Upload app.py

app.py

@@ -4,17 +4,9 @@ from transformers import pipeline
 st.set_page_config(page_title="Automated Question Answering System")
 st.title("Automated Question Answering System")
 st.subheader("Try")
-# """
-# [![](https://img.shields.io/github/followers/OOlajide?label=OOlajide&style=social)](https://gitHub.com/OOlajide)
-# [![](https://img.shields.io/twitter/follow/sageOlamide?label=@sageOlamide&style=social)](https://twitter.com/sageOlamide)
-# """
-# expander = st.sidebar.expander("About")
-# expander.write("This web app allows you to perform common Natural Language Processing tasks, select a task below to get started.")
 
-# st.sidebar.header("What will you like to do?")
-# option = st.sidebar.radio("", ["Text summarization", "Extractive question answering", "Text generation"])
+@st.cache_resource(show_spinner=True)
 
-@st.cache_data(show_spinner=True)
 def question_model():
     model_name = "deepset/roberta-base-squad2"
     question_answerer = pipeline(model=model_name, tokenizer=model_name, task="question-answering")
@@ -23,20 +15,17 @@ def question_model():
 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('___')
-
-# source = st.radio("How would you upload the essay? Choose an option below", ["I want to input some text", "I want to upload a file"])
-
-sample_question = "What is NLP?"
+# st.markdown('___')
 
 tab1, tab2 = st.tabs(["Input text", "Upload File"])
 
-# if source == "I want to input some text":
-with tab1:
+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 or input your own text in English (10,000 characters max)", value=sample_text, max_chars=10000, height=330)
-    question = st.text_input(label="Use the question below or enter your own question", value=sample_question)
+
+    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..."):
@@ -44,10 +33,8 @@ with tab1:
         with st.spinner(text="Getting answer..."):
             answer = question_answerer(context=context, question=question)
             answer = answer["answer"]
-
-            st.success(answer)
+            st.success('___' + answer)
             
-# elif source == "I want to upload a file":
 with tab2:
     uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"])
     if uploaded_file is not None:
@@ -56,7 +43,7 @@ with tab2:
         question = st.text_input(label="Enter your question", value=sample_question)
         button = st.button("Get answer")
         if button:
-            with st.spinner(text="Loading summarization model..."):
+            with st.spinner(text="Loading question model..."):
                 question_answerer = question_model()
             with st.spinner(text="Getting answer..."):
                 answer = question_answerer(context=context, question=question)