import streamlit as st
from transformers import pipeline
from PyPDF2 import PdfReader
import nltk
import pandas as pd
nltk.download('punkt')
st.title(body='7 - Question Generation')


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


########################################################
st.subheader(body='Proposition 1',divider='orange')

if st.toggle(label='Show Proposition 1'):
    st.title('Question Generator from PDFs')
    if st.checkbox('Show Caption'):
        st.caption('Hugging Face Model used: ramsrigouthamg/t5_squad_v1')
    pipe = pipeline(
        task = 'text2text-generation',
        model = 'ramsrigouthamg/t5_squad_v1'
    )
    file = st.file_uploader(label='Upload',accept_multiple_files=True)
    # pr = st.button(label='Process')
    raw_text = get_pdf_text(file)
    sentences = nltk.sent_tokenize(text=raw_text)
    s = pipe(sentences)
    questions = []
    for i in s:
        x = i['generated_text'][10:]
        questions.append(x)
        # st.write(f':blue[{x}]')
    if st.toggle('Show Text'):
        st.write(raw_text)
    if st.toggle(label='Show Questions'):
        st.subheader("*Generated Questions are:*")
        for i in questions:
            st.write(f':blue[{i}]')
    if st.toggle(label='Show Pipeline Output'):
        st.write(s)
    if st.toggle(label='Show Questions list'):
        st.write(questions)

    if questions:
        df = pd.DataFrame(questions, columns=["Question"])
        csv = df.to_csv(index=False).encode('utf-8')        
        st.download_button(
            label="Download Questions as CSV",
            data=csv,
            file_name='questions.csv',
            mime='text/csv'
        )