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import gradio as gr
from PyPDF2 import PdfReader
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from gtts import gTTS
from io import BytesIO
import re
import os
model_name = "pszemraj/led-base-book-summary"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def extract_abstract_and_summarize(pdf_file):
try:
with open(pdf_file, "rb") as file:
pdf_reader = PdfReader(file)
abstract_text = ""
for page_num in range(len(pdf_reader.pages)):
page = pdf_reader.pages[page_num]
text = page.extract_text()
abstract_match = re.search(r"\bAbstract\b", text, re.IGNORECASE)
if abstract_match:
start_index = abstract_match.end()
introduction_match = re.search(r"\bIntroduction\b", text[start_index:], re.IGNORECASE)
if introduction_match:
end_index = start_index + introduction_match.start()
else:
end_index = None
abstract_text = text[start_index:end_index]
break
# Summarize the extracted abstract
inputs = tokenizer(abstract_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, min_length=30)
summary = tokenizer.decode(outputs[0])
# Generate audio
speech = gTTS(text=summary, lang="en")
speech_bytes = BytesIO()
speech.write_to_fp(speech_bytes)
# Return individual output values
return summary, speech_bytes.getvalue(), abstract_text.strip()
except Exception as e:
raise Exception(str(e))
interface = gr.Interface(
fn=extract_abstract_and_summarize,
inputs=[gr.File(label="Upload PDF")],
outputs=[gr.Textbox(label="Summary"), gr.Audio()],
title="PDF Summarization & Audio Tool",
description="""PDF Summarization App. This app extracts the abstract from a PDF, summarizes it in one sentence with information till "Introduction", and generates an audio of it. Only upload PDFs with abstracts. Please read the README.MD for information about the app and sample PDFs."""
)
interface.launch()
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