Update app.py

app.py

@@ -7,7 +7,7 @@ def predict(image):
     r_image = yolo.detect_image(image)
     return r_image
 
-title = "MASFNet: Multi-scale Adaptive Sampling Fusion Network for Object Detection in Adverse Weather"
+title = "MASFNet: Multi-scale Adaptive Sampling Fusion Network for Object Detection in Adverse Weather "
 description = ""
 article = ""
 
@@ -15,35 +15,34 @@ def reset_interface():
     return gr.update(value=None), gr.update(visible=False)
 
 example_images = [
-    "img/1.png",
-    "img/2.png",
-    "img/3.png",
-    "img/4.png",
-    "img/5.png",
-    "img/6.png",
-    "img/7.png",
-    "img/8.png",
-    "img/10.png",
+    ["img/1.png"],
+    ["img/2.png"],
+    ["img/3.png"],
+    ["img/4.png"],
+    ["img/5.png"],
+    ["img/6.png"],
+    ["img/7.png"],
+    ["img/8.png"],
+    ["img/10.png"],
 ]
 
 with gr.Blocks() as demo:
     gr.Markdown(f"### {title}")
     gr.Markdown(description)
-
+    
     with gr.Row():
         with gr.Column():
             img_input = gr.Image(type="pil", label="Upload an Image")
             submit_btn = gr.Button("Submit")
         with gr.Column():
             output = gr.Image(type="pil", label="Prediction Result")
-
+    
     submit_btn.click(fn=predict, inputs=img_input, outputs=output)
     demo.load(reset_interface, None, [output])
+    gr.Examples(
+        examples=example_images,
+        inputs=img_input,
+    )
+    
 
-    with gr.Row():
-        gr.Examples(
-            examples=example_images,
-            inputs=img_input,
-        )
-
-demo.launch()
+demo.launch(share=True)