Update app.py

app.py

@@ -41,6 +41,18 @@ def image_classifier(image):
     image = Image.fromarray(image)
     image = data_transform(image)
     image = image.unsqueeze(0)
+    
+    # Use your custom model for inference
+    with torch.no_grad():
+        outputs = model(image)
+        _, predicted = torch.max(outputs.data, 1)
+    
+    # Map the index to a class label
+    labels = ['Healthy', 'Parkinson']
+    predicted_label = labels[predicted.item()]
+    
+    # Return the result
+    return outputs[0].numpy(), predicted_label
 
 # Load and preprocess the image
 img_path = '/content/test_image_healthy.png'