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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'
)
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