Update app.py

app.py

@@ -2,9 +2,12 @@ import gradio as gr
 from gender_classification import gender_classification
 from emotion_classification import emotion_classification
 
-# Function to update the selected model state when a button is clicked.
-def select_model(model_name):
-    return model_name
+# Functions to update the model state when a button is clicked.
+def select_gender():
+    return "gender"
+
+def select_emotion():
+    return "emotion"
 
 # Main classification function that calls the appropriate model based on selection.
 def classify(image, model_name):
@@ -16,20 +19,20 @@ def classify(image, model_name):
         return {"Error": "No model selected"}
 
 with gr.Blocks() as demo:
-    # Sidebar with title and model selection buttons
+    # Sidebar with title and model selection buttons.
     with gr.Sidebar():
         gr.Markdown("# SigLIP2 Classification")
         with gr.Row():
             gender_btn = gr.Button("Gender Classification")
             emotion_btn = gr.Button("Emotion Classification")
-        # State to hold the current model choice
+        # State to hold the current model choice.
         selected_model = gr.State("gender")
-        # Set model state when buttons are clicked
-        gender_btn.click(fn=select_model, inputs=[], outputs=selected_model, _js="() => 'gender'")
-        emotion_btn.click(fn=select_model, inputs=[], outputs=selected_model, _js="() => 'emotion'")
+        # 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 display when state changes
+        # Update display when state changes.
         selected_model.change(lambda m: m, selected_model, model_display)
 
     # Main interface: image input, analyze button, and prediction output.