_ids_list.append(image_id)
-
-            if image_id in chunk_ids: chunk_ids[image_id].append(chunk_id)
-            else: chunk_ids[image_id] = [chunk_id]
-
-            if image_id in input_ids: input_ids[image_id].append(input_id)
-            else: input_ids[image_id] = [input_id]
-
-            if image_id in bboxes: bboxes[image_id].append(bbox)
-            else: bboxes[image_id] = [bbox]
-
-            # get prediction with forward pass
-            with torch.no_grad():
-                output = model(
-                    input_ids=input_id,
-                    attention_mask=attention_mask,
-                    bbox=bbox
-                    )
-              
-            # save probabilities of predictions in dictionnary
-            if image_id in outputs: outputs[image_id].append(F.softmax(output.logits.squeeze(), dim=-1))
-            else: outputs[image_id] = [F.softmax(output.logits.squeeze(), dim=-1)]
-
-        return outputs, images_ids_list, chunk_ids, input_ids, bboxes
-
-    else:
-        print("An error occurred while getting predictions!")
-
-from functools import reduce
-
-# Get predictions (line level)
-def predictions_line_level(dataset, outputs, images_ids_list, chunk_ids, input_ids, bboxes):
-
-    ten_probs_dict, ten_input_ids_dict, ten_bboxes_dict = dict(), dict(), dict()
-    bboxes_list_dict, input_ids_dict_dict, probs_dict_dict, df = dict(), dict(), dict(), dict()
-
-    if len(images_ids_list) > 0:
-
-        for i, image_id in enumerate(images_ids_list):
-
-            # get image information
-            images_list = dataset.filter(lambda example: example["images_ids"] == image_id)["images"]
-            image = images_list[0]
-            width, height = image.size
-
-            # get data
-            chunk_ids_list = chunk_ids[image_id]
-            outputs_list = outputs[image_id]
-            input_ids_list = input_ids[image_id]
-            bboxes_list = bboxes[image_id]
-
-            # create zeros tensors
-            ten_probs = torch.zeros((outputs_list[0].shape[0] - 2)*len(outputs_list), outputs_list[0].shape[1])
-            ten_input_ids = torch.ones(size=(1, (outputs_list[0].shape[0] - 2)*len(outputs_list)), dtype =int)
-            ten_bboxes = torch.zeros(size=(1, (outputs_list[0].shape[0] - 2)*len(outputs_list), 4), dtype =int)
-
-            if len(outputs_list) > 1:
-              
-                for num_output, (output, input_id, bbox) in enumerate(zip(outputs_list, input_ids_list, bboxes_list)):
-                    start = num_output*(max_length - 2) - max(0,num_output)*doc_stride
-                    end = start + (max_length - 2)
-                    
-                    if num_output == 0:
-                        ten_probs[start:end,:] += output[1:-1]
-                        ten_input_ids[:,start:end] = input_id[:,1:-1]
-                        ten_bboxes[:,start:end,:] = bbox[:,1:-1,:]
-                    else:
-                        ten_probs[start:start + doc_stride,:] += output[1:1 + doc_stride]
-                        ten_probs[start:start + doc_stride,:] = ten_probs[start:start + doc_stride,:] * 0.5
-                        ten_probs[start + doc_stride:end,:] += output[1 + doc_stride:-1]
-
-                        ten_input_ids[:,start:start + doc_stride] = input_id[:,1:1 + doc_stride]
-                        ten_input_ids[:,start + doc_stride:end] = input_id[:,1 + doc_stride:-1]
-
-                        ten_bboxes[:,start:start + doc_stride,:] = bbox[:,1:1 + doc_stride,:]
-                        ten_bboxes[:,start + doc_stride:end,:] = bbox[:,1 + doc_stride:-1,:]
-              
-            else:
-                ten_probs += outputs_list[0][1:-1] 
-                ten_input_ids = input_ids_list[0][:,1:-1] 
-                ten_bboxes = bboxes_list[0][:,1:-1] 
-
-            ten_probs_list, ten_input_ids_list, ten_bboxes_list = ten_probs.tolist(), ten_input_ids.tolist()[0], ten_bboxes.tolist()[0]
-            bboxes_list = list()
-            input_ids_dict, probs_dict = dict(), dict()
-            bbox_prev = [-100, -100, -100, -100]
-            for probs, input_id, bbox in zip(ten_probs_list, ten_input_ids_list, ten_bboxes_list):
-                bbox = denormalize_box(bbox, width, height)
-                if bbox != bbox_prev and bbox != cls_box and bbox != sep_box and bbox[0] != bbox[2] and bbox[1] != bbox[3]:
-                    bboxes_list.append(bbox)
-                    input_ids_dict[str(bbox)] = [input_id]
-                    probs_dict[str(bbox)] = [probs]
-                elif bbox != cls_box and bbox != sep_box and bbox[0] != bbox[2] and bbox[1] != bbox[3]:
-                    input_ids_dict[str(bbox)].append(input_id)
-                    probs_dict[str(bbox)].append(probs)
-                bbox_prev = bbox
-
-            probs_bbox = dict()
-            for i,bbox in enumerate(bboxes_list):
-                probs = probs_dict[str(bbox)]
-                probs = np.array(probs).T.tolist()
-            
-                probs_label = list()
-                for probs_list in probs:
-                    prob_label = reduce(lambda x, y: x*y, probs_list)
-                    prob_label = prob_label**(1./(len(probs_list))) # normalization
-                    probs_label.append(prob_label)
-                max_value = max(probs_label)
-                max_index = probs_label.index(max_value)
-                probs_bbox[str(bbox)] = max_index
-
-            bboxes_list_dict[image_id] = bboxes_list
-            input_ids_dict_dict[image_id] = input_ids_dict
-            probs_dict_dict[image_id] = probs_bbox
-
-            df[image_id] = pd.DataFrame()
-            df[image_id]["bboxes"] = bboxes_list
-            df[image_id]["texts"] = [tokenizer.decode(input_ids_dict[str(bbox)]) for bbox in bboxes_list]
-            df[image_id]["labels"] = [id2label[probs_bbox[str(bbox)]] for bbox in bboxes_list]
-
-        return probs_bbox, bboxes_list_dict, input_ids_dict_dict, probs_dict_dict, df
-
-    else:
-        print("An error occurred while getting predictions!")
