Update app.py

app.py

@@ -1,7 +1,8 @@
 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"
+DPR_MODEL = "deepset/roberta-base-squad2" #, model="distilbert-base-cased-distilled-squad"
+pipe_exqa = pipeline("question-answering", model=DPR_MODEL) 
 st.title('Question Answering example')
 st.subheader('1. A simple question (extractive, closed domain)')
 response = req.request(WIKI_METHOD, f'{WIKI_URL}{WIKI_QUERY}{WIKI_BERT}')
@@ -17,37 +18,20 @@ query_text = 'Question used for QA (you can also edit, and experiment with the a
 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'
 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: # { "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"]
+        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, answer_dict["start"]-86), min(answer_dict["end"]+90, len(paragraph))
+            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(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)
+        
+        st.write(f'Answer JSON: '); st.write(qa_result)
     else:
         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)