final fix

app.py

@@ -77,12 +77,8 @@ def predict(img) -> Tuple[Dict, float]:
         pred_probs = torch.sigmoid(effnetb2(img)).squeeze()
 
     # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
-    if (torch.round(pred_probs) == 1):
-        pred_labels_and_probs = {
-            'Normal': 1-pred_probs.item(), 'Pneumonia': pred_probs.item()}
-    else:
-        pred_labels_and_probs = {
-            'Normal': pred_probs.item(), 'Pneumonia': 1-pred_probs.item()}
+    pred_labels_and_probs = {
+        'Normal': 1-pred_probs.item(), 'Pneumonia': pred_probs.item()}
 
     # Calculate the prediction time
     pred_time = round(timer() - start_time, 5)