Update app.py

app.py

@@ -37,40 +37,3 @@ if st.button('Run keyword search'):
     else:
         st.write('Write a query to submit your keyword search'); st.stop()
 
-
-# if "answer" in qa_result.keys():
-#     answer_span, answer_score = qa_result["answer"], qa_result["score"]
-#     st.write(f'Answer: **{answer_span}**')
-#     start_par, stop_para = max(0, qa_result["start"]-86), min(qa_result["end"]+90, len(paragraph))
-#     answer_context = paragraph[start_par:stop_para].replace(answer_span, f'**{answer_span}**')
-#     st.write(f'Answer context (and score): ... _{answer_context}_ ... (score: {format(answer_score, ".3f")})')
-
-# question_similarity = [ (hit['_score'], hit['_source']['content'][:200])
-#     for hit in result_first_two_hits ] # print(question_similarity)
-
-# top_hit = result['hits']['hits'][0]
-# context = top_hit['_source']['content']
-# # 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 = input # "What is extractive question answering?" 
-# # "What is a good example of a question answering dataset?"
-# print(question)
-# context = context[:5000]
-# print(context)
-# try:
-#     qa_result = pipe_exqa(question=question, context=context)
-# except Exception as e:
-#     return {"output": str(e)}
-
-# return {"output": str(qa_result)}
-    
-# answer = qa_result['answer']
-# score = round(qa_result['score'], 4)
-# span = f"start: {qa_result['start']}, end: {qa_result['end']}"
-# # st.write(answer); st.write(f"score: {score}"); st.write(f"span: {span}")
-# output = f"{str(answer)} \n {str(score)} \n {str(span)}"
-
-# return {"output": output} or {"output": str(question_similarity)} or result or {"Hello": "World!"}