Upload app.py

app.py

@@ -21,38 +21,38 @@ def question_model():
     return question_answerer
 
 
-if option == "Extractive question answering":
-    st.markdown("<h2 style='text-align: center; color:grey;'>Extractive Question Answering</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 like to start? Choose an option below", ["I want to input some text", "I want to upload a file"])
-    sample_question = "What did the shepherd boy do to amuse himself?"
-    if source == "I want to input some text":
-        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)
+# if option == "Extractive question answering":
+st.markdown("<h2 style='text-align: center; color:grey;'>Extractive Question Answering</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 like to start? Choose an option below", ["I want to input some text", "I want to upload a file"])
+sample_question = "What did the shepherd boy do to amuse himself?"
+if source == "I want to input some text":
+    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)
+    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.text(answer)
+elif source == "I want to upload a file":
+    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..."):
+            with st.spinner(text="Loading summarization model..."):
                 question_answerer = question_model()
             with st.spinner(text="Getting answer..."):
                 answer = question_answerer(context=context, question=question)
                 answer = answer["answer"]
                 st.text(answer)
-    elif source == "I want to upload a file":
-        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 summarization model..."):
-                    question_answerer = question_model()
-                with st.spinner(text="Getting answer..."):
-                    answer = question_answerer(context=context, question=question)
-                    answer = answer["answer"]
-                    st.text(answer)