Update app.py

app.py

@@ -21,7 +21,7 @@ class_names = ['Apple Pie','Bibimbap','Cannoli','Edamame','Falafel','French Toas
 # Returns transformed image
 def transform_img(img):
     return the_transform(img) 
-    
+
 # Returns string with class and probability
 def classify_img(img):
     # Applying transformation to the image
@@ -35,21 +35,21 @@ def classify_img(img):
     
     # Converting values to softmax values 
     result = F.softmax(result,dim=1)
-    
     # Grabbing top 3 indices and probabilities for each index
     top3_prob, top3_catid = torch.topk(result,3)
     
     # Dictionary I will display
     model_output = {}
     for i in range(top3_prob.size(1)):
-        model_output[class_names[top3_catid[0][i].item()]] = round(top3_prob[0][i].item())
         
+        model_output[class_names[top3_catid[0][i].item()]] = top3_prob[0][i].item()
+    print(model_output)
     return model_output 
-    
+
 demo = gr.Interface(classify_img,
                         inputs = gr.inputs.Image(type="pil"), 
                         outputs = gr.outputs.Label(type="confidences",num_top_classes=3),
-                        description="Insert food image you would like to classify! Returns confidence % for top three categories <br> Categories: Apple Pie, Bibimbap, Cannoli, Edamame, Falafel, French Toast, Ice Cream, Ramen, Sushi, Tiramisu"
+                        description="Insert food image you would like to classify! Returns confidence % for the top three categories <br> Categories: Apple Pie, Bibimbap, Cannoli, Edamame, Falafel, French Toast, Ice Cream, Ramen, Sushi, Tiramisu"
                     )
 
 demo.launch()