Update app.py

app.py

@@ -266,44 +266,11 @@ def sepia(input_img):
     fig = draw_plot(pred_img, seg)
     return fig
 
-def custom_interface(input_image):
-    # Process input image
-    input_img = Image.fromarray(input_image)
-    inputs = feature_extractor(images=input_img, return_tensors="tf")
-    outputs = model(**inputs)
-    logits = outputs.logits
+demo = gr.Interface(fn=sepia,
+                    inputs=gr.Image(shape=(400, 600)),
+                    outputs=['plot'],
+                    examples=["c-1.jpg", "c-2.jpg", "c-3.jpg"],
+                    allow_flagging='never')
 
-    logits = tf.transpose(logits, [0, 2, 3, 1])
-    logits = tf.image.resize(
-        logits, input_img.size[::-1]
-    )
-    seg = tf.math.argmax(logits, axis=-1)[0]
-
-    color_seg = np.zeros(
-        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
-    )
-    for label, color in enumerate(colormap):
-        color_seg[seg.numpy() == label, :] = color
-
-    # Combine original image and mask
-    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
-    pred_img = pred_img.astype(np.uint8)
-
-    # Create a figure with custom style
-    fig = plt.figure(figsize=(10, 10))
-    plt.imshow(pred_img)
-    plt.axis('off')
-    plt.title("Segmented Image", fontsize=18, color="#333")  # Customize title
-    return fig
-
-# Create the Gradio interface with the custom style
-demo = gr.Interface(
-    fn=custom_interface,
-    inputs=gr.Image(shape=(400, 600)),
-    outputs=["plot"],
-    examples=["c-1.jpg", "c-2.jpg", "c-3.jpg"],
-    allow_flagging="never",
-    live=False  # Disabling live updates for this example
-)
 
 demo.launch()