app.py

# ================================================================
# TESTING VERSION
# ALL-IN-ONE CELL VERSION
# OF THE PROGRAM
# ================================================================
#
# -------------------------
# PDF
# -------------------------

!pip install PyPDF2
!pip install pdfminer.six
!pip install pdfplumber
!pip install pdf2image
!pip install Pillow
!pip install pytesseract
!pip install poppler-utils
!pip install tesseract-ocr
!pip install libtesseract-dev

!pip install fastapi
!pip install -q torch
!pip install -q transformers
!pip install -q gradio
!pip install ffmpeg


#!apt-get install poppler-utils
#!apt install tesseract-ocr
#!apt install libtesseract-dev


# 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

# -----------------------------------------------------------------------------
# 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"


text_per_page = read_pdf(pdf_path)

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]


from transformers import pipeline

summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
summary=(summarizer(abstract_sect))
summary_text=summary[0].get("summary_text")
print(summary_text)



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

import gradio as gr
from transformers import pipeline, AutoProcessor, AutoModel
# =======================================
# 
# ======================================= 
def sentence_to_audio(summary_text):
    # Sentence 2 Speech
    processor = AutoProcessor.from_pretrained("suno/bark-small")
    model = AutoModel.from_pretrained("suno/bark-small")
    inputs = processor(
        text=summary_text,
        return_tensors="pt",
    )
    speech_values = model.generate(**inputs, do_sample=True)
    sampling_rate = model.generation_config.sample_rate
    return sampling_rate, speech_values.cpu().numpy().squeeze()


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"
sentence_to_audio(summary_txt)

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"

demo = gr.Interface(fn=sentence_to_audio, inputs="file", outputs="audio",examples=[pdf_path,pdf_path2])
demo.launch(share=True)