Update app.py

app.py

@@ -18,15 +18,6 @@ def classify(image, model_name):
     else:
         return {"Error": "No model selected"}
 
-# Function to update button styles based on the selected model.
-def update_button_styles(selected):
-    if selected == "gender":
-        # Set gender button to red (using the "stop" variant) and emotion button to primary.
-        return gr.Button.update(variant="stop"), gr.Button.update(variant="primary")
-    else:
-        # Set emotion button to red and gender button to primary.
-        return gr.Button.update(variant="primary"), gr.Button.update(variant="stop")
-
 with gr.Blocks() as demo:
     # Sidebar with title and model selection buttons.
     with gr.Sidebar():
@@ -36,21 +27,20 @@ with gr.Blocks() as demo:
             emotion_btn = gr.Button("Emotion Classification")
         # State to hold the current model choice.
         selected_model = gr.State("gender")
-        # Update model state on button clicks.
+        # Set model state when buttons are clicked.
         gender_btn.click(fn=select_gender, inputs=[], outputs=selected_model)
         emotion_btn.click(fn=select_emotion, inputs=[], outputs=selected_model)
         gr.Markdown("### Current Model:")
         model_display = gr.Textbox(value="gender", interactive=False)
-        # Update model display when state changes.
+        # Update display when state changes.
         selected_model.change(lambda m: m, selected_model, model_display)
-        # Update the button styles based on selection.
-        selected_model.change(fn=update_button_styles, inputs=selected_model, outputs=[gender_btn, emotion_btn])
 
     # Main interface: image input, analyze button, and prediction output.
     with gr.Column():
         image_input = gr.Image(type="numpy", label="Upload Image")
         analyze_btn = gr.Button("Analyze")
         output_label = gr.Label(label="Prediction Scores")
+
         # When the "Analyze" button is clicked, use the selected model to classify the image.
         analyze_btn.click(fn=classify, inputs=[image_input, selected_model], outputs=output_label)