dd

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 
 
 import torch
+import torchvision
 
 model = torch.load('v4-epoch=19-val_loss=0.6964-val_accuracy=0.8964.ckpt', map_location=torch.device('cpu'))
 
@@ -11,9 +12,10 @@ from torchvision import transforms
 
 # Download human-readable labels for ImageNet.
 labels = ['good', 'ill']
+CROP=384
 
 def predict(inp):
-  img = transforms.ToTensor()(inp)
+  img = torchvision.transforms.ToTensor()(inp)
   img = torchvision.transforms.Resize((800, 800))(img)
   img = torchvision.transforms.CenterCrop(CROP)(img)
   img = img.unsqueeze(0)
@@ -26,7 +28,7 @@ import gradio as gr
 
 gr.Interface(fn=predict, 
              inputs=gr.Image(type="pil"),
-             outputs=gr.Label(num_top_classes=3),
+             outputs=gr.Label(num_top_classes=1),
              ).launch()