info on demo and test script use gpu

app.py

@@ -51,8 +51,9 @@ def predict(input_image, model_name):
     fig.canvas.draw()
     data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
     data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    # return correct image
-    return sr, data
+    # return correct image and info
+    info = f"{model_name} with {sum(p.numel() for p in generators[MODELS_TYPE.index(model_name)].parameters())} parameters"
+    return info, sr, data
 
 iface = gr.Interface(
     fn=predict, 
@@ -61,6 +62,7 @@ iface = gr.Interface(
          gr.inputs.Radio(MODELS_TYPE)
     ], 
     outputs=[
+        gr.Text(),
         gr.Image(),
         gr.Image()
     ],

test.py

@@ -10,8 +10,8 @@ from models.modelNetC import Generator as GC
 
 
 
-DEVICE='cpu'
-model_type = 'model_b'
+DEVICE='cuda'
+model_type = 'model_c'
 
 modeltype2path = {
     'model_a': 'DTM_exp_train10%_model_a/g-best.pth',