Update app.py

app.py

@@ -26,11 +26,11 @@ def predict(question, lower_threshold, tags):
   similarities = cosine_similarity(query_embeddings,corpus_embeddings)
   retrieved_doc_id = np.argmax(similarities)
 
-  if similarities[retrieved_doc_id] < lower_threshold:
+  if similarities[0][retrieved_doc_id] < float(lower_threshold):
     ans = pipe(query, candidate_labels=[x.strip() for x in tags.split(",") if x.strip()!=""])
-    ans['query_similarity_score'] = similarities[retrieved_doc_id]
+    ans['query_similarity_score'] = similarities[0][retrieved_doc_id]
     return ans
-  return {"sequence" : data[retrieved_doc_id][-1], 'query_similarity_score' : similarities[retrieved_doc_id]}
+  return {"sequence" : data[retrieved_doc_id][-1], 'query_similarity_score' : similarities[0][retrieved_doc_id]}