app.py

# https://huggingface.co/spaces/FlavioBF/AI_in_production_PRJs

# ================================================================
# 
# import
# 
# ================================================================

#PDF PROCESSING
# To read the PDF
import PyPDF2
# To analyze the PDF layout and extract text
from pdfminer.high_level import extract_pages, extract_text
from pdfminer.layout import LTTextContainer, LTChar, LTRect, LTFigure
# To extract text from tables in PDF
import pdfplumber
# To extract the images from the PDFs
from PIL import Image
from pdf2image import convert_from_path
# To perform OCR to extract text from images
import pytesseract

# To remove the additional created files
import os

#SUMMARIZATION AND AUDIO PROCESSING
import torch
import numpy as np
import scipy
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline, AutoProcessor, AutoModel
from transformers import pipeline 

# -----------------------------------------------------------------------------
# Create a function to extract text

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)

# 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


# 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


# 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

# ..............................................................

def read_pdf(pdf_path):
  # create a PDF file object
  pdfFileObj = open(pdf_path, 'rb')
  # create a PDF reader object
  pdfReaded = PyPDF2.PdfReader(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

# mount drive location

#from google.colab import drive
#drive.mount('/content/drive')

#pdf_path = 'C:/Users/Cristina/Documents/MDS/TERM1_AppliedArtificialIntelligence/Assesment3/NIPS-2015-hidden-technical-debt-in-machine-learning-systems-Paper.pdf' 
#pdf_path="C:/Users/Cristina/Documents/MDS/TERM1_AppliedArtificialIntelligence/Assesment3/hidden-technical-debt-in-machine-learning-systems-Paper.pdf"
#pdf_path2="C:/Users/Cristina/Documents/MDS/TERM1_AppliedArtificialIntelligence/Assesment3/1812_05944.pdf"

pdf_path=os.path.join(os.path.abspath(""), "hidden-technical-debt-in-machine-learning-systems-Paper.pdf")
pdf_path2=os.path.join(os.path.abspath(""), "1812_05944.pdf")


# =======================================
# 
# ======================================= 
def sentence_to_audio(fileobj):


    
    # text mining from pdf
    text_per_page = read_pdf(fileobj.name)
    text_per_page.keys()
    page_1 = text_per_page['Page_0']


    # picking up the abstract from the first page content
    flag=False
    abstract_sect=""
    
    for i in range(len(page_1)):
        if page_1[0][i].strip()=="Abstract":
            flag=True
        if page_1[0][i].strip()=="1 Introduction":
            flag = False
        if flag:
            # abstract_sect contains the Abstract section content
            abstract_sect+=page_1[0][i]

    # abstract summarization
    summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
    summary=(summarizer(abstract_sect))
    summary_text=summary[0].get("summary_text")
    
    # Sentence 2 Speech


    #txt1="Hello ->>          " + fileobj.name + "        <<!"
    #txt1="Hello"
    #txt2="ciccio"

    # Sentence 2 Speech
    s_to_s = pipeline("text-to-speech", model="suno/bark-small")
    generated_audio=s_to_s(summary_text,forward_params={"do_sample": True})
    scipy.io.wavfile.write("s_2_s.wav", rate=generated_audio["sampling_rate"], data=generated_audio["audio"].T)
    return "s_2_s.wav",summary_text    

# ===========================================================    

#summary_txt="It is dangerous to think of machine learning as a free-to-use toolkit, as it is common to incur ongoing maintenance costs in real-world ML systems"

pdf_path=os.path.join(os.path.abspath(""), "hidden-technical-debt-in-machine-learning-systems-Paper.pdf")
#pdf_path2=os.path.join(os.path.abspath(""), "1812_05944.pdf")
pdf_path2=os.path.join(os.path.abspath(""), "Article_4_ExperimentalEvidence_on_the_Productivity_Effects_ of_Generative_ Artificial_Intelligence.pdf")



#iface = gr.Interface(fn=sentence_to_audio, inputs="file", outputs=["audio",gr.Textbox(lines=4,label="one sentence summ.")],title="SINGLE SENTENCE SUMMARY TO AUDIO CONVERSIONE (upload only pdf files with Abstract section)")
#iface.launch(share=True)


demo = gr.Interface(fn=sentence_to_audio, inputs="file", outputs=["audio",gr.Textbox(lines=4,label="one sentence summ.")],examples=[pdf_path,pdf_path2],title="SINGLE SENTENCE SUMMARY TO AUDIO CONVERSION - upload only pdf files with Abstract section -")
demo.launch(share=True)