Update app.py

app.py

@@ -102,30 +102,26 @@ def predict(image, model, face_detector, device, margin=0.4, input_size=224):
     return image
 
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-model = load_model(device)
-face_detector = dlib.get_frontal_face_detector()
-fn = functools.partial(predict,
-                       model=model,
-                       face_detector=face_detector,
-                       device=device)
-
-image_dir = pathlib.Path('sample_images')
-examples = [path.as_posix() for path in sorted(image_dir.glob('*.jpg'))]
-
-with gr.Interface(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(label='Input', type='filepath')
-            run_button = gr.Button('Run')
-        with gr.Column():
-            result = gr.Image(label='Result')
-
-    gr.Examples(examples=examples,
-                inputs=image,
-                outputs=result,
-                fn=fn,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-    run_button.click(fn=fn, inputs=image, outputs=result, api_name='predict')
-demo.queue(max_size=15).launch()
+def main():
+    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    model = load_model(device)
+    face_detector = dlib.get_frontal_face_detector()
+    fn = functools.partial(predict, model=model, face_detector=face_detector, device=device)
+
+    image_dir = pathlib.Path('sample_images')
+    examples = [path.as_posix() for path in sorted(image_dir.glob('*.jpg'))]
+
+    demo = gr.Interface(
+        fn=fn,
+        inputs=gr.inputs.Image(type="filepath"),
+        outputs="image",
+        examples=examples,
+        title="Age Estimation",
+        description=DESCRIPTION,
+        cache_examples=os.getenv('CACHE_EXAMPLES') == '1'
+    )
+
+    demo.launch()
+
+if __name__ == '__main__':
+    main()