Update app.py

app.py

@@ -76,7 +76,6 @@ def T2Isearch(query,focussed_word, k=5):
     Y = train_yt
     neighbor_ys = Y[I]
     class_freq = np.zeros(Y.shape[1])
-    
     for neighbor_y in neighbor_ys:
         classes = np.where(neighbor_y > 0.5)[0]
         for _class in classes:
@@ -89,10 +88,10 @@ def T2Isearch(query,focussed_word, k=5):
     ranked_classes = np.argsort(-class_freq) # chosen order of pivots   -- predicted sequence of all labels for the query
     ranked_classes_after_knn = ranked_classes[:count]  # predicted sequence of top labels after knn search
 
+
     lis  = ['aeroplane', 'bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']
     class_ = lis[ranked_classes_after_knn[0]-1]
 
-
     # Map the image ids to the corresponding image URLs
     count = 0
     for i in range(len(image_list)):