Update app.py

app.py

@@ -1,12 +1,10 @@
 import gradio as gr
-
-from matplotlib import gridspec
-import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
 import tensorflow as tf
 from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
 
+
 feature_extractor = SegformerFeatureExtractor.from_pretrained(
     "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
 )
@@ -85,27 +83,18 @@ def sepia(input_img):
     logits = tf.transpose(logits, [0, 2, 3, 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
-    )  # height, width, 3
-    for label, color in enumerate(colormap):
-        color_seg[seg.numpy() == label, :] = color
-
-    # 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
+    # Return segmentation label image instead of Matplotlib Figure
+    return seg.numpy()
 
+# Gradio Interface 설정
 demo = gr.Interface(fn=sepia,
                     inputs=gr.Image(shape=(800, 600)),
-                    outputs=['plot'],
+                    outputs=['label'],  # 'plot'에서 'label'로 변경
                     examples=["cityoutdoor-1.jpg", "cityoutdoor-2.jpg", "cityoutdoor-3.jpg"],
                     allow_flagging='never')
 
-
+# Gradio 실행
 demo.launch()