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import gradio as gr
import torch
import PyPDF2
from transformers import pipeline
import numpy
import scipy
from gtts import gTTS
from io import BytesIO
def extract_text(pdf_file):
pdfReader = PyPDF2.PdfReader(pdf_file)
pageObj = pdfReader.pages[0]
return pageObj.extract_text()
def summarize_text(text):
sentences = text.split(". ")
for i, sentence in enumerate(sentences):
if "Abstract" in sentence:
start = i + 1
end = start + 3
break
abstract = ". ".join(sentences[start:end+1])
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
summary = summarizer(abstract, max_length=50, min_length=30)
return summary[0]['summary_text']
def text_to_audio(text):
tts = gTTS(text, lang='en')
buffer = BytesIO()
tts.write_to_fp(buffer)
buffer.seek(0)
return buffer.read()
def audio_pdf(pdf_file):
text = extract_text(pdf_file)
summary = summarize_text(text)
audio = text_to_audio(summary)
return audio
inputs = gr.File()
audio_summary = gr.Audio()
iface = gr.Interface(
fn=audio_pdf,
inputs=inputs,
outputs=audio_summary,
title="PDF Summarizer"
)
iface.launch() |