Update app.py

app.py

@@ -37,18 +37,27 @@ def visualize_mask(predicted_semantic_map, class_ids, class_colors):
     return image_mask
 
 def get_out_image(image, predicted_semantic_map):
-    class_centers = get_class_centers(predicted_semantic_map, class_dict) 
+    class_centers = get_class_centers(predicted_semantic_map, class_dict)
     mask = visualize_mask(predicted_semantic_map, class_ids, class_colors)
     image_mask = combine_ims(image, mask, val=128)
     draw = ImageDraw.Draw(image_mask)
-    
+
     extracted_tags = []
     for id, (y, x) in class_centers.items():
         class_name = str(class_names[id - 1])
-        extracted_tags.append(class_name)  # Only appending the class_name
+        color = class_colors[id - 1]
+        color_hex = "#{:02x}{:02x}{:02x}".format(*color)  # Convert RGB to hex
+
+        symbol = "●"  # You can choose any symbol you like
+        tag_info = f"{symbol} [{color_hex}] {class_name}"
+        extracted_tags.append(tag_info)
         draw.text((x, y), class_name, fill='black')
 
-    return image_mask, extracted_tags
+    # Joining all tags into a single string, each tag on a new line
+    tags_string = "\n".join(extracted_tags)
+
+    return image_mask, tags_string
+
 
 
 
@@ -76,12 +85,10 @@ model.eval()
 demo = gr.Interface(
     gradio_process, 
     inputs=gr.inputs.Image(type="pil"), 
-    outputs=[gr.outputs.Image(type="pil"), gr.outputs.Label()],
+    outputs=[gr.outputs.Image(type="pil"), gr.outputs.Textbox()],
     title="Semantic Segmentation",
     examples=glob.glob('./examples/*.jpg'),
     allow_flagging="never",
 )
 
-
-
 demo.launch()