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| # https://huggingface.co./spaces/itsmariamaraki/AAI-Assessment3 | |
| # Here are the imports | |
| import gradio as gr | |
| import PyPDF2 | |
| from PyPDF2 import PdfReader | |
| from pdfminer.high_level import extract_pages, extract_text | |
| from transformers import pipeline, AutoProcessor, AutoModel, AutoTokenizer | |
| import torch | |
| import soundfile as sf | |
| from IPython.display import Audio | |
| from datasets import load_dataset | |
| from io import BytesIO | |
| import os | |
| # Here is the code | |
| def abstract(pdf_file): | |
| pdf_bytes = BytesIO(pdf_file) | |
| pdf_reader = PyPDF2.PdfReader(pdf_bytes) | |
| abstract = '' | |
| for page_number in range(len(pdf_reader.pages)): | |
| text = pdf_reader.pages[page_number].extract_text() | |
| if 'abstract' in text.lower(): #in order to read only the abstract, i set as a start the abstract point & as an end the introduction point | |
| start_index = text.lower().find('abstract') | |
| end_index = text.lower().find('introduction') | |
| abstract = text[start_index:end_index] | |
| break | |
| return abstract | |
| summarization = pipeline('summarization', model = 'pszemraj/long-t5-tglobal-base-16384-book-summary') #best summarization model i tested regarding this assessment | |
| audiospeech = pipeline('text-to-speech', model = 'suno/bark-small') #the voice is a bit distorted but gives a good output & takes less time | |
| def summarization_n_audiospeech(pdf_file): | |
| abstract_text = abstract(pdf_file) | |
| summary = summarization(abstract_text, max_length = 50, min_length = 10)[0]['summary_text'] #didn't know exactly what would give one sentence, so i checked multiple times the min & max lengths regarding the 11th article. for a dif article, those parameters would probably have to be different as well | |
| fin_summary = summary.split('.', 1)[0] + '.' #extract and print only the first sentence of the summary | |
| #converting the summarization into an audio output | |
| tts_output = audiospeech(fin_summary) | |
| audio_data = tts_output['audio'][0] | |
| with BytesIO() as buffer: | |
| sf.write(buffer, audio_data, 16000, format = 'wav') | |
| audio_bytes = buffer.getvalue() | |
| return fin_summary, audio_bytes | |
| iface = gr.Interface( | |
| fn = summarization_n_audiospeech, | |
| inputs = gr.File(label='upload PDF', type='binary'), #if i didn't set a type, the gradio output was an error - searched it online for the solution | |
| outputs = [ | |
| gr.Textbox(label='Summarization of the Abstract:'), | |
| gr.Audio(label="Audio Speech of the Abstract's Summary:") | |
| ], | |
| title = "PDF's Abstract Summarization & Audio Speech Processor", | |
| description = "App that generates a one-line summary of the abstract & a speech audio of this summarization -- requirements: app only accepts PDFs which include an ABSTRACT section", | |
| examples = [os.path.join(os.path.dirname(__file__), 'Hidden_Technical_Debt.pdf'), | |
| os.path.join(os.path.dirname(__file__), 'Semiconductors.pdf'), | |
| os.path.join(os.path.dirname(__file__), 'Efficient_Estimation_of_Word_Representations.pdf') | |
| ] | |
| ) | |
| iface.launch() |