:)

app.py

@@ -44,63 +44,24 @@ def predict(img) -> Tuple[Dict, float]:
 
 # Gradio app
 title = "Food101 🍕🍪"
-description = "EfficientNetB2 model to classify food images into 101 classes. <br>Dataset: [Food-101](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/)"
-article = "Created by [Your Name](https://github.com/aashu-0/Food101) ❤️"
+description = "EfficientNetB2 model to classify food images into 101 classes. Dataset: [Food-101](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/)"
+article = "Created with ❤️ [github/aashu-0](https://github.com/aashu-0/Food101)"
 
 # Example images
 example_list = [['examples/' + example] for example in os.listdir('examples')]
 
-with gr.Blocks() as demo:
-    gr.Markdown(f"# {title}")
-    gr.Markdown(description)
-    
-    # Input mode radio
-    input_mode = gr.Radio(
-        choices=["Upload", "Camera"],
-        value="Upload",
-        label="Image Input Method"
-    )
-    
-    # Dynamic image input
-    image_input = gr.Image(
-        type="pil",
-        label="Food Image",
-        source="upload",
-        interactive=True
-    )
-    
-    # Update image source based on input mode
-    input_mode.change(
-        fn=lambda mode: gr.update(source="upload" if mode == "Upload" else "webcam"),
-        inputs=input_mode,
-        outputs=image_input
-    )
-    
-    # Predict button
-    predict_btn = gr.Button("Classify 🍔")
-    
-    # Outputs
-    label_output = gr.Label(num_top_classes=5, label="Predictions")
-    time_output = gr.Number(label="Prediction Time (s)")
-    
-    # Examples
-    gr.Examples(
-        examples=example_list,
-        inputs=image_input,
-        outputs=[label_output, time_output],
-        fn=predict,
-        cache_examples=True
-    )
-    
-    # Prediction handler
-    predict_btn.click(
-        fn=predict,
-        inputs=image_input,
-        outputs=[label_output, time_output]
-    )
-    
-    gr.Markdown(article)
+# gradio demo
+demo = gr.Interface(fn= predict,
+                    inputs = gr.Image(type= 'pil'),
+                    outputs= [gr.Label(num_top_classes=5, label='Predictions'),
+                              gr.Number(label="Predictions time(in seconds)")],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article,
+                    live= True
+                    )
 
-if __name__ == "__main__":
-    demo.launch(share=True)
+# launch
+demo.launch(share=True)