Upload app.py

app.py

@@ -4,14 +4,14 @@ from transformers import AutoModelForQuestionAnswering, AutoTokenizer
 
 # set page title
 st.set_page_config(page_title="Automated Question Answering System")
-# heading 
-st.markdown("<h2 style='text-align: center; color:grey;'>Question Answering on Academic Essays</h2>", unsafe_allow_html=True)
+
+# heading and description
+st.markdown("<h2 style='text-align: center;'>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
-# ref: https://docs.streamlit.io/library/advanced-features/caching 
+
+# 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
@@ -25,7 +25,8 @@ def question_model():
 tab1, tab2 = st.tabs(["Input text", "Upload File"])
 
 # if type the text as input
-with tab1:    
+with tab1:  
+    # set the example  
     sample_question = "What is NLP?"
     with open("sample.txt", "r") as text_file:
         sample_text = text_file.read()
@@ -46,6 +47,7 @@ with tab1:
     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")
     if button:
         with st.spinner(text="Loading question model..."):
@@ -56,13 +58,19 @@ with tab1:
             container = st.container(border=True)
             container.write("<h5><b>Answer:</b></h5>" + answer, unsafe_allow_html=True)
 
+
 # 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("", value=raw_text, height=330)
         question = st.text_input(label="Enter your question", value=sample_question)
+
+        # perform question answering when "get answer" button clicked
         button = st.button("Get answer")
         if button:
             with st.spinner(text="Loading question model..."):