update app.py 2023-08-31 08:34

app.py

@@ -54,8 +54,8 @@ def predict(img) -> Tuple[Dict, float]:
         pred_probs = torch.softmax(densenet(img), dim=1).squeeze()
 
     pred_labels_and_probs = {
-        'Normal': pred_probs[0].item(), 
-        'Nodules': pred_probs[1].item()
+        'Nodules': pred_probs[0].item(), 
+        'Normal': pred_probs[1].item()
     }
 
     # Calculate the prediction time
@@ -68,8 +68,8 @@ example_list = [[f"examples/example{i+1}.jpg"] for i in range(3)]
 
 title = "ChestXray Classification"
 description = "A Densenet121 computer vision model to classify images of Xray Chest images as Normal or Nodules."
-article = "Created at (https://github.com/azizche/chest_xray_Classification)."
-
+article = "model train by hamsteryang0"
+# article = "Created at (https://github.com/azizche/chest_xray_Classification)."
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),