Update app.py

app.py

@@ -16,6 +16,7 @@ def predict(image: Image.Image):
     return result, f"Confidence: {confidence:.2f}%"
 
 def report_feedback():
+    global last_prediction
     if last_prediction is not None:
         image, predicted_label = last_prediction
         correct_label = 1 if predicted_label == 0 else 0  # Invert the label
@@ -24,20 +25,20 @@ def report_feedback():
     return "No prediction available to report."
 
 # Define the Gradio interface for prediction
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Confidence")],
-    title="Vision Transformer Model",
-    description="Upload an image to classify it using the Vision Transformer model.",
-    theme=gr.themes.Soft()
-)
-
-# Define the feedback button
-feedback_button = gr.Button("The model was wrong")
-feedback_button.click(report_feedback)
+with gr.Blocks() as demo:
+    gr.Markdown("### Vision Transformer Model")
+    gr.Markdown("Upload an image to classify it using the Vision Transformer model.")
+    
+    image_input = gr.Image(type="pil", label="Upload Image")
+    prediction_output = gr.Textbox(label="Prediction")
+    confidence_output = gr.Textbox(label="Confidence")
+    
+    submit_btn = gr.Button("Submit")
+    feedback_btn = gr.Button("The model was wrong")
+    
+    submit_btn.click(predict, inputs=image_input, outputs=[prediction_output, confidence_output])
+    feedback_btn.click(report_feedback)
 
 # Launch the Gradio interface
 if __name__ == "__main__":
-    demo.launch(share=True)
-    feedback_button.launch()
+    demo.launch(share=True)