Update app.py

app.py

@@ -40,7 +40,7 @@ def predict(img) -> Tuple[Dict, float]:
     effnet_b1.eval()
     with torch.inference_mode():
         # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
-        pred_probs = torch.softmax(effnetb0(img), dim=1)
+        pred_probs = torch.softmax(effnet_b1(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))}