Update app.py

app.py

@@ -1,75 +1,53 @@
-import json
-import streamlit as st
-import requests as req
-
-# TODO: improve layout (columns, sidebar, forms)
-# st.set_page_config(layout='wide')
-
-
+import json; import streamlit as st; import requests as req; from transformers import pipeline
+WIKI_URL = 'https://en.wikipedia.org/w/api.php'; WIKI_BERT = "&titles=BERT_(language_model)"
+WIKI_QUERY = "?format=json&action=query&prop=extracts&explaintext=1"; WIKI_METHOD = 'GET'
+pipe_exqa = pipeline("question-answering") #, model="distilbert-base-cased-distilled-squad"
 st.title('Question Answering example')
-
-
-##########################################################
 st.subheader('1. A simple question (extractive, closed domain)')
-##########################################################
-
-
-WIKI_URL = 'https://en.wikipedia.org/w/api.php'
-WIKI_QUERY = "?format=json&action=query&prop=extracts&explaintext=1"
-WIKI_BERT = "&titles=BERT_(language_model)"
-WIKI_METHOD = 'GET'
-
 response = req.request(WIKI_METHOD, f'{WIKI_URL}{WIKI_QUERY}{WIKI_BERT}')
 resp_json = json.loads(response.content.decode("utf-8"))
 wiki_bert = resp_json['query']['pages']['62026514']['extract']
-paragraph =  wiki_bert
-
-written_passage = st.text_area(
-    'Paragraph used for QA (you can also edit, or copy/paste new content)', 
-    paragraph, 
-    height=250
-)
+paragraph = wiki_bert
+par_text = 'Paragraph used for QA (you can also edit, or copy/paste new content)'
+written_passage = st.text_area(par_text, paragraph, height=250)
 if written_passage:
     paragraph = written_passage
-
-# question = 'How many languages does bert understand?'
-question = 'How many attention heads does Bert have?'
-
-written_question = st.text_input(
-    'Question used for QA (you can also edit, and experiment with the answers)', 
-    question
-)
+question = 'How many attention heads does Bert have?' # question = 'How many languages does bert understand?'
+query_text = 'Question used for QA (you can also edit, and experiment with the answers)'
+written_question = st.text_input(query_text, question)
 if written_question:
     question = written_question
-
-QA_URL = "https://api-inference.huggingface.co/models/deepset/roberta-base-squad2"
-QA_METHOD = 'POST'
-
-
+QA_URL = "https://api-inference.huggingface.co/models/deepset/roberta-base-squad2"; QA_METHOD = 'POST'
 if st.button('Run QA inference (get answer prediction)'):
     if paragraph and question:
         inputs = {'question': question, 'context': paragraph}
         payload = json.dumps(inputs)
         prediction = req.request(QA_METHOD, QA_URL, data=payload)
         answer = json.loads(prediction.content.decode("utf-8"))
-        # >>> answer structure:
-        # {
-        #   "score": 0.24088488519191742,
-        #   "start": 3595,
-        #   "end": 3602,
-        #   "answer": "over 70"
-        # }
-        answer_dict = dict(answer)
-        # st.write(answer_dict)
-        answer_span = answer_dict["answer"]
-        answer_score = answer_dict["score"]
-        st.write(f'Answer: **{answer_span}**')
-        start_par = max(0, answer_dict["start"]-86)
-        stop_para = min(answer_dict["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(answer)
+        # >>> answer structure: # { "answer": "over 70", "score": 0.240, "start": 35, "end": 62 }
+        answer_dict = dict(answer) # st.write(answer_dict)
+        print(answer_dict)
+        if "answer" in answer_dict.keys():
+            answer_span, answer_score = answer_dict["answer"], answer_dict["score"]
+            st.write(f'Answer: **{answer_span}**')
+            start_par, stop_para = max(0, answer_dict["start"]-86), min(answer_dict["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(answer)
+        else:
+            try:
+                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)
     else:
-        st.write('Write some passage of text and a question')
-        st.stop()
+        st.write('Write some passage of text and a question'); st.stop()
+# x = st.slider('Select a value'); st.write(x, 'squared is', x * x)