Update app.py

app.py

@@ -39,27 +39,27 @@ st.title('Harry Potter and the Extractive Question Answering Model')
 # Type in HP-related query here
 query = st.text_area("Hello my dears! What is your question? Be patient please, I am not a Ravenclaw!")
 
-# Perform sentence embedding on query and sentence groups
-model_embed_name = 'sentence-transformers/msmarco-distilbert-dot-v5'
-
-model_embed = SentenceTransformer(model_embed_name)
-doc_emb = model_embed.encode(paragraphs)
-query_emb = model_embed.encode(query)
-
-#Compute dot score between query and all document embeddings
-scores = util.cos_sim(query_emb, doc_emb)[0].cpu().tolist()
-
-#Combine docs & scores
-doc_score_pairs = list(zip(paragraphs, scores))
-
-#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]
-
-# Join these similar groups to form the context 
-context = "".join(x[0] for x in doc_score_pairs)
-
 if st.button('Ask'):
+    # Perform sentence embedding on query and sentence groups
+    model_embed_name = 'sentence-transformers/msmarco-distilbert-dot-v5'
+    
+    model_embed = SentenceTransformer(model_embed_name)
+    doc_emb = model_embed.encode(paragraphs)
+    query_emb = model_embed.encode(query)
+    
+    #Compute dot score between query and all document embeddings
+    scores = util.cos_sim(query_emb, doc_emb)[0].cpu().tolist()
+    
+    #Combine docs & scores
+    doc_score_pairs = list(zip(paragraphs, scores))
+    
+    #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]
+    
+    # Join these similar groups to form the context 
+    context = "".join(x[0] for x in doc_score_pairs)
+
     # Perform the querying
     QA_input = {'question': query, 'context': context}
     res = pipe(QA_input)