Update app.py

app.py

@@ -75,6 +75,7 @@ concept_options = list(CONCEPTS_MAP.keys())
 
 examples = [
     ['./IP_Composer/assets/objects/mug.png', './IP_Composer/assets/patterns/splash.png', 'patterns (including color)', None, None, None, None, 80, 30, 30, None,1.0,0, 30],
+    ['./IP_Composer/assets/objects/mug.png', './IP_Composer/assets/patterns/red_pattern.png', 'patterns (including color)', None, None, None, None, 100, 30, 30, None,1.0,0, 30],
     ['./IP_Composer/assets/emotions/joyful.png', './IP_Composer/assets/emotions/sad.png', 'emotions', './IP_Composer/assets/age/kid.png', 'age', None, None, 30, 30, 30, None,1.0,0, 30],
     ['./IP_Composer/assets/flowers/rose_1.jpg', './IP_Composer/assets/flowers/flowers_3.jpg', 'flowers', None, None, None, None, 30, 30, 30, None,1.0,0, 30],
 ]
@@ -356,10 +357,12 @@ following the algorithm proposed in [*IP-Composer: Semantic Composition of Visua
                         scale = gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Scale")
                         randomize_seed = gr.Checkbox(value=True, label="Randomize seed")
                         seed = gr.Number(value=0, label="Seed", precision=0)
-                    with gr.Row():
-                        rank1 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="rank concept 1", info="rank of projection matrix")
-                        rank2 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="rank concept 2")
-                        rank3 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="rank concept 3")
+                    with gr.Column():
+                        gr.Markdown("if concept is not showing enough try to increase the rank")
+                        with gr.Row():
+                            rank1 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="rank concept 1")
+                            rank2 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="rank concept 2")
+                            rank3 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="rank concept 3")
             
             with gr.Column():
                 output_image = gr.Image(label="composed output", show_label=True)