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| import streamlit as st | |
| from transformers import pipeline | |
| from PyPDF2 import PdfReader | |
| import PyPDF2 | |
| import fitz | |
| import os | |
| import nltk | |
| def get_pdf_text(pdf_docs): | |
| text = "" | |
| for pdf in pdf_docs: | |
| pdf_reader = PdfReader(pdf) | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| def main(): | |
| st.title('Question Generator from PDFs') | |
| pipe = pipeline( | |
| task = 'text2text-generation', | |
| model = 'ramsrigouthamg/t5_squad_v1' | |
| ) | |
| file = st.file_uploader(label='Upload',accept_multiple_files=True) | |
| pr = st.button(label='Start') | |
| if pr: | |
| st.write('Hi') | |
| raw_text = get_pdf_text(file) | |
| sentences = nltk.sent_tokenize(text=raw_text) | |
| # st.write(sts) | |
| # for i in sentences: | |
| # st.write(i) | |
| questions = [] | |
| st.subheader("Generated Questions are: ") | |
| s = pipe(sentences) | |
| for i in s: | |
| questions.append(i['generated_text'][10:]) | |
| st.write(i['generated_text'][10:]) | |
| if st.toggle(label='Show Pipeline Output'): | |
| st.write(s) | |
| if st.toggle(label='Show Questions list'): | |
| st.write(questions) | |
| # for i in sts: | |
| # x = pipe(i) | |
| # questions.append(x) | |
| # st.write(x) | |
| if __name__ == '__main__': | |
| main() |