Added score code

app.py

@@ -64,17 +64,22 @@ def main():
         query_emb = model(query).astype(float)
         
     #Compute dot score between query and all document embeddings
-        scores = util.dot_score(query_emb, doc_emb.astype(float))#[0].cpu().tolist()
+        #scores = util.dot_score(query_emb, doc_emb.astype(float))#[0].cpu().tolist()
+        
+        #cos_scores = util.pytorch_cos_sim(query_emb, doc_emb)[0]
+        
+        scores = util.pytorch_cos_sim(query_emb, doc_emb)[0]
 
     #Combine docs & scores with other attributes
         doc_score_pairs = list(zip(docs, scores, titles, start_times, end_times, locations))
+        
 
     # top_k results to return
         top_k=3
 
         print(" Your top", top_k, "most similar sessions in the Summit:")
 
-    #Sort by decreasing score
+    #Sort the results in decreasing order and get the first top_k
         doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)