Restore MDL/fixed fitting toggle (generic labels, no basis families)

app.py

@@ -107,9 +107,11 @@ feature vectors, and a per-window anomaly score for free.</div>
 """.strip()
 
 
-def run(sample_name: str, csv_file, degree: int, segment_length: int):
+def run(sample_name: str, csv_file, fitting: str, degree: int, segment_length: int):
     signal = _load_signal(sample_name, csv_file)
-    r = demo_core.run_demo(signal, degree=int(degree), segment_length=int(segment_length))
+    r = demo_core.run_demo(
+        signal, basis_mode=fitting, degree=int(degree), segment_length=int(segment_length),
+    )
     return _summary(r), plots.fig_reconstruction(r), plots.fig_sigma(r), plots.fig_sizes(r)
 
 
@@ -123,6 +125,11 @@ def build_ui() -> gr.Blocks:
                     label="Sample signal",
                 )
                 csv = gr.File(label="...or upload a numeric CSV", file_types=[".csv"])
+                fitting = gr.Radio(
+                    choices=[("MDL adaptive selection", "auto"), ("Single fixed basis", "chebyshev")],
+                    value="auto",
+                    label="Fitting",
+                )
                 degree = gr.Slider(1, 10, value=5, step=1, label="Model detail")
                 seg = gr.Slider(100, 2000, value=500, step=100, label="Window length")
                 go = gr.Button("Tokenize", variant="primary")
@@ -132,7 +139,7 @@ def build_ui() -> gr.Blocks:
         with gr.Row():
             sigma = gr.Plot(label="Anomaly signal")
             sizes = gr.Plot(label="Stored size")
-        inputs = [sample, csv, degree, seg]
+        inputs = [sample, csv, fitting, degree, seg]
         outputs = [summary, recon, sigma, sizes]
         go.click(run, inputs, outputs)
         ui.load(run, inputs, outputs)