add examples

app.py

@@ -30,7 +30,8 @@ print("Loading resnet...")
 model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
 hidden_state_size = model.fc.in_features
 model.fc = torch.nn.Linear(in_features=hidden_state_size, out_features=2, bias=True)
-model.load_state_dict(torch.load("model_best_epoch_4_59.62.pth"))
+model.to("cuda")
+model.load_state_dict(torch.load("model_best_epoch_4_59.62.pth", map_location=torch.device("cuda")))
 model.to("cuda")
 
 import gradio as gr
@@ -49,7 +50,7 @@ def count_barnacles(input_img, progress=gr.Progress()):
     predicted_labels = torch.cat(predicted_labels_list)
     x = int(math.sqrt(predicted_labels.shape[0]))
     predicted_labels = predicted_labels.reshape([x, x, 2]).detach()
-    label_img = predicted_labels[:, :, :1].cpu().numpy()
+    label_img = predicted_labels[:, :, :1].cuda().numpy()
     label_img -= label_img.min()
     label_img /= label_img.max()
     label_img = (label_img * 255).astype(np.uint8)
@@ -78,9 +79,10 @@ def count_barnacles(input_img, progress=gr.Progress()):
     blank_img_copy = input_img.copy()
     for x, y in points:
         blank_img_copy = cv2.circle(blank_img_copy, (x, y), radius=4, color=(255, 0, 0), thickness=-1)
-    return blank_img_copy, len(list(points))
+    return blank_img_copy, int(len(list(points)))
 
 
 demo = gr.Interface(count_barnacles, gr.Image(shape=(500, 500), type="numpy"),
-                    outputs=[gr.Image(type="numpy"), "number"])
+                    outputs=["image", "number"],
+                    examples="examples")
 demo.queue(concurrency_count=10).launch()