Update app.py

app.py

@@ -29,221 +29,3 @@ from PIL import Image
 from pdf2image import convert_from_path
 import pytesseract
 import os
-def text_extraction(element):
-    # Extracting the text from the in-line text element
-    line_text = element.get_text()
-
-    # Find the formats of the text
-    # Initialize the list with all the formats that appeared in the line of text
-    line_formats = []
-    for text_line in element:
-        if isinstance(text_line, LTTextContainer):
-            # Iterating through each character in the line of text
-            for character in text_line:
-                if isinstance(character, LTChar):
-                    # Append the font name of the character
-                    line_formats.append(character.fontname)
-                    # Append the font size of the character
-                    line_formats.append(character.size)
-    # Find the unique font sizes and names in the line
-    format_per_line = list(set(line_formats))
-
-    # Return a tuple with the text in each line along with its format
-    return (line_text, format_per_line)
-# @title
-# Create a function to crop the image elements from PDFs
-def crop_image(element, pageObj):
-    # Get the coordinates to crop the image from the PDF
-    [image_left, image_top, image_right, image_bottom] = [element.x0,element.y0,element.x1,element.y1]
-    # Crop the page using coordinates (left, bottom, right, top)
-    pageObj.mediabox.lower_left = (image_left, image_bottom)
-    pageObj.mediabox.upper_right = (image_right, image_top)
-    # Save the cropped page to a new PDF
-    cropped_pdf_writer = PyPDF2.PdfWriter()
-    cropped_pdf_writer.add_page(pageObj)
-    # Save the cropped PDF to a new file
-    with open('cropped_image.pdf', 'wb') as cropped_pdf_file:
-        cropped_pdf_writer.write(cropped_pdf_file)
-
-# Create a function to convert the PDF to images
-def convert_to_images(input_file,):
-    images = convert_from_path(input_file)
-    image = images[0]
-    output_file = "PDF_image.png"
-    image.save(output_file, "PNG")
-
-# Create a function to read text from images
-def image_to_text(image_path):
-    # Read the image
-    img = Image.open(image_path)
-    # Extract the text from the image
-    text = pytesseract.image_to_string(img)
-    return text
-# @title
-# Extracting tables from the page
-
-def extract_table(pdf_path, page_num, table_num):
-    # Open the pdf file
-    pdf = pdfplumber.open(pdf_path)
-    # Find the examined page
-    table_page = pdf.pages[page_num]
-    # Extract the appropriate table
-    table = table_page.extract_tables()[table_num]
-    return table
-
-# Convert table into the appropriate format
-def table_converter(table):
-    table_string = ''
-    # Iterate through each row of the table
-    for row_num in range(len(table)):
-        row = table[row_num]
-        # Remove the line breaker from the wrapped texts
-        cleaned_row = [item.replace('\n', ' ') if item is not None and '\n' in item else 'None' if item is None else item for item in row]
-        # Convert the table into a string
-        table_string+=('|'+'|'.join(cleaned_row)+'|'+'\n')
-    # Removing the last line break
-    table_string = table_string[:-1]
-    return table_string
-# @title
-def read_pdf(pdf_path):
-  # create a PDF file object
-  pdfFileObj = open(pdf_path, 'rb')
-  # create a PDF reader object
-  #pdfReaded = PyPDF2.PdfReader(pdfFileObj) #coded out as suggested by chatgpt
-  pdfReaded = PyPDF2.PdfFileReader(pdfFileObj)
-
-  # Create the dictionary to extract text from each image
-  text_per_page = {}
-  # We extract the pages from the PDF
-  for pagenum, page in enumerate(extract_pages(pdf_path)):
-      print("Elaborating Page_" +str(pagenum))
-      # Initialize the variables needed for the text extraction from the page
-      pageObj = pdfReaded.pages[pagenum]
-      page_text = []
-      line_format = []
-      text_from_images = []
-      text_from_tables = []
-      page_content = []
-      # Initialize the number of the examined tables
-      table_num = 0
-      first_element= True
-      table_extraction_flag= False
-      # Open the pdf file
-      pdf = pdfplumber.open(pdf_path)
-      # Find the examined page
-      page_tables = pdf.pages[pagenum]
-      # Find the number of tables on the page
-      tables = page_tables.find_tables()
-
-
-      # Find all the elements
-      page_elements = [(element.y1, element) for element in page._objs]
-      # Sort all the elements as they appear in the page
-      page_elements.sort(key=lambda a: a[0], reverse=True)
-
-      # Find the elements that composed a page
-      for i,component in enumerate(page_elements):
-          # Extract the position of the top side of the element in the PDF
-          pos= component[0]
