Update app.py

app.py

@@ -70,8 +70,6 @@ def T2Isearch(query,focussed_word, k=5):
     
     # Search for the nearest neighbors in the FAISS text index
     D, I = text_index.search(query_vector, k)
-    print(D)
-    print(I)
 
     # get rank of all classes wrt to query
     classes_all = []
@@ -81,7 +79,6 @@ def T2Isearch(query,focussed_word, k=5):
     
     for neighbor_y in neighbor_ys:
         classes = np.where(neighbor_y > 0.5)[0]
-        print(classes)
         for _class in classes:
             class_freq[_class] += 1
 
@@ -94,15 +91,18 @@ def T2Isearch(query,focussed_word, k=5):
 
     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]
-    print(class_)
+
 
     # Map the image ids to the corresponding image URLs
+    count = 0
     for i in range(len(image_list)):
-        if class_list[i] == class_ :            
+        if class_list[i] == class_ :
+            count+=1
             image_name = image_list[i]
             image_data = zip_file.open("pascal_raw/images/dataset/"+ image_name)
             image = Image.open(image_data)
             st.image(image, width=600)
+            if count == 5: break
 
 query = st.text_input("Enter your search query here:")
 focussed_word = st.text_input("Enter focussed word here")