remove rest txt

app.py

@@ -61,28 +61,4 @@ if st.button('Run QA inference (get answer prediction)'):
         st.write(answer)
     else:
         st.write('Write some passage of text and a question')
-        st.stop()
-
-
-"""
-from transformers import pipeline
-
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
-
-question_answerer = pipeline("question-answering")
-
-context = r" Extractive Question Answering is the task of extracting an answer from a text given a question. 
-An example of a question answering dataset is the SQuAD dataset, which is entirely based on that task. 
-If you would like to fine-tune a model on a SQuAD task, you may leverage the 
-examples/pytorch/question-answering/run_squad.py script."
-question = "What is extractive question answering?" #"What is a good example of a question answering dataset?"
-result = question_answerer(question=question, context=context)
-answer = result['answer']
-score = round(result['score'], 4)
-span = f"start: {result['start']}, end: {result['end']}"
-
-st.write(answer)
-st.write(f"score: {score}")
-st.write(f"span: {span}") 
-"""
+        st.stop()