Upload app.py

app.py

@@ -83,31 +83,29 @@ def sepia(input_img):
     logits = outputs.logits
 
     logits = tf.transpose(logits, [0, 2, 3, 1])
-    logits = tf.image.resize(logits, input_img.size[::-1])
+    logits = tf.image.resize(
+        logits, input_img.size[::-1]
+    )  # We reverse the shape of `image` because `image.size` returns width and height.
     seg = tf.math.argmax(logits, axis=-1)[0]
 
-    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
+    color_seg = np.zeros(
+        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
+    )  # height, width, 3
     for label, color in enumerate(colormap):
         color_seg[seg.numpy() == label, :] = color
 
-    # 이미지 + 마스크 표시
+    # Show image + mask
     pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
     pred_img = pred_img.astype(np.uint8)
 
     fig = draw_plot(pred_img, seg)
     return fig
 
-demo = gr.Interface(
-    fn=sepia,
-    inputs=gr.Image(shape=(400, 600), label="입력 이미지"),
-    outputs=gr.Image(type="plot", label="Image Segmentation 결과"),
-    examples=["cityscape_example1.jpeg"],
-    theme="compact",
-    title="Image Segmentation 결과",
-    description="이미지를 업로드하고 Segmentation 결과를 확인하세요.",
-    allow_flagging='never'
-)
-
-demo.launch()
+demo = gr.Interface(fn=sepia,
+                    inputs=gr.Image(shape=(400, 600)),
+                    outputs=['plot'],
+                    examples=["cityscape_example1.jpeg"],
+                    allow_flagging='never')
 
 
+demo.launch()