-
-
-
-def get_labeled_images(dataset, images_ids_list, bboxes_list_dict, probs_dict_dict):
-
-    labeled_images = list()
-
-    for i, image_id in enumerate(images_ids_list):
-
-        # get image
-        images_list = dataset.filter(lambda example: example["images_ids"] == image_id)["images"]
-        image = images_list[0]
-        width, height = image.shape[1], image.shape[0]
-
-        # get predicted boxes and labels
-        bboxes_list = bboxes_list_dict[image_id]
-        probs_bbox = probs_dict_dict[image_id]
-
-        img_with_boxes = image.copy()
-
-        for bbox in bboxes_list:
-            predicted_label = id2label[probs_bbox[str(bbox)]]
-            bbox = tuple(map(int, bbox))  # Convert to integers
-            cv2.rectangle(img_with_boxes, bbox[0:2], bbox[2:4], label2color[predicted_label], 2)
-            cv2.putText(img_with_boxes, predicted_label, (bbox[0] + 10, bbox[1] - 30), cv2.FONT_HERSHEY_SIMPLEX, 1, label2color[predicted_label], 2)
-
-        labeled_images.append(img_with_boxes)
-
-    return labeled_images
-
-
-# get data of encoded chunk
-def get_encoded_chunk_inference(index_chunk=None):
-
-  # get datasets
-  example = dataset
-  encoded_example = encoded_dataset
-
-  # get randomly a document in dataset
-  if index_chunk == None: index_chunk = random.randint(0, len(encoded_example)-1)
-  encoded_example = encoded_example[index_chunk]
-  encoded_image_ids = encoded_example["images_ids"]
-
-  # get the image
-  example = example.filter(lambda example: example["images_ids"] ==  encoded_image_ids)[0]
-  image = example["images"] # original image
-  width, height = image.size
-  page_no = example["page_no"]
-  num_pages = example["num_pages"]
-
-  # get boxes, texts, categories
-  bboxes, input_ids  = encoded_example["normalized_bboxes"][1:-1], encoded_example["input_ids"][1:-1]
-  bboxes = [denormalize_box(bbox, width, height) for bbox in bboxes]
-  num_tokens = len(input_ids) + 2
-
-  # get unique bboxes and corresponding labels
-  bboxes_list, input_ids_list = list(), list()
-  input_ids_dict = dict()
-  bbox_prev = [-100, -100, -100, -100]
-  for i, (bbox, input_id) in enumerate(zip(bboxes, input_ids)):
-    if bbox != bbox_prev:
-      bboxes_list.append(bbox)
-      input_ids_dict[str(bbox)] = [input_id]
-    else:
-      input_ids_dict[str(bbox)].append(input_id)
-    
-    # start_indexes_list.append(i)
-    bbox_prev = bbox
-  
-  # do not keep "</s><pad><pad>..."
-  if input_ids_dict[str(bboxes_list[-1])][0] == (tokenizer.convert_tokens_to_ids('</s>')):
-    del input_ids_dict[str(bboxes_list[-1])]
-    bboxes_list = bboxes_list[:-1]
-
-  # get texts by line
-  input_ids_list = input_ids_dict.values()
-  texts_list = [tokenizer.decode(input_ids) for input_ids in input_ids_list]
-  
-  # display DataFrame
-  df = pd.DataFrame({"texts": texts_list, "input_ids": input_ids_list, "bboxes": bboxes_list})
-
-  return image, df, num_tokens, page_no, num_pages
-
-
-# display chunk of PDF image and its data
-def display_chunk_lines_inference(index_chunk=None):
-
-    # get image and image data
-    image, df, num_tokens, page_no, num_pages = get_encoded_chunk_inference(index_chunk=index_chunk)
-
-    # get data from dataframe
-    input_ids = df["input_ids"]
-    texts = df["texts"]
-    bboxes = df["bboxes"]
-
-    print(f'Chunk ({num_tokens} tokens) of the PDF (page: {page_no+1} / {num_pages})\n')
-
-    # display image with bounding boxes
-    print(">> PDF image with bounding boxes of lines\n")
-    img_with_boxes = image.copy()
-
-    for box, text in zip(bboxes, texts):
-        color = (0, 0, 255)  # Red color in BGR
-        box = tuple(map(int, box))  # Convert to integers
-        cv2.rectangle(img_with_boxes, box[0:2], box[2:4], color, 2)
-
-    # resize image to original
-    width, height = img_with_boxes.shape[1], img_with_boxes.shape[0]
-    img_with_boxes = cv2.resize(img_with_boxes, (int(0.5*width), int(0.5*height)))
-
-    # display image using OpenCV
-    cv2.imshow("PDF image with bounding boxes of lines", img_with_boxes)
-    cv2.waitKey(0)
-
-    # display image dataframe
-    print("\n>> Dataframe of annotated lines\n")
-    cols = ["texts", "bboxes"]
-    df = df[cols]
-    display(df)  # Assuming display is a function for displaying DataFrames