update appearance and add examples

app.py

@@ -39,36 +39,59 @@ def show_anns(anns):
             img[:,:,i] = color_mask[i]
         ax.imshow(np.dstack((img, m*0.35)))
 
+
 # demo function
 def segment_image(input_image):
 
-    # generate masks
-    masks = mask_generator.generate(input_image)
+    if input_image is not None:
+
+        # generate masks
+        masks = mask_generator.generate(input_image)
 
-    # add masks to image
-    plt.clf()
-    ppi = 100
-    height, width, _ = input_image.shape
-    plt.figure(figsize=(width / ppi, height / ppi))  # convert pixel to inches
-    plt.imshow(input_image)
-    show_anns(masks)
-    plt.axis('off')
+        # add masks to image
+        plt.clf()
+        ppi = 100
+        height, width, _ = input_image.shape
+        plt.figure(figsize=(width / ppi, height / ppi))  # convert pixel to inches
+        plt.imshow(input_image)
+        show_anns(masks)
+        plt.axis('off')
 
-    # save and get figure
-    plt.savefig('output_figure.png', bbox_inches='tight')
-    output_image = cv2.imread('output_figure.png')
-    return Image.fromarray(output_image)
+        # save and get figure
+        plt.savefig('output_figure.png', bbox_inches='tight')
+        output_image = cv2.imread('output_figure.png')
+        return Image.fromarray(output_image)
 
 
 with gr.Blocks() as demo:
 
-    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.Row():
+        gr.Markdown("## Segment Anything (by Meta AI Research)")
+    with gr.Row():
+        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.Row():
-        image_input = gr.Image()
-        image_output = gr.Image()
 
-    segment_image_button = gr.Button('Generate Mask')
+        with gr.Column():
+            image_input = gr.Image()
+            segment_image_button = gr.Button('Generate Mask')
+
+        with gr.Column():
+            image_output = gr.Image()
+
     segment_image_button.click(segment_image, inputs=[image_input], outputs=image_output)
 
-demo.launch()
+    gr.Examples(
+        examples=[
+            ['./examples/dog.jpg'],
+            ['./examples/groceries.jpg'],
+            ['./examples/truck.jpg']
+
+        ],
+        inputs=[image_input],
+        outputs=[image_output],
+        fn=segment_image,
+        #cache_examples=True
+    )
+
+demo.launch()