Update app.py

app.py

@@ -31,8 +31,8 @@ book1_raw_1 = re.sub(r'Mr. ', 'Mr ', book1_raw_1)
 book1_raw_1 = re.sub(r'Ms. ', 'Ms ', book1_raw_1)
 book1_raw_1 = re.sub(r'Mrs. ', 'Mrs ', book1_raw_1)
 
-# Group into 5 sentences-long parts
-paragraphs = re.findall("[^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!]", book1_raw_1)
+# Group into 6 sentences-long parts
+paragraphs = re.findall("[^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!]", book1_raw_1)
 
 st.title('Harry Potter and the Extractive Question Answering Model')
 
@@ -55,7 +55,7 @@ if st.button('Accio Responsa!'):
     
     #Sort by decreasing score and get only 3 most similar groups
     doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1],
-                         reverse=True)[:3]
+                         reverse=True)[:1]
     
     # Join these similar groups to form the context 
     context = "".join(x[0] for x in doc_score_pairs)
@@ -63,5 +63,5 @@ if st.button('Accio Responsa!'):
     # Perform the querying
     QA_input = {'question': query, 'context': context}
     res = pipe(QA_input)
-    out = res.get('answer')
-    st.write(out)
+    #out = res.get('answer')
+    st.write(res)