Upload app.py

app.py

@@ -1,34 +1,41 @@
 import streamlit as st
 from transformers import pipeline
 from transformers import AutoModelForQuestionAnswering, AutoTokenizer
-from transformers import DebertaV2Tokenizer
 
+# set page title
 st.set_page_config(page_title="Automated Question Answering System")
-st.title("Automated Question Answering System")
-st.subheader("Try")
+# heading 
+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('___')
 
+# store the model in cache resources to enhance efficiency
 @st.cache_resource(show_spinner=True)
 def question_model():
+    # call my model for question answering
     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('___')
-
+# choose the source with different tabs
 tab1, tab2 = st.tabs(["Input text", "Upload File"])
 
+# if type the text as input
 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", value=sample_text, height=330)
-    question = st.text_input(label="Use the example question below / enter your own question", value=sample_question)
+    example = st.button("Try example")
+    context = st.text_area("Enter the essay below:", height=330)
+    question = st.text_input(label="Enter the question: ")
+    if example:
+        context.value = sample_text
+        question.value = sample_question
+        
     button = st.button("Get answer")
     if button:
         with st.spinner(text="Loading question model..."):
@@ -38,7 +45,8 @@ with tab1:
             answer = answer["answer"]
             container = st.container(border=True)
             container.write("<h5><b>Answer:</b></h5>" + answer, unsafe_allow_html=True)
-                        
+
+# if upload file as input  
 with tab2:
     uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"])
     if uploaded_file is not None: