Update app.py

app.py

@@ -43,14 +43,14 @@ def predict(img) -> Tuple[Dict, float]:
     start_time = timer()
     
     # Transform the target image and add a batch dimension
-    img = effnetb2_transforms(img).unsqueeze(0)
+    img1 = effnetb2_transforms(img).unsqueeze(0)
     pix = normalize_2d(np.array(img))
     
     # 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)
+        pred_probs = torch.softmax(effnetb2(img1), 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))}