fix app.py

app.py

@@ -28,14 +28,14 @@ app_state = {
     'ch_names': None
 }
 
-sound_manager = None
+sound_control= None
 data_processor = None
 classifier = None
 
 def lazy_init():
-    global sound_manager, data_processor, classifier
-    if sound_manager is None:
-        sound_manager = SoundManager()
+    global sound_control, data_processor, classifier
+    if sound_control is None:
+        sound_control = SoundManager()
     if data_processor is None:
         data_processor = EEGDataProcessor()
     if classifier is None:
@@ -63,18 +63,18 @@ def get_movement_sounds() -> Dict[str, str]:
     if not hasattr(get_movement_sounds, 'play_counter'):
         get_movement_sounds.play_counter = {m: 0 for m in ['left_hand', 'right_hand', 'left_leg', 'right_leg']}
         get_movement_sounds.total_calls = 0
-    from sound_manager import AudioEffectsProcessor
+    from sound_control import AudioEffectsProcessor
     import tempfile
     import soundfile as sf
     # If in DJ mode, use effect-processed file if effect is ON
-    dj_mode = getattr(sound_manager, 'current_phase', None) == 'dj_effects'
-    for movement, sound_file in sound_manager.current_sound_mapping.items():
+    dj_mode = getattr(sound_control, 'current_phase', None) == 'dj_effects'
+    for movement, sound_file in sound_control.current_sound_mapping.items():
         if movement in ['left_hand', 'right_hand', 'left_leg', 'right_leg']:
             if sound_file is not None:
-                sound_path = sound_manager.sound_dir / sound_file
+                sound_path = sound_control.sound_dir / sound_file
                 if sound_path.exists():
                     # Sticky effect for all movements: if effect was ON, keep returning processed audio until next ON
-                    effect_on = dj_mode and sound_manager.active_effects.get(movement, False)
+                    effect_on = dj_mode and sound_control.active_effects.get(movement, False)
                     # If effect just turned ON, update sticky state
                     if effect_on:
                         get_movement_sounds.last_effect_state[movement] = True
@@ -102,7 +102,7 @@ def get_movement_sounds() -> Dict[str, str]:
                         if effect_on:
                             # Apply effect
                             processed = AudioEffectsProcessor.process_layer_with_effects(
-                                data, sr, movement, sound_manager.active_effects
+                                data, sr, movement, sound_control.active_effects
                             )
                             # Save to temp file (persistent for this effect state)
                             tmp = tempfile.NamedTemporaryFile(delete=False, suffix=f'_{movement}_effect.wav')
@@ -166,11 +166,11 @@ def start_composition():
     lazy_init()
     if not app_state['composition_active']:
         app_state['composition_active'] = True
-        sound_manager.start_new_cycle()
+        sound_control.start_new_cycle()
     if app_state['demo_data'] is None:
         return "❌ No data", "❌ No data", "❌ No data", None, None, None, None, None, None, "No EEG data available"
     # Force first trial to always be left_hand/instrumental
-    if len(sound_manager.movements_completed) == 0:
+    if len(sound_control.movements_completed) == 0:
         next_movement = 'left_hand'
         left_hand_label = [k for k, v in classifier.class_names.items() if v == 'left_hand'][0]
         import numpy as np
@@ -182,20 +182,20 @@ def start_composition():
     else:
         epoch_data, true_label = data_processor.simulate_real_time_data(app_state['demo_data'], app_state['demo_labels'], mode="class_balanced")
         true_label_name = classifier.class_names[true_label]
-        next_movement = sound_manager.get_current_target_movement()
+        next_movement = sound_control.get_current_target_movement()
     if next_movement == "cycle_complete":
         return continue_dj_phase()
     predicted_class, confidence, probabilities = classifier.predict(epoch_data)
     predicted_name = classifier.class_names[predicted_class]
     # Only add sound if confidence > threshold, predicted == true label, and true label matches the prompt
     if confidence > CONFIDENCE_THRESHOLD and predicted_name == true_label_name:
-        result = sound_manager.process_classification(predicted_name, confidence, CONFIDENCE_THRESHOLD, force_add=True)
+        result = sound_control.process_classification(predicted_name, confidence, CONFIDENCE_THRESHOLD, force_add=True)
     else:
         result = {'sound_added': False}
     fig = create_eeg_plot(epoch_data, true_label_name, predicted_name, confidence, result['sound_added'], app_state.get('ch_names'))
     # Only play completed movement sounds (layered)
     sounds = get_movement_sounds()
-    completed_movements = sound_manager.movements_completed
+    completed_movements = sound_control.movements_completed
     # Assign audio paths only for completed movements
     left_hand_audio = sounds.get('left_hand') if 'left_hand' in completed_movements else None
     right_hand_audio = sounds.get('right_hand') if 'right_hand' in completed_movements else None
@@ -210,9 +210,9 @@ def start_composition():
     }
     movement_command_lines = []
     # Show 'Now Playing' for all completed movements (layers that are currently playing)
-    completed_movements = sound_manager.movements_completed
+    completed_movements = sound_control.movements_completed
     for movement in ["left_hand", "right_hand", "left_leg", "right_leg"]:
-        sound_file = sound_manager.current_sound_mapping.get(movement, "")
+        sound_file = sound_control.current_sound_mapping.get(movement, "")
         instrument_type = ""
         for key in ["bass", "drums", "instruments", "vocals"]:
             if key in sound_file.lower():
@@ -233,7 +233,7 @@ def start_composition():
     movement_command_text = "🎼 Composition Mode - Movement to Stems Mapping\n" + "\n".join(movement_command_lines)
     # 3. Next Trial: always prompt user
