Simplify UI by fixing defaults and renaming controls

app.py

@@ -682,18 +682,9 @@ with gr.Blocks(title="Human Hallucination Prediction", css="""
             
             # Parameters section (initially hidden)
             with gr.Group(visible=False) as params_section:
-                with gr.Row():
-                    model_choice = gr.Dropdown(
-                        choices=["resnet50_robust", "standard_resnet50"], # "resnet50_robust_face" - hidden for deployment
-                        value="resnet50_robust", 
-                        label="Model"
-                    )
-                    
-                    inference_type = gr.Dropdown(
-                        choices=["Prior-Guided Drift Diffusion", "IncreaseConfidence"], 
-                        value="Prior-Guided Drift Diffusion", 
-                        label="Inference Method"
-                    )
+                # Keep core method defaults fixed (not user-editable in simple UI)
+                model_choice = gr.State(value="resnet50_robust")
+                inference_type = gr.State(value="Prior-Guided Drift Diffusion")
                 
                 with gr.Row():
                     eps_slider = gr.Slider(minimum=0.0, maximum=40.0, value=40.0, step=0.01, label="Epsilon (Stimulus Fidelity)")
@@ -707,11 +698,11 @@ with gr.Blocks(title="Human Hallucination Prediction", css="""
                     
                 with gr.Row():
                     step_size_slider = gr.Slider(minimum=0.01, maximum=2.0, value=1.0, step=0.01, 
-                                               label="Update Rate")
+                                               label="Moulation Gain")
                     layer_choice = gr.Dropdown(
                         choices=["all", "conv1", "bn1", "relu", "maxpool", "layer1", "layer2", "layer3", "layer4", "avgpool"],
                         value="all",
-                        label="Model Layer"
+                        label="Hierarchy Level"
                     )
                 
                 gr.Markdown("### 🎯 Adaptive Gaussian mask (spatially varying constraint)")
@@ -887,9 +878,9 @@ with gr.Blocks(title="Human Hallucination Prediction", css="""
     ### Parameters:
     - **Drift Noise**: Initial uncertainty in the prediction process
     - **Diffusion Noise**: Stochastic exploration during prediction
-    - **Update Rate**: Speed of convergence to the predicted hallucination
+    - **Moulation Gain**: Speed of convergence to the predicted hallucination
     - **Number of Iterations**: How many prediction steps to perform
-    - **Model Layer**: Which perceptual level to predict from (early edges vs. high-level objects)
+    - **Hierarchy Level**: Which perceptual level to predict from (early edges vs. high-level objects)
     - **Epsilon (Stimulus Fidelity)**: How closely the prediction must match the input stimulus
     
     ### Why Does This Work?