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| # https://huggingface.co./spaces/Mishmosh/MichelleAssessment3 | |
| #python app.py | |
| #python -m pip install --upgrade pip | |
| #pip install torch torchvision torchaudio tensorflow | |
| # Install Rust | |
| #RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y | |
| #RUN python -m pip install --upgrade pip | |
| #pip install --upgrade pip | |
| #RUN pip install --no-cache-dir -r requirements.txt | |
| #RUN pip install --use-feature=in-tree-build tokenizers | |
| #!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) #coded out as suggested by chatgpt | |
| pdfReaded = PyPDF2.PdfFileReader(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 = 'test.pdf' #article 11 | |
| #pdf_path = 'https://huggingface.co./spaces/Mishmosh/MichelleAssessment3/blob/main/test.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) | |