# https://huggingface.co./spaces/Mishmosh/MichelleAssessment3 !pip install PyPDF2 !pip install sentencepiece !pip install pdfminer.six !pip install pdfplumber !pip install pdf2image !pip install Pillow !pip install pytesseract # @title !apt-get install poppler-utils !apt install tesseract-ocr !apt install libtesseract-dev import PyPDF2 from pdfminer.high_level import extract_pages, extract_text from pdfminer.layout import LTTextContainer, LTChar, LTRect, LTFigure import pdfplumber 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) # 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 = '/content/drive/MyDrive/ArticleHidden.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) from transformers import pipeline summarizer = pipeline("summarization", model="ainize/bart-base-cnn") #summarizer = pipeline("summarization", model="linydub/bart-large-samsum") # various models were tried and the best one was selected #summarizer = pipeline("summarization", model="slauw87/bart_summarisation") #summarizer = pipeline("summarization", model="facebook/bart-large-cnn") #summarizer = pipeline("summarization", model="google/pegasus-cnn_dailymail") #print(summarizer(abstract_from_pdf, max_length=50, min_length=5, do_sample=False)) summarized_text=(summarizer(abstract_from_pdf)) print(summarized_text) #summary_of_abstract=str(summarizer) #type(summary_of_abstract) #print(summary_of_abstract) # the aim of this section of code is to get a summary of just one sentence by summarizing the summary all while the summary is longer than one sentence. # unfortunately, I tried many many models and none of them actually summarize the text to as short as one sentence. #I had searched for ways to fine tune the summarization model to specify that the summarization should be done in just one sentence but did not find a way to implement it from transformers import pipeline summarized_text_list_list=summarized_text_list['summary_text'] summarizer = pipeline("summarization", model="facebook/bart-large-cnn") #print(summarizer) number_of_sentences=summarized_text_list_list.count('.') print(number_of_sentences) while(number_of_sentences)>1: print(number_of_sentences) summarized_text_list_list=summarizer(summarized_text_list_list)[0]['summary_text'] number_of_sentences-=1 print(summarized_text_list_list) print(number_of_sentences) #text to speech !pip install git+https://github.com/huggingface/transformers.git !pip install datasets sentencepiece import torch import soundfile as sf from IPython.display import Audio from datasets import load_dataset from transformers import pipeline from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts") #text = "The future belongs to those who believe in the beauty of their dreams." #text = (summarized_text_list_list) inputs = processor(text=summarized_text_list_list, return_tensors="pt") from datasets import load_dataset embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") import torch speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings) from transformers import SpeechT5HifiGan vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") with torch.no_grad(): speech = vocoder(spectrogram) speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) Audio(speech, rate=16000)