Update main.py

main.py

@@ -1,6 +1,7 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
 from typing import List
+from PIL import Image
 
 class Item(BaseModel):
     name: str
@@ -29,7 +30,7 @@ def predict(item: 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([Image.open(item.image1), Image.open(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