updated

app.py

@@ -1,4 +1,4 @@
-### 1. Imports and class names setup ###
+### 1. Imports and class names setup ### 
 import gradio as gr
 import os
 import torch
@@ -33,23 +33,23 @@ def predict(img) -> Tuple[Dict, float]:
     """
     # Start the timer
     start_time = timer()
-
+    
     # Transform the target image and add a batch dimension
     img = effnetb2_transforms(img).unsqueeze(0)
-
+    
     # Put model into evaluation mode and turn on inference mode
     effnetb2.eval()
     with torch.inference_mode():
         # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
         pred_probs = torch.softmax(effnetb2(img), dim=1)
-
+    
     # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
     pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-
+    
     # Calculate the prediction time
     pred_time = round(timer() - start_time, 5)
-
-    # Return the prediction dictionary and prediction time
+    
+    # Return the prediction dictionary and prediction time 
     return pred_labels_and_probs, pred_time
 
 ### 4. Gradio app ###
@@ -68,10 +68,10 @@ demo = gr.Interface(fn=predict, # mapping function from input to output
                     outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
                              gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
                     # Create examples list from "examples/" directory
-                    examples=example_list,
+                    examples=example_list, 
                     title=title,
                     description=description,
                     article=article)
 
 # Launch the demo!
-demo.launch()
+demo.launch()