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| # https://elrmnd-vocal-pdf-summarizer.hf.space | |
| # Import libraries | |
| import gradio as gr | |
| import PyPDF2 | |
| import torch | |
| from transformers import pipeline | |
| import scipy | |
| import numpy | |
| from gtts import gTTS | |
| from io import BytesIO | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| # Function to extract text from PDF | |
| # Defines a function to extract raw text from a PDF file | |
| def extract_text(pdf_file): | |
| pdfReader = PyPDF2.PdfReader(pdf_file) | |
| pageObj = pdfReader.pages[0] | |
| return pageObj.extract_text() | |
| # Function to summarize text | |
| # Defines a function to summarize the extracted text using facebook/bart-large-cnn | |
| def summarize_text(text): | |
| sentences = text.split(". ") | |
| for i, sentence in enumerate(sentences): | |
| if "Abstract" in sentence: | |
| start = i + 1 | |
| end = start + 6 | |
| break | |
| abstract = ". ".join(sentences[start:end+1]) | |
| # Load BART model & tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-base-book-summary") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-base-book-summary") | |
| # Tokenize abstract | |
| inputs = tokenizer(abstract, | |
| max_length=1024, | |
| return_tensors="pt", | |
| truncation=True) | |
| # Generate summary | |
| summary_ids = model.generate(inputs['input_ids'], | |
| max_length=50, | |
| min_length=30, | |
| no_repeat_ngram_size=3, | |
| encoder_no_repeat_ngram_size=3, | |
| repetition_penalty=3.5, | |
| num_beams=4, | |
| do_sample=True, | |
| early_stopping=False) | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| if '.' in summary: | |
| index = summary.rindex('.') | |
| if index != -1: | |
| summary = summary[:index+1] | |
| return summary | |
| # Function to convert text to audio | |
| # Defines a function to convert text to an audio file using Google Text-to-Speech | |
| def text_to_audio(text): | |
| tts = gTTS(text, lang='en') | |
| buffer = BytesIO() | |
| tts.write_to_fp(buffer) | |
| buffer.seek(0) | |
| return buffer.read() | |
| ### Main function | |
| ### The main function that ties everything together: | |
| ### extracts text, summarizes, and converts to audio. | |
| def audio_pdf(pdf_file): | |
| text = extract_text(pdf_file) | |
| summary = summarize_text(text) | |
| audio = text_to_audio(summary) | |
| return summary, audio | |
| # Define Gradio interface | |
| # Gradio web interface with a file input, text output to display the summary | |
| # and audio output to play the audio file. # Launches the interface | |
| inputs = gr.File() | |
| summary_text = gr.Text() | |
| audio_summary = gr.Audio() | |
| iface = gr.Interface( | |
| fn=audio_pdf, | |
| inputs=inputs, | |
| outputs=[summary_text,audio_summary], | |
| title="The Vocal PDF Summarizer", | |
| description="I will summarize your pdf and transform it in to an audio", | |
| examples=["Article 11 Hidden Technical Debt in Machine Learning Systems.pdf", | |
| "Article 6 BloombergGPT_ A Large Language Model for Finance.pdf", | |
| "Article 5 A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks.pdf", | |
| "Article 8 Llama 2_ Open Foundation and Fine-Tuned Chat Models.pdf" | |
| ] | |
| ) | |
| iface.launch() # Launch the interface |