Create app.py

app.py

@@ -0,0 +1,69 @@
+import os; import json; import requests
+import streamlit as st
+ES_URL = os.environ.get("ES_URL")
+
+question = 'What is the capital of Netherlands?'
+query_text = 'Query used for keyword search (you can also edit, and experiment with the responses)'
+written_question = st.text_input(query_text, question)
+if written_question:
+    question = 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": {
+                "more_like_this": { "like": question, # "What is the capital city of Netherlands?"
+                "fields": ["content"], "min_term_freq": 1.9, "min_doc_freq": 4, "max_query_terms": 50
+            }}})
+            headers = {'Content-Type': 'application/json'}
+            response = requests.request("GET", url, headers=headers, data=payload) 
+            qa_result = response.json() # print(response.text)
+            
+        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)
+    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)}
+
+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!"}
+"""