Upload 6 files

app.py

@@ -1,5 +1,3 @@
-def sepia(input_img, intensity):
-
 import gradio as gr
 
 from matplotlib import gridspec
@@ -10,10 +8,10 @@ import tensorflow as tf
 from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
 
 feature_extractor = SegformerFeatureExtractor.from_pretrained(
-    "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
+    "mattmdjaga/segformer_b2_clothes"
 )
 model = TFSegformerForSemanticSegmentation.from_pretrained(
-    "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
+    "mattmdjaga/segformer_b2_clothes"
 )
 
 def ade_palette():
@@ -228,21 +226,17 @@ def sepia(input_img):
         color_seg[seg.numpy() == label, :] = color
 
     # Show image + mask
-    pred_img = np.array(input_img) * (1 - intensity) + color_seg * intensity
+    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)),
+                    outputs=['plot'],
+                    examples=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"],
+                    allow_flagging='never')
 
-demo = gr.Interface(
-    fn=sepia,
-    inputs=[gr.inputs.Image(shape=(400, 600)), gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5)],
-    outputs='plot',
-    examples=[["cheonggyecheon_stream_in_seoul_city.jpg", 0.5], ["Incheon_stadium.jpg", 0.7],
-              ["Incheon_city.jpg", 0.3]],
-    title="Semantic Segmentation",
-    description="This is a demo of semantic segmentation using Segformer.",
-    allow_flagging=False,
-)
-demo.launch()
+
+demo.launch()