i cant do any more...

app.py

@@ -15,7 +15,7 @@ model = TFSegformerForSemanticSegmentation.from_pretrained(
     "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
 )
 
-def palette():
+def ade_palette():
 
     return [
         [204, 87, 92],  # road (Reddish)
@@ -45,7 +45,8 @@ labels_list = []
 with open(r'labels.txt', 'r') as fp:
     for line in fp:
         labels_list.append(line[:-1])
-colormap = np.asarray(palette())
+
+colormap = np.asarray(ade_palette())
 
 def label_to_color_image(label):
     if label.ndim != 2:
@@ -55,34 +56,70 @@ def label_to_color_image(label):
         raise ValueError("label value too large.")
     return colormap[label]
 
-# Gradio 인터페이스 정의
-iface = gr.Interface(
-    fn=predict_segmentation,
-    inputs=gr.Image(shape=(564,846)),
-    outputs="image",
-    examples=["city1.jpg","city2.jpg","city3.jpg"],
-)
-iface.launch()
-
-# 이미지 세그멘테이션 함수 정의
-def predict_segmentation(image, model):
-    # 이미지 변환
-    image = Image.fromarray(image.astype('uint8'), 'RGB')
-    image = image.resize((1024, 1024))  # 모델의 입력 크기에 맞게 조절
-    image_array = tf.keras.preprocessing.image.img_to_array(image)
-    image_array = tf.expand_dims(image_array, 0)
-
-    # 모델 추론
-    predictions = model(image_array)["output_0"]
-
-    # 레이블별 색상 매핑
-    segmented_image = tf.zeros_like(predictions)
-    for label, color in label_colors.items():
-        mask = tf.reduce_all(tf.equal(predictions, color), axis=-1, keepdims=True)
-        for i in range(3):
-            segmented_image += tf.cast(mask, tf.float32) * tf.constant(color[i], dtype=tf.float32)
-
-    # 이미지 리턴
-    segmented_image = tf.cast(segmented_image, tf.uint8)
-    segmented_image = tf.image.resize(segmented_image, [image.height, image.width])
-    return segmented_image.numpy()
+def draw_plot(pred_img, seg):
+    fig = plt.figure(figsize=(20, 15))
+
+    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
+
+    plt.subplot(grid_spec[0])
+    plt.imshow(pred_img)
+    plt.axis('off')
+    LABEL_NAMES = np.asarray(labels_list)
+    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
+    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
+
+    unique_labels = np.unique(seg.numpy().astype("uint8"))
+
+    ax = plt.subplot(grid_spec[1])
+    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
+
+    ax.yaxis.tick_left()
+    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
+    plt.xticks([], [])
+    ax.tick_params(width=0.0, labelsize=27)
+    return fig
+
+def sepia(input_img):
+    input_img = Image.fromarray(input_img)
+
+    inputs = feature_extractor(images=input_img, return_tensors="tf")
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+    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
+
+
+demo = gr.Interface(fn=sepia,
+                    inputs=gr.Image(shape=(564,846)),
+                    outputs=['plot'],
+                    live=True,
+                    examples=["city1.jpg","city2.jpg","city3.jpg"],
+                    allow_flagging='never',
+                    title="City Image Segmentation Model",
+                    theme="huggingfacedark",
+                    description=["This model is a high-performance city image segmentation model based on the Segformer architecture provided by NVIDIA. Specifically, the "segformer-b5" model, trained on the Cityscapes dataset, excels at performing intricate segmentation on high-resolution images of 1024x1024 pixels. It accurately identifies various urban elements such as roads, buildings, pedestrians, providing visually rich segmentation results.",
+                                 "This is a machine learning activity project at Kyunggi University."],
+
+
+                    )
+
+
+demo.launch()
+