Update app.py

app.py

@@ -12,10 +12,8 @@ class_names = [
     "tooth_discoloration"
 ]
 
-num_classes = len(class_names)
-
 model = models.resnet50(weights=None)
-model.fc = torch.nn.Linear(model.fc.in_features, num_classes)
+model.fc = torch.nn.Linear(model.fc.in_features, len(class_names))
 
 model.load_state_dict(torch.load('best_model.pth', map_location=torch.device('cpu')))
 model.eval()
@@ -26,7 +24,7 @@ preprocess = transforms.Compose([
     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
 ])
 
-def predict_image(image, model=model, preprocess=preprocess, class_names=class_names):
+def predict_image(image):
     processed_image = preprocess(image).unsqueeze(0)
 
     with torch.no_grad():
@@ -37,11 +35,11 @@ def predict_image(image, model=model, preprocess=preprocess, class_names=class_n
     return predicted_class
 
 iface = gr.Interface(
-    fn=predict_image, 
-    inputs=gr.inputs.Image(type='pil'),
-    outputs=gr.outputs.Label(num_top_classes=1),
-    title="Klasifikasi Gambar Medis",
-    description="Upload gambar untuk memprediksi kelasnya."
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="label",
+    title="Medical Image Classification",
+    description="Upload an image to predict its class."
 )
 
-iface.launch()
+iface.launch()