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import streamlit as st
from transformers import pipeline
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
from transformers import DebertaV2Tokenizer
st.set_page_config(page_title="Automated Question Answering System")
st.title("Automated Question Answering System")
st.subheader("Try")
@st.cache_resource(show_spinner=True)
def question_model():
model_name = "kxx-kkk/FYP_deberta-v3-base-squad2_mrqa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
question_answerer = pipeline("question-answering", model=model, tokenizer=tokenizer)
return question_answerer
st.markdown("<h2 style='text-align: center; color:grey;'>Question Answering on Academic Essays</h2>", unsafe_allow_html=True)
st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is extractive question answering about?<b></h3>", unsafe_allow_html=True)
st.write("Extractive question answering is a Natural Language Processing task where text is provided for a model so that the model can refer to it and make predictions about where the answer to a question is.")
# st.markdown('___')
tab1, tab2 = st.tabs(["Input text", "Upload File"])
with tab1:
sample_question = "What is NLP?"
with open("sample.txt", "r") as text_file:
sample_text = text_file.read()
context = st.text_area("Use the example below / input your essay in English (10,000 characters max)", value=sample_text, max_chars=10000, height=330)
question = st.text_input(label="Use the example question below / enter your own question", value=sample_question)
button = st.button("Get answer")
if button:
with st.spinner(text="Loading question model..."):
question_answerer = question_model()
with st.spinner(text="Getting answer..."):
answer = question_answerer(context=context, question=question)
answer = answer["answer"]
container = st.container(border=True)
container.write("<h5><b>Answer:</b></h5>" + answer, unsafe_allow_html=True)
with tab2:
uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"])
if uploaded_file is not None:
raw_text = str(uploaded_file.read(),"utf-8")
context = st.text_area("", value=raw_text, height=330)
question = st.text_input(label="Enter your question", value=sample_question)
button = st.button("Get answer")
if button:
with st.spinner(text="Loading question model..."):
question_answerer = question_model()
with st.spinner(text="Getting answer..."):
answer = question_answerer(context=context, question=question)
answer = answer["answer"]
st.success(answer)
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