app.py

# https://huggingface.co/spaces/Mi-Ni/PDFtoAudio

# Unfortunately I wasn´t able to create a running space. I couldn´t adapt my code to create a running app in a huggingface space. After solving a lot of issues I ended up with a problem I wasn´t able to solve. Nevertheless, you´ll find my code below. Sorry that  


#here are the imports: other imports and modules see in requirements

import gradio as gr

import numpy as np





#here is the code


# 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)

def read_pdf(pdf_path):

    # Use pdf_path.name to get the file name from the gr.File object
  with open(pdf_path.name, 'rb') as pdfFileObj:
      pdfReaded = PyPDF2.PdfReader(pdfFileObj)
    
    
    
  # 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


      # 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()

  return text_per_page

#pdf_path = 'Article 11 Hidden Technical Debt in Machine Learning Systems'
pdf_path = gr.File()

text_per_page = read_pdf(pdf_path)
text_per_page.keys()
page_0 = text_per_page['Page_0']
page_1 = text_per_page['Page_1']
page_2 = text_per_page['Page_2']
page_3 = text_per_page['Page_3']
page_4 = text_per_page['Page_4']
page_5 = text_per_page['Page_5']
page_6 = text_per_page['Page_6']
page_7 = text_per_page['Page_7']
page_8 = text_per_page['Page_8']
page_all = page_0 + page_1 +page_2 + page_3 +page_4 + page_5 +page_6 + page_7 + page_8

# Flatten the nested lists
flattened_page_all = list(chain.from_iterable(page_all))

# Convert the flattened list to a string
page_all_string = ''.join(map(str, flattened_page_all))

# Use regular expression to find the abstract text including the delimiters
match = re.search(r'Abstract\n(.*?)(?=\d+\nIntroduction)', page_all_string, re.DOTALL)

# Check if a match is found
if match:
    abstract_text = match.group(1)
    #print(abstract_text)
else:
    print("Abstract not found.")

# Initialize summarization pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")

# Get the summary
summary_result = summarizer(abstract_text, max_length=100, min_length=30, do_sample=False)

# Extract the summary text from the result
summary_text = summary_result[0]['summary_text']

# Replace the dot between two sentences with a space and "and"
merged_summary = summary_text.replace('. ', ' and ', 1)

# Find the index of "and" in the merged summary
and_index = merged_summary.find('and')

# Replace the first letter after "and" with its lowercase equivalent
if and_index != -1 and and_index + 4 < len(merged_summary):
    merged_summary = merged_summary[:and_index + 4] + merged_summary[and_index + 4].lower() + merged_summary[and_index + 5:]

# Print the merged summary
#print(merged_summary)
merged_summary_1 = "A"
synthesiser = pipeline("text-to-speech", "suno/bark")
speech = synthesiser(merged_summary_1, forward_params={"do_sample": True})
Audio(speech["audio"], rate=speech["sampling_rate"])

def PDF_abstract(audio):
  #pdf_path = gr.File()
  pdf_path = 'Article 11 Hidden Technical Debt in Machine Learning Systems'

  text_per_page = read_pdf(pdf_path)
  text_per_page.keys()
  page_0 = text_per_page['Page_0']
  page_1 = text_per_page['Page_1']
  page_2 = text_per_page['Page_2']
  page_3 = text_per_page['Page_3']
  page_4 = text_per_page['Page_4']
  page_5 = text_per_page['Page_5']
  page_6 = text_per_page['Page_6']
  page_7 = text_per_page['Page_7']
  page_8 = text_per_page['Page_8']
  page_all = page_0 + page_1 +page_2 + page_3 +page_4 + page_5 +page_6 + page_7 + page_8

  # Flatten the nested lists
  flattened_page_all = list(chain.from_iterable(page_all))

  # Convert the flattened list to a string
  page_all_string = ''.join(map(str, flattened_page_all))

  # Use regular expression to find the abstract text including the delimiters
  match = re.search(r'Abstract\n(.*?)(?=\d+\nIntroduction)', page_all_string, re.DOTALL)

  # Check if a match is found
  if match:
      abstract_text = match.group(1)
      #print(abstract_text)
  else:
      print("Abstract not found.")

  # Initialize summarization pipeline
  summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")

  # Get the summary
  summary_result = summarizer(abstract_text, max_length=100, min_length=30, do_sample=False)

  # Extract the summary text from the result
  summary_text = summary_result[0]['summary_text']

  # Replace the dot between two sentences with a space and "and"
  merged_summary = summary_text.replace('. ', ' and ', 1)

  # Find the index of "and" in the merged summary
  and_index = merged_summary.find('and')

  # Replace the first letter after "and" with its lowercase equivalent
  if and_index != -1 and and_index + 4 < len(merged_summary):
      merged_summary = merged_summary[:and_index + 4] + merged_summary[and_index + 4].lower() + merged_summary[and_index + 5:]

  # Print the merged summary
  #print(merged_summary)
  merged_summary_1 = "A"
  synthesiser = pipeline("text-to-speech", "suno/bark")
  speech = synthesiser(merged_summary_1, forward_params={"do_sample": True})
  #Audio(speech["audio"], rate=speech["sampling_rate"])

  # Convert audio bytes to playable format
  audio_bytes = BytesIO(speech["audio"])
  audio = Audio(audio_bytes, rate=speech["sampling_rate"])

  return PDF_abstract()     #({"sampling_rate": sr, "raw": y})["text"]

demo = gr.Interface(
    PDF_abstract,
    inputs="file",
    outputs="audio",
    live=True
)

demo.launch()