version3_44_4

app.py

@@ -14,6 +14,7 @@ model = TFSegformerForSemanticSegmentation.from_pretrained(
     "mattmdjaga/segformer_b2_clothes"
 )
 
+
 def ade_palette():
     """ADE20K palette that maps each class to RGB values."""
     return [
@@ -37,6 +38,7 @@ def ade_palette():
         [255, 99, 71]
     ]
 
+
 labels_list = []
 
 with open(r'../segmentation2/labels.txt', 'r') as fp:
@@ -45,6 +47,7 @@ with open(r'../segmentation2/labels.txt', 'r') as fp:
 
 colormap = np.asarray(ade_palette())
 
+
 def label_to_color_image(label):
     if label.ndim != 2:
         raise ValueError("Expect 2-D input label")
@@ -53,6 +56,7 @@ def label_to_color_image(label):
         raise ValueError("label value too large.")
     return colormap[label]
 
+
 def draw_plot(pred_img, seg):
     fig = plt.figure(figsize=(20, 15))
 
@@ -74,6 +78,7 @@ def draw_plot(pred_img, seg):
     ax.tick_params(width=0.0, labelsize=25)
     return fig
 
+
 def sepia(input_img):
     input_img = Image.fromarray(input_img)
 
@@ -100,6 +105,7 @@ def sepia(input_img):
     fig = draw_plot(pred_img, seg)
     return fig
 
+
 demo = gr.Interface(fn=sepia,
                     inputs=gr.Image(shape=(400, 600)),
                     outputs=['plot'],