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| # https://huggingface.co/spaces/FlavioBF/AI_in_production_PRJs | |
| # ================================================================ | |
| # | |
| # import | |
| # | |
| # ================================================================ | |
| #PDF PROCESSING | |
| # 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 | |
| #SUMMARIZATION AND AUDIO PROCESSING | |
| import torch | |
| import numpy as np | |
| import scipy | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| from transformers import pipeline, AutoProcessor, AutoModel | |
| from transformers import pipeline | |
| # ----------------------------------------------------------------------------- | |
| # 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" | |
| pdf_path=os.path.join(os.path.abspath(""), "hidden-technical-debt-in-machine-learning-systems-Paper.pdf") | |
| pdf_path2=os.path.join(os.path.abspath(""), "1812_05944.pdf") | |
| # ======================================= | |
| # | |
| # ======================================= | |
| def sentence_to_audio(fileobj): | |
| # text mining from pdf | |
| text_per_page = read_pdf(fileobj.name) | |
| 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] | |
| # abstract summarization | |
| summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY") | |
| summary=(summarizer(abstract_sect)) | |
| summary_text=summary[0].get("summary_text") | |
| # Sentence 2 Speech | |
| #txt1="Hello ->> " + fileobj.name + " <<!" | |
| #txt1="Hello" | |
| #txt2="ciccio" | |
| # Sentence 2 Speech | |
| s_to_s = pipeline("text-to-speech", model="suno/bark-small") | |
| generated_audio=s_to_s(summary_text,forward_params={"do_sample": True}) | |
| scipy.io.wavfile.write("s_2_s.wav", rate=generated_audio["sampling_rate"], data=generated_audio["audio"].T) | |
| return "s_2_s.wav",summary_text | |
| # =========================================================== | |
| #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" | |
| pdf_path=os.path.join(os.path.abspath(""), "hidden-technical-debt-in-machine-learning-systems-Paper.pdf") | |
| #pdf_path2=os.path.join(os.path.abspath(""), "1812_05944.pdf") | |
| pdf_path2=os.path.join(os.path.abspath(""), "Article_4_ExperimentalEvidence_on_the_Productivity_Effects_ of_Generative_ Artificial_Intelligence.pdf") | |
| #iface = gr.Interface(fn=sentence_to_audio, inputs="file", outputs=["audio",gr.Textbox(lines=4,label="one sentence summ.")],title="SINGLE SENTENCE SUMMARY TO AUDIO CONVERSIONE (upload only pdf files with Abstract section)") | |
| #iface.launch(share=True) | |
| demo = gr.Interface(fn=sentence_to_audio, inputs="file", outputs=["audio",gr.Textbox(lines=4,label="one sentence summ.")],examples=[pdf_path,pdf_path2],title="SINGLE SENTENCE SUMMARY TO AUDIO CONVERSION - upload only pdf files with Abstract section -") | |
| demo.launch(share=True) | |