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import streamlit as st
from qg_pipeline import Pipeline
## Load NLTK
import nltk
nltk.download('punkt')
# Add a model selector to the sidebar
q_model = st.sidebar.selectbox(
'Select Question Generation Model',
('valhalla/t5-small-qg-hl', 'valhalla/t5-base-qg-hl', 'ck46/t5-base-squad-qa-qg', 'ck46/t5-small-squad-qa-qg', 'ck46/t5-base-hotpot-qa-qg', 'ck46/t5-small-hotpot-qa-qg')
)
a_model = st.sidebar.selectbox(
'Select Answer Extraction Model',
('valhalla/t5-small-qa-qg-hl', 'valhalla/t5-base-qa-qg-hl', 'ck46/t5-base-squad-qa-qg', 'ck46/t5-small-squad-qa-qg', 'ck46/t5-base-hotpot-qa-qg', 'ck46/t5-small-hotpot-qa-qg')
)
st.header('Question-Answer Generation')
st.write(f'Model in use: {model}')
txt = st.text_area('Text for context')
pipeline = Pipeline(
q_model=q_model,
q_tokenizer=q_model,
a_model=a_model,
q_tokenizer=a_model
)
if len(txt) >= 1:
autocards = pipeline(txt)
else:
autocards = []
st.header('Generated question and answers')
st.write(autocards)
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