casting to float32

app.py

@@ -79,10 +79,10 @@ def get_activations(intermediate_model, image: list,
 
 def predict_and_analyze(model_name, num_channels, dim, image):
     
-    '''Loads a model with activations, passes through image and shows activations
+    '''
+    Loads a model with activations, passes through image and shows activations
     
     The image must be a numpy array of shape (C, W, W) or (1, C, W, W) 
-    
     '''
     
     num_channels = int(num_channels)
@@ -90,6 +90,7 @@ def predict_and_analyze(model_name, num_channels, dim, image):
 
     print("Loading data")
     image = np.load(image.name, allow_pickle=True)
+    image = image.astype(np.float32)
     
     if len(image.shape) != 4:
         image = image[np.newaxis, :, :, :]
@@ -97,7 +98,7 @@ def predict_and_analyze(model_name, num_channels, dim, image):
     assert image.shape == (1, num_channels, W, W), "Data is the wrong shape"
     
     model_name += '_%i' % (num_channels)
-        
+    
     print("Loading model")
     model = load_model(model_name, activation=True)
     print("Model loaded")
@@ -153,7 +154,7 @@ if __name__ == "__main__":
 
     demo = gr.Interface(
         fn=predict_and_analyze,
-        inputs=[gr.Dropdown(["regnet", "efficientnet"], 
+        inputs=[gr.Dropdown(["efficientnet", "regnet"], 
                             value="efficientnet",
                             label="Model Selection",
                             show_label=True),