# ================================================================ # 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)