Update app.py

app.py

@@ -23,8 +23,8 @@ with fs.open(custom_model_path, 'rb') as f:
 custom_model_file = io.BytesIO(custom_model_content)
 custom_model_state_dict = torch.load(custom_model_file, map_location=torch.device('cpu'))
 
-# Create a new instance of your model
-model = MyModel()
+# Create a new instance of your custom model
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='/content/efficientnet_b3.pt')
 
 # Load your custom model into the new instance
 model.load_state_dict(custom_model_state_dict)
@@ -41,18 +41,6 @@ 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'