Switched to checkbox control and added in commented out code for future demo mode.

app.py

@@ -17,9 +17,10 @@ model = TimbreTrap(sample_rate=22050,
 model.eval()
 
 model_path_orig = os.path.join('models', 'tt-orig.pt')
+#model_path_demo = os.path.join('models', 'tt-demo.pt')
 
 tt_weights_orig = torch.load(model_path_orig, map_location='cpu')
-model.load_state_dict(tt_weights_orig)
+#tt_weights_demo = torch.load(model_path_demo, map_location='cpu')
 
 model_card = ModelCard(
     name='Timbre-Trap',
@@ -29,7 +30,7 @@ model_card = ModelCard(
 )
 
 
-def process_fn(audio_path, transcribe):
+def process_fn(audio_path, transcribe):#, demo):
     # Load the audio with torchaudio
     audio, fs = torchaudio.load(audio_path)
     # Average channels to obtain mono-channel
@@ -41,6 +42,15 @@ def process_fn(audio_path, transcribe):
     # Determine original number of samples
     n_samples = audio.size(-1)
 
+    """
+    if demo:
+        # Load weights of the demo version
+        model.load_state_dict(tt_weights_demo)
+    else:
+    """
+    # Load weights of the original model
+    model.load_state_dict(tt_weights_orig)
+
     # Obtain transcription or reconstructed spectral coefficients
     coefficients = model.chunked_inference(audio, transcribe)
 
@@ -73,25 +83,10 @@ def process_fn(audio_path, transcribe):
 # Build Gradio endpoint
 with gr.Blocks() as demo:
     components = [
-        #gr.Checkbox(
-        #    value=False,
-        #    label='De-Timbre'
-        #),
-        gr.Slider(
-            minimum=0,
-            maximum=1,
-            step=1,
-            value=0,
-            label='De-Timbre'
-        ),
-        #gr.Number(
-        #    value=0,
-        #    label='De-Timbre'
-        #),
-        #gr.Textbox(
-        #    value='text',
-        #    label='De-Timbre'
-        #)
+        gr.Checkbox(
+            value=False,
+            label='Remove Timbre'
+        )
     ]
 
     app = build_endpoint(model_card=model_card,