import streamlit as st from transformers import pipeline from transformers import AutoModelForQuestionAnswering, AutoTokenizer st.set_page_config(page_title="Automated Question Answering System") # set page title # heading st.markdown("

Question Answering on Academic Essays

", unsafe_allow_html=True) # description st.markdown("

What is extractive question answering about?

", 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.") # store the model in cache resources to enhance efficiency (ref: https://docs.streamlit.io/library/advanced-features/caching) @st.cache_resource(show_spinner=True) def question_model(): # call my model for question answering model_name = "deepset/deberta-v3-base-squad2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForQuestionAnswering.from_pretrained(model_name) question_answerer = pipeline("question-answering", model=model, tokenizer=tokenizer) return question_answerer # get the answer by passing the context & question to the model def question_answering(context, question): 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_score = str(answer["score"]) answer = answer["answer"] # display the result in container container = st.container(border=True) container.write("
Answer:
"+answer+"

(F1 score: "+answer_score+")


", unsafe_allow_html=True) # choose the source with different tabs tab1, tab2 = st.tabs(["Input text", "Upload File"]) #---------- input text ---------- # if type the text as input with tab1: # set the example sample_question = "What is NLP?" with open("sample.txt", "r") as text_file: sample_text = text_file.read() # Get the initial values of context and question context = st.session_state.get("contextInput", "") question = st.session_state.get("questionInput", "") # Button to try the example example = st.button("Try example") # Update the values if the "Try example" button is clicked if example: context = sample_text question = sample_question # Display the text area and text input with the updated or default values context = st.text_area("Enter the essay below:", value=context, key="contextInput", height=330) question = st.text_input(label="Enter the question: ", value=question, key="questionInput") # perform question answering when "get answer" button clicked button = st.button("Get answer", key="textInput") if button: if context=="" or question=="": st.error ("Please enter BOTH the context and the question", icon="🚨") else: question_answering(context, question) # ---------- upload file ---------- # if upload file as input with tab2: # provide upload place uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"]) # transfer file to context and allow ask question, then perform question answering if uploaded_file is not None: raw_text = str(uploaded_file.read(),"utf-8") context = st.text_area("Your essay context: ", value=raw_text, height=330) question = st.text_input(label="Enter your question", value="Enter question here") # perform question answering when "get answer" button clicked button2 = st.button("Get answer", key="fileInput") if button2: if context=="" or question=="": st.error ("Please enter BOTH the context and the question", icon="🚨") else: question_answering(context, question) st.markdown("




", unsafe_allow_html=True)