app.py

# Import  libraries

import gradio as gr
import torch
import PyPDF2
from transformers import pipeline 
import numpy
import scipy
from gtts import gTTS
from io import BytesIO
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from bark import SAMPLE_RATE, generate_audio, preload_models


# 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
            
    if start is not None and end is not None:  
        abstract = ". ".join(sentences[start:end+1])
        #print(abstract)
    else:                                            #if the Abstract is not found
        return("Abstract section not found")

  # 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()
#######################
    #preload_models()
    speech_array = generate_audio(text)
    return (SAMPLE_RATE, speech_array)



### 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="PDF Audio Summarizer 📻",
    description="App that converts an abstract into audio",
    examples=["Attention_is_all_you_need.pdf", 
              "ImageNet_Classification.pdf"
             ]
)

iface.launch() # Launch the interface