Update app.py

app.py

@@ -38,32 +38,37 @@ book1_raw_1 = re.sub(r'Mrs. ', 'Mrs ', book1_raw_1)
 # Group into 3 sentences-long parts
 paragraphs = re.findall("[^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!]", book1_raw_1)
 
+# desc = "Uses  LSTM neural network trained on *The Lord of the Rings*. Check out the code [here](https://github.com/christian-doucette/tolkein_text)!"
+
+st.title('Harry Potter and the Extractive Question Answering Model')
+# st.write(desc)
+
 # 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/multi-qa-MiniLM-L6-cos-v1'
+if st.button('Ask'):
+    # Perform sentence embedding on query and sentence groups
+    model_embed_name = 'sentence-transformers/multi-qa-MiniLM-L6-cos-v1'
 
-model_embed = SentenceTransformer(model_embed_name)
-doc_emb = model_embed.encode(paragraphs)
-query_emb = model_embed.encode(query)
+    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()
+    #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))
+    #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],
+    #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)
-
+    # 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)
-out = res.get('answer')
-st.write(out)
+    # Perform the querying
+    QA_input = {'question': query, 'context': context}
+    res = pipe(QA_input)
+    out = res.get('answer')
+    st.write(out)