Update main.py

main.py

@@ -33,9 +33,10 @@ def predict(item: Item):
     #image_names = list(glob.glob('./*.jpg'))
     #print("Images:", len(image_names))
 
-
+    image_stream1 = io.BytesIO(item.image1);
+    image_stream2 = io.BytesIO(item.image2);
     
-    encoded_image = model.encode([Image.open(io.BytesIO(item.image1)), Image.open(io.BytesIO(item.image2))], batch_size=128, convert_to_tensor=True, show_progress_bar=True)
+    encoded_image = model.encode([Image.open(image_stream1), Image.open(image_stream2)], 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