Update app.py

app.py

@@ -39,46 +39,50 @@ description_p = """# Interactive Instance Segmentation
                 </ol>
                 - Github [link](https://github.com/xinghaochen/TinySAM)
               """
+css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
 
 def infer(img):
-  if img is None:
-      gr.Error("Please upload an image and select a point.")
-  if img["background"] is None:
-      gr.Error("Please upload an image and select a point.")
-  # background (original image) layers[0] ( point prompt) composite (total image)
-  image = img["background"].convert("RGB")
-  point_prompt = img["layers"][0]
-  total_image = img["composite"]
-  predictor.set_image(np.array(image))
+    if img is None:
+        gr.Error("Please upload an image and select a point.")
+    if img["background"] is None:
+        gr.Error("Please upload an image and select a point.")
+    # background (original image) layers[0] ( point prompt) composite (total image)
+    image = img["background"].convert("RGB")
+    point_prompt = img["layers"][0]
+    total_image = img["composite"]
+    predictor.set_image(np.array(image))
+    print("point_prompt : ", point_prompt)
 
-  # get point prompt
-  img_arr = np.array(point_prompt)
-  if not np.any(img_arr):
-    gr.Error("Please select a point on top of the image.")
-  else:
-    nonzero_indices = np.nonzero(img_arr)
+    # get point prompt
     img_arr = np.array(point_prompt)
-    nonzero_indices = np.nonzero(img_arr)
-    center_x = int(np.mean(nonzero_indices[1]))
-    center_y = int(np.mean(nonzero_indices[0]))
-    input_point = np.array([[center_x, center_y]])
-    input_label = np.array([1])
-    masks, scores, logits = predictor.predict(
-      point_coords=input_point,
-      point_labels=input_label,
-  )
-    result_label = [(masks[scores.argmax(), :, :], "mask")]
-    return image, result_label
+    if not np.any(img_arr):
+        gr.Error("Please select a point on top of the image.")
+    else:
+        nonzero_indices = np.nonzero(img_arr)
+        img_arr = np.array(point_prompt)
+        nonzero_indices = np.nonzero(img_arr)
+        center_x = int(np.mean(nonzero_indices[1]))
+        center_y = int(np.mean(nonzero_indices[0]))
+        input_point = np.array([[center_x, center_y]])
+        input_label = np.array([1])
+        masks, scores, logits = predictor.predict(
+            point_coords=input_point,
+            point_labels=input_label,
+        )
+        result_label = [(masks[scores.argmax(), :, :], "mask")]
+        return image, result_label
 
 
-with gr.Blocks() as demo:
-    gr.Markdown("## TinySAM")
-    gr.Markdown("**[TinySAM](https://arxiv.org/abs/2312.13789) is a framework to distill Segment Anything Model.**")
-    gr.Markdown("**To try it out, simply upload an image, click the green tick, and then leave a point mark on what you would like to segment using the pencil on Image Editor.**")
+with gr.Blocks(css=css, title="TinySAM") as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Title
+            gr.Markdown(title)
     with gr.Row():
         with gr.Column():
             im = gr.ImageEditor(
-                type="pil"
+                type="pil",
+                value=default_example[0]
             )
         output = gr.AnnotatedImage()
     with gr.Row():
@@ -87,7 +91,7 @@ with gr.Blocks() as demo:
             gr.Examples(
                 examples=examples,
                 inputs=[im],
-                examples_per_page=4,
+                examples_per_page=6,
             )
 
         with gr.Column():