update app.py

app.py

@@ -2,6 +2,7 @@ import os
 
 import cv2
 import gradio as gr
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -9,11 +10,14 @@ from PIL import Image
 
 from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
 
+# suppress server-side GUI windows
+matplotlib.pyplot.switch_backend('Agg') 
 
 # setup model
 sam = sam_model_registry["vit_b"](checkpoint="./sam_vit_b_01ec64.pth")
 mask_generator = SamAutomaticMaskGenerator(sam)
 
+
 # copied from: https://github.com/facebookresearch/segment-anything
 def show_anns(anns):
     if len(anns) == 0:
@@ -54,21 +58,17 @@ def segment_image(input_image):
 
 with gr.Blocks() as demo:
 
-    with gr.Row():
-        input_image = gr.Image(label='Input Image')
-        output_image = gr.Image(label='Output Image')
-
-    button = gr.Button('Mask Image')
-    button.click(segment_image, inputs=[input_image], outputs=output_image)
-
-    gr.Examples(
-        examples = [
-            ['./imgs/cat.jpg']
-        ],
-        inputs=[input_image],
-        outputs=[output_image],
-        fn=segment_image,
-        cache_examples=True
-    )
+    gr.Markdown("The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.")
+
+    with gr.Tabs():
+
+        with gr.TabItem("Mask Generator"):
+
+            with gr.Row():
+                image_input = gr.Image()
+                image_output = gr.Image()
+            segment_image_button = gr.Button('Generate Mask')
+    
+        segment_image_button.click(segment_image, inputs=[image_input], outputs=image_output)
 
 demo.launch()