Update app.py

app.py

@@ -10,7 +10,6 @@ if written_question:
 if st.button('Run keyword search'):
     if question:
         try:
-            # qa_result = pipe_exqa(question=question, context=paragraph)
             url = f"{ES_URL}/document/_search?pretty"
             # payload = json.dumps({"query":{"match":{"content":"moldova"}}})
             payload = json.dumps({"query": {
@@ -20,50 +19,56 @@ if st.button('Run keyword search'):
             headers = {'Content-Type': 'application/json'}
             response = requests.request("GET", url, headers=headers, data=payload) 
             qa_result = response.json() # print(response.text)
+            # qa_result = pipe_exqa(question=question, context=paragraph)
             
         except Exception as e:
             qa_result = str(e)
 
-        # 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")})')
-        
-        st.write(f'Answer JSON: '); st.write(qa_result)
+        top_5_hits = result['hits']['hits'][:5] # print("First 5 results:")
+        top_5_text = [{'text': hit['_source']['content'][:500],
+                       'confidence': hit['_score']} for hit in top_5_hits ]
+
+        for doc_hit in top_5_text:
+            st.write(f'Search result (and score): ... _{doc_hit["text"]}_ ... (score: {format(doc_hit["confidence"], ".3f")})')
+            
+        st.write(f'Answer JSON: '); st.write(top_5_text) # st.write(qa_result)
     else:
         st.write('Write a query to submit your keyword search'); st.stop()
 
-"""
-result_first_two_hits = result['hits']['hits'][:2] # print("First 2 results:")
-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)}
+# 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)}
+# 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)}"
+# 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!"}
-"""
+# return {"output": output} or {"output": str(question_similarity)} or result or {"Hello": "World!"}