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()