update app.py

app.py

@@ -80,14 +80,18 @@ import torchvision
 def load_sample(index):
     #sample_index = index
 
-    sample = torch.load(f"samples/val{index-1}.pt")
-    imgs = []
+    image_filenames = []
     for i in range(4):
-        imgs.append(sample["image"][i, :, :, 70])
+        image_filenames[i] = f"thumbnails/image{index-1}_{j}.png"
 
-    pil_images = []
-    for i in range(4):
-        pil_images.append(torchvision.transforms.functional.to_pil_image(imgs[i]))
+    sample = torch.load(f"samples/val{index-1}.pt")
+    #imgs = []
+    #for i in range(4):
+    #    imgs.append(sample["image"][i, :, :, 70])
+
+    #pil_images = []
+    #for i in range(4):
+    #    pil_images.append(torchvision.transforms.functional.to_pil_image(imgs[i]))
 
     imgs_label = []
     for i in range(3):
@@ -97,9 +101,12 @@ def load_sample(index):
     for i in range(3):
         pil_images_label.append(torchvision.transforms.functional.to_pil_image(imgs_label[i]))
 
-    return [index, pil_images[0], pil_images[1], pil_images[2], pil_images[3],
+    return [index, image_filenames[0], image_filenames[1], image_filenames[2], image_filenames[3],
             pil_images_label[0], pil_images_label[1], pil_images_label[2]]
 
+    #return [index, pil_images[0], pil_images[1], pil_images[2], pil_images[3],
+    #        pil_images_label[0], pil_images_label[1], pil_images_label[2]]
+
 
 def predict(sample_index):
     sample = torch.load(f"samples/val{sample_index-1}.pt")
@@ -132,10 +139,16 @@ with gr.Blocks(title="Brain tumor 3D segmentation with MONAIMNIST - ClassCat",
     gr.HTML("""<h4 style="color:navy;">1. Select an example, which includes input images and label images, by clicking "Example x" button.</h4>""")
 
     with gr.Row():
+        input_image0 = gr.Image(label="image channel 0", type="filepath", shape=(240, 240))
+        input_image1 = gr.Image(label="image channel 1", type="filepath", shape=(240, 240))
+        input_image2 = gr.Image(label="image channel 2", type="filepath", shape=(240, 240))
+        input_image3 = gr.Image(label="image channel 3", type="filepath", shape=(240, 240))
+        """
         input_image0 = gr.Image(label="image channel 0", type="pil", shape=(240, 240))
         input_image1 = gr.Image(label="image channel 1", type="pil", shape=(240, 240))
         input_image2 = gr.Image(label="image channel 2", type="pil", shape=(240, 240))
         input_image3 = gr.Image(label="image channel 3", type="pil", shape=(240, 240))
+        """
 
     with gr.Row():
         label_image0 = gr.Image(label="label channel 0", type="pil")