     next_trial_text = "Imagine next movement"
-    composition_info = sound_manager.get_composition_info()
+    composition_info = sound_control.get_composition_info()
     status_text = format_composition_summary(composition_info)
     return (
         movement_command_text,
@@ -274,7 +274,7 @@ def continue_dj_phase():
             # UI update: show which movement is expected
             # Always play all completed movement sounds (layered)
             sounds = get_movement_sounds()
-            completed_movements = sound_manager.movements_completed
+            completed_movements = sound_control.movements_completed
             left_hand_audio = sounds.get('left_hand') if 'left_hand' in completed_movements else None
             right_hand_audio = sounds.get('right_hand') if 'right_hand' in completed_movements else None
             left_leg_audio = sounds.get('left_leg') if 'left_leg' in completed_movements else None
@@ -288,10 +288,10 @@ def continue_dj_phase():
             emoji_map = {"left_hand": "🫲", "right_hand": "🫱", "left_leg": "🦵", "right_leg": "🦵"}
             movement_command_lines = []
             for m in ["left_hand", "right_hand", "left_leg", "right_leg"]:
-                status = "ON" if sound_manager.active_effects.get(m, False) else "off"
+                status = "ON" if sound_control.active_effects.get(m, False) else "off"
                 movement_command_lines.append(f"{emoji_map[m]} {m.replace('_', ' ').title()}: {movement_map[m]['effect']} [{'ON' if status == 'ON' else 'off'}] → {movement_map[m]['instrument']}")
             target_text = "🎧 DJ Mode - Movement to Effect Mapping\n" + "\n".join(movement_command_lines)
-            composition_info = sound_manager.get_composition_info()
+            composition_info = sound_control.get_composition_info()
             status_text = format_composition_summary(composition_info)
             fig = create_eeg_plot(epoch_data, classifier.class_names[true_label], predicted_name, confidence, False, app_state.get('ch_names'))
             return (
@@ -309,7 +309,7 @@ def continue_dj_phase():
     # If correct movement, apply effect and advance order
     effect_applied = False
     if confidence > CONFIDENCE_THRESHOLD and predicted_name == continue_dj_phase.dj_order[continue_dj_phase.dj_index]:
-        result = sound_manager.toggle_dj_effect(predicted_name, brief=True, duration=1.0)
+        result = sound_control.toggle_dj_effect(predicted_name, brief=True, duration=1.0)
         effect_applied = result.get("effect_applied", False)
         continue_dj_phase.dj_index += 1
     else:
@@ -317,7 +317,7 @@ def continue_dj_phase():
     fig = create_eeg_plot(epoch_data, classifier.class_names[true_label], predicted_name, confidence, effect_applied, app_state.get('ch_names'))
     # Always play all completed movement sounds (layered)
     sounds = get_movement_sounds()
-    completed_movements = sound_manager.movements_completed
+    completed_movements = sound_control.movements_completed
     left_hand_audio = sounds.get('left_hand') if 'left_hand' in completed_movements else None
     right_hand_audio = sounds.get('right_hand') if 'right_hand' in completed_movements else None
     left_leg_audio = sounds.get('left_leg') if 'left_leg' in completed_movements else None
@@ -330,16 +330,16 @@ def continue_dj_phase():
         "right_leg": {"effect": "Echo", "instrument": "Vocals"},
     }
     emoji_map = {"left_hand": "🫲", "right_hand": "🫱", "left_leg": "🦵", "right_leg": "🦵"}
-    # Get effect ON/OFF status from sound_manager.active_effects
+    # Get effect ON/OFF status from sound_control.active_effects
     movement_command_lines = []
     for m in ["left_hand", "right_hand", "left_leg", "right_leg"]:
         # Show [ON] only if effect is currently active (True), otherwise [off]
-        status = "ON" if sound_manager.active_effects.get(m, False) else "off"
+        status = "ON" if sound_control.active_effects.get(m, False) else "off"
         movement_command_lines.append(f"{emoji_map[m]} {m.replace('_', ' ').title()}: {movement_map[m]['effect']} [{'ON' if status == 'ON' else 'off'}] → {movement_map[m]['instrument']}")
     target_text = "🎧 DJ Mode - Movement to Effect Mapping\n" + "\n".join(movement_command_lines)
     # In DJ mode, Next Trial should only show the prompt, not the predicted/target movement
     predicted_text = "Imagine next movement"
-    composition_info = sound_manager.get_composition_info()
+    composition_info = sound_control.get_composition_info()
     status_text = format_composition_summary(composition_info)
     # Ensure exactly 10 outputs: [textbox, textbox, plot, audio, audio, audio, audio, textbox, timer, button]
     # Use fig for the plot, and fill all outputs with correct types
@@ -471,11 +471,11 @@ def create_interface():
                     ''' Stop composing and reset state (works in both building and DJ mode). '''
                     timer_counter["count"] = 0
                     app_state['composition_active'] = False  # Ensure new cycle on next start
-                    # Reset sound_manager state for new session
-                    sound_manager.current_phase = "building"
-                    sound_manager.composition_layers = {}
-                    sound_manager.movements_completed = set()
-                    sound_manager.active_effects = {m: False for m in ["left_hand", "right_hand", "left_leg", "right_leg"]}
+                    # Reset sound_control state for new session
+                    sound_control.current_phase = "building"
+                    sound_control.composition_layers = {}
+                    sound_control.movements_completed = set()
+                    sound_control.active_effects = {m: False for m in ["left_hand", "right_hand", "left_leg", "right_leg"]}
                     # Clear static audio cache in get_movement_sounds
                     if hasattr(get_movement_sounds, 'audio_cache'):
                         for m in get_movement_sounds.audio_cache: