Update main.py

main.py

@@ -29,7 +29,7 @@ def predict(Item):
     # encoded_image = model.encode(Image.open(filepath))
     #image_names = list(glob.glob('./*.jpg'))
     #print("Images:", len(image_names))
-    encoded_image = model.encode([Item.image1, Item.image2]], batch_size=128, convert_to_tensor=True, show_progress_bar=True)
+    encoded_image = model.encode([Item.image1, Item.image2], batch_size=128, convert_to_tensor=True, show_progress_bar=True)
     
     # Now we run the clustering algorithm. This function compares images aganist 
     # all other images and returns a list with the pairs that have the highest