Update app.py

app.py

@@ -80,26 +80,29 @@ def predict_image_class(image):
         top_10[label_dict[idx.item()]] = percentage[idx].item()
     return top_10
 
+def main():
+    # Define Gradio interface
+    description = "This is a demo of EfficientNet trained on Food101 dataset.\
+        Upload an image of food and it will predict the class of the food."
+    inputs = gr.Image()
+    outputs = gr.Label(num_top_classes=10, label="Prediction")
+    gradio_app = gr.Interface(
+        fn=predict_image_class,
+        inputs=inputs,
+        outputs=outputs,
+        title="FoodVision",
+        description=description,
+        theme="snehilsanyal/scikit-learn",
+        examples=[
+            ["examples/pizza.jpg"],
+            ["examples/samosa.jpg"],
+        ],
+    )
+    
+    gradio_app.queue().launch(server_name="0.0.0.0", server_port=7860)
+
+
+if __name__ == "__main__":
+    # Run Gradio app
+    main()
 
-# Define Gradio interface
-description = "This is a demo of EfficientNet trained on Food101 dataset.\
-    Upload an image of food and it will predict the class of the food."
-inputs = gr.inputs.Image()
-outputs = gr.outputs.Label(num_top_classes=10)
-gradio_app = gr.Interface(
-    fn=predict_image_class,
-    inputs=inputs,
-    outputs=outputs,
-    title="FoodVision",
-    description=description,
-    theme="snehilsanyal/scikit-learn",
-    examples=[
-        ["examples/pizza.jpg"],
-        ["examples/samosa.jpg"],
-    ],
-)
-
-# Run Gradio app
-gradio_app.launch(
-    server_port=7860,
-)