Update app.py

app.py

@@ -7,28 +7,6 @@ from gym_workout_classification import workout_classification
 from augmented_waste_classifier import waste_classification
 from age_classification import age_classification
 
-# Functions to update the model state when a button is clicked.
-def select_gender():
-    return "gender"
-
-def select_emotion():
-    return "emotion"
-
-def select_dog_breed():
-    return "dog breed"
-
-def select_deepfake():
-    return "deepfake"
-
-def select_gym_workout():
-    return "gym workout"
-
-def select_waste():
-    return "waste"
-
-def select_age():
-    return "age"
-
 # Main classification function that calls the appropriate model based on selection.
 def classify(image, model_name):
     if model_name == "gender":
@@ -48,44 +26,79 @@ def classify(image, model_name):
     else:
         return {"Error": "No model selected"}
 
+# Function to update the selected model and button styles.
+def select_model(model_name):
+    # Set each button's variant to "primary" if selected, otherwise "secondary"
+    gender_variant = "primary" if model_name == "gender" else "secondary"
+    emotion_variant = "primary" if model_name == "emotion" else "secondary"
+    dog_breed_variant = "primary" if model_name == "dog breed" else "secondary"
+    deepfake_variant = "primary" if model_name == "deepfake" else "secondary"
+    gym_workout_variant = "primary" if model_name == "gym workout" else "secondary"
+    waste_variant = "primary" if model_name == "waste" else "secondary"
+    age_variant = "primary" if model_name == "age" else "secondary"
+    # Return new state and update objects for each button in the specified order.
+    return (
+       model_name,
+       gr.update(variant=gender_variant),
+       gr.update(variant=emotion_variant),
+       gr.update(variant=dog_breed_variant),
+       gr.update(variant=deepfake_variant),
+       gr.update(variant=gym_workout_variant),
+       gr.update(variant=waste_variant),
+       gr.update(variant=age_variant)
+    )
+
 with gr.Blocks() as demo:
     # Sidebar with title and model selection buttons.
     with gr.Sidebar():
         gr.Markdown("# SigLIP2 224")
         with gr.Row():
-            age_btn = gr.Button("Age Classification")
-            gender_btn = gr.Button("Gender Classification")
-            emotion_btn = gr.Button("Emotion Classification")
-            dog_breed_btn = gr.Button("Dog Breed Classification")
-            deepfake_btn = gr.Button("Deepfake vs Real")
-            gym_workout_btn = gr.Button("Gym Workout Classification")
-            waste_btn = gr.Button("Waste Classification")
+            # Initialize buttons with variants. Default is "age" set to primary.
+            age_btn = gr.Button("Age Classification", variant="primary")
+            gender_btn = gr.Button("Gender Classification", variant="secondary")
+            emotion_btn = gr.Button("Emotion Classification", variant="secondary")
+            dog_breed_btn = gr.Button("Dog Breed Classification", variant="secondary")
+            deepfake_btn = gr.Button("Deepfake vs Real", variant="secondary")
+            gym_workout_btn = gr.Button("Gym Workout Classification", variant="secondary")
+            waste_btn = gr.Button("Waste Classification", variant="secondary")
 
         # State to hold the current model choice.
         selected_model = gr.State("age")
 
-        # 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)
-        dog_breed_btn.click(fn=select_dog_breed, inputs=[], outputs=selected_model)
-        deepfake_btn.click(fn=select_deepfake, inputs=[], outputs=selected_model)
-        gym_workout_btn.click(fn=select_gym_workout, inputs=[], outputs=selected_model)
-        waste_btn.click(fn=select_waste, inputs=[], outputs=selected_model)
-        age_btn.click(fn=select_age, inputs=[], outputs=selected_model)
-
         gr.Markdown("### Current Model:")
         model_display = gr.Textbox(value="age", interactive=False)
         # Update display when state changes.
         selected_model.change(lambda m: m, selected_model, model_display)
 
+        # Set up click events for each button, updating state and button variants.
+        gender_btn.click(fn=lambda: select_model("gender"),
+                         inputs=[],
+                         outputs=[selected_model, gender_btn, emotion_btn, dog_breed_btn, deepfake_btn, gym_workout_btn, waste_btn, age_btn])
+        emotion_btn.click(fn=lambda: select_model("emotion"),
+                          inputs=[],
+                          outputs=[selected_model, gender_btn, emotion_btn, dog_breed_btn, deepfake_btn, gym_workout_btn, waste_btn, age_btn])
+        dog_breed_btn.click(fn=lambda: select_model("dog breed"),
+                            inputs=[],
+                            outputs=[selected_model, gender_btn, emotion_btn, dog_breed_btn, deepfake_btn, gym_workout_btn, waste_btn, age_btn])
+        deepfake_btn.click(fn=lambda: select_model("deepfake"),
+                           inputs=[],
+                           outputs=[selected_model, gender_btn, emotion_btn, dog_breed_btn, deepfake_btn, gym_workout_btn, waste_btn, age_btn])
+        gym_workout_btn.click(fn=lambda: select_model("gym workout"),
+                              inputs=[],
+                              outputs=[selected_model, gender_btn, emotion_btn, dog_breed_btn, deepfake_btn, gym_workout_btn, waste_btn, age_btn])
+        waste_btn.click(fn=lambda: select_model("waste"),
+                        inputs=[],
+                        outputs=[selected_model, gender_btn, emotion_btn, dog_breed_btn, deepfake_btn, gym_workout_btn, waste_btn, age_btn])
+        age_btn.click(fn=lambda: select_model("age"),
+                      inputs=[],
+                      outputs=[selected_model, gender_btn, emotion_btn, dog_breed_btn, deepfake_btn, gym_workout_btn, waste_btn, age_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("Classify / Predict")
-        with gr.Column(scale=2): 
-            output_label = gr.Label(label="Prediction Scores")
-
+        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)
 
-demo.launch()
+demo.launch()