smalle updates

app.py

@@ -194,17 +194,18 @@ def load_pitch_model(model_selection):
         qt_path = os.path.join(pitch_path, 'qt.joblib') if model_type == 'diffusion' else None, \
         device = 'cpu'
     )
-    return pitch_model, pitch_qt, pitch_task_fn, invert_pitch_fn
+    return pitch_model, pitch_qt, pitch_task_fn, invert_pitch_fn, model_type
 
 @debug_profile
 def container_generate(model_selection, task_selection, audio, singer_id, t0):
     global pitch_model, pitch_qt, pitch_task_fn, invert_pitch_fn, model_loaded
     # load pitch model
     if model_loaded is None or model_loaded != model_selection:
-        pitch_model, pitch_qt, pitch_task_fn, invert_pitch_fn = load_pitch_model(model_selection)
+        pitch_model, pitch_qt, pitch_task_fn, invert_pitch_fn, model_type = load_pitch_model(model_selection)
         model_loaded = model_selection
     else:
         logging.log(logging.INFO, f'using existing model: {model_selection}')
+        model_type = pitch_paths[model_selection][0]
     # extract pitch from input
     if audio is None:
         return None, None
@@ -247,9 +248,9 @@ def container_generate(model_selection, task_selection, audio, singer_id, t0):
     elif singer_id == 'Singer 2':
         singer = [27]
     if task_selection == 'Call and Response':
-        partial_generate = partial(generate, num_samples=1, num_steps=100, singers=singer, outfolder=None, pitch_qt=pitch_qt, type='response', invert_pitch_fn=invert_pitch_fn, model_type=model_selection) 
+        partial_generate = partial(generate, num_samples=1, num_steps=100, singers=singer, outfolder=None, pitch_qt=pitch_qt, type='response', invert_pitch_fn=invert_pitch_fn, model_type=model_type) 
     else:
-        partial_generate = partial(generate, num_samples=1, num_steps=100, singers=singer, outfolder=None, pitch_qt=pitch_qt, type='reinterp', invert_pitch_fn=invert_pitch_fn, t0=t0, model_type=model_selection) 
+        partial_generate = partial(generate, num_samples=1, num_steps=100, singers=singer, outfolder=None, pitch_qt=pitch_qt, type='reinterp', invert_pitch_fn=invert_pitch_fn, t0=t0, model_type=model_type) 
     audio, output_plot = partial_generate(f0)
     return audio, user_input_plot, output_plot
 
@@ -270,8 +271,8 @@ def toggle_visibility(selection):
         return gr.update(visible=False)
     
 def toggle_options(selection, options = ['Call and Response', 'Melodic Reinterpretation']):
-    # Show element if selection is "Show", otherwise hide it
-    if selection == "Melodic Reinterpretation":
+    
+    if selection == "Diffusion Pitch Generator":
         return gr.update(choices=options)
     else:
         return gr.update(choices=options[:-1])
@@ -308,7 +309,7 @@ with gr.Blocks(css=css) as demo:
             """)
     model_dropdown = gr.Dropdown(["Diffusion Pitch Generator", "Autoregressive Pitch Generator"], label="Select a model type")
     task_dropdown = gr.Dropdown(label="Select a task", choices=["Call and Response", "Melodic Reinterpretation"])
-    model_dropdown.change(toggle_options, outputs=task_dropdown)
+    model_dropdown.change(toggle_options, inputs=model_dropdown, outputs=task_dropdown)
     t0 = gr.Slider(label="Faithfulness to the input (For melodic reinterpretation task only)", minimum=0.0, maximum=1.0, step=0.01, value=0.3, visible=False)
     task_dropdown.change(toggle_visibility, inputs=task_dropdown, outputs=t0)
     singer_dropdown = gr.Dropdown(label="Select a singer", choices=["Singer 1", "Singer 2"])