without torch

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 
 from PIL import Image
-import torch, os
+# import torch, os
 
 from transformers import CLIPProcessor, CLIPModel
 
@@ -28,6 +28,8 @@ if st.button('Classify', use_container_width=True, type='primary') :
         outputs = model(**inputs)
         logits_per_image = outputs.logits_per_image # this is the image-text similarity score
         probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
-        indice = torch.argmax(probs).item()
+        # indice = torch.argmax().item()
 
-        classes[indice]
+        # classes[indice]
+
+        max(probs)