-          # Extract the element of the page layout
-          element = component[1]
-
-          # Check if the element is a text element
-          if isinstance(element, LTTextContainer):
-              # Check if the text appeared in a table
-              if table_extraction_flag == False:
-                  # Use the function to extract the text and format for each text element
-                  (line_text, format_per_line) = text_extraction(element)
-                  # Append the text of each line to the page text
-                  page_text.append(line_text)
-                  # Append the format for each line containing text
-                  line_format.append(format_per_line)
-                  page_content.append(line_text)
-              else:
-                  # Omit the text that appeared in a table
-                  pass
-
-          # Check the elements for images
-          if isinstance(element, LTFigure):
-              # Crop the image from the PDF
-              crop_image(element, pageObj)
-              # Convert the cropped pdf to an image
-              convert_to_images('cropped_image.pdf')
-              # Extract the text from the image
-              image_text = image_to_text('PDF_image.png')
-              text_from_images.append(image_text)
-              page_content.append(image_text)
-              # Add a placeholder in the text and format lists
-              page_text.append('image')
-              line_format.append('image')
-
-          # Check the elements for tables
-          if isinstance(element, LTRect):
-              # If the first rectangular element
-              if first_element == True and (table_num+1) <= len(tables):
-                  # Find the bounding box of the table
-                  lower_side = page.bbox[3] - tables[table_num].bbox[3]
-                  upper_side = element.y1
-                  # Extract the information from the table
-                  table = extract_table(pdf_path, pagenum, table_num)
-                  # Convert the table information in structured string format
-                  table_string = table_converter(table)
-                  # Append the table string into a list
-                  text_from_tables.append(table_string)
-                  page_content.append(table_string)
-                  # Set the flag as True to avoid the content again
-                  table_extraction_flag = True
-                  # Make it another element
-                  first_element = False
-                  # Add a placeholder in the text and format lists
-                  page_text.append('table')
-                  line_format.append('table')
-
-                  # Check if we already extracted the tables from the page
-                  if element.y0 >= lower_side and element.y1 <= upper_side:
-                      pass
-                  elif not isinstance(page_elements[i+1][1], LTRect):
-                      table_extraction_flag = False
-                      first_element = True
-                      table_num+=1
-
-
-      # Create the key of the dictionary
-      dctkey = 'Page_'+str(pagenum)
-      # Add the list of list as the value of the page key
-      text_per_page[dctkey]= [page_text, line_format, text_from_images,text_from_tables, page_content]
-
-  # Closing the pdf file object
-  pdfFileObj.close()
-
-  # Deleting the additional files created
-  #os.remove('cropped_image.pdf')
-  #os.remove('PDF_image.png')
-  return text_per_page
-
-#google drive
-#from google.colab import drive
-#drive.mount('/content/drive')
-#read PDF
-
-pdf_path = 'test.pdf' #article 11
-#pdf_path = 'https://huggingface.co/spaces/Mishmosh/MichelleAssessment3/blob/main/test.pdf' #article 11
-
-text_per_page = read_pdf(pdf_path)
-
-# This section finds the abstract. My plan was to find the end of the abstract by identifying the same font size as the text 'abstract', but it was too late
-#to try this here since the formatting of the text has already been removed.
-#  Instead I extracted just one paragraph. If an abstract is more than 1 paragraph this will not extract the entire abstract
-abstract_from_pdf='' # define empty variable that will hold the text from the abstract
-found_abstract=False # has the abstract been found
-for key in text_per_page.keys(): # go through keys in dictionary
-  current_item=text_per_page[key] #current key
-  for paragraphs in current_item: #go through each item
-    for index,paragraph in enumerate(paragraphs): #go through each line
-      if 'Abstract\n' == paragraph: #does line match paragraph
-        found_abstract=True #word abstract has been found
-        abstract_from_pdf=paragraphs[index+1] #get next paragraph
-    if found_abstract: #if abstract found
-      break
-print(abstract_from_pdf)
-