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app.py

@@ -270,8 +270,20 @@ def live_retrieve(seed_track_id, concepts, *alphas):
             *_slider_components(concepts, alphas),
         )
     pairs = _active_pairs(concepts, alphas)
+    active_pairs = [(c, a) for c, a in pairs if abs(float(a)) >= 1e-6]
+    if not active_pairs:
+        return (
+            _status_html(concepts, alphas, prefix="Showing nearest neighbours for the seed."),
+            results_html(_baseline_results(seed_track_id)),
+            *_slider_components(concepts, alphas),
+        )
     eng = ensure_engine()
-    results = eng.multi_steer_and_retrieve(seed_track_id, pairs, k=K)
+    missing = [concept for concept, _ in active_pairs if not eng.has_slider(concept)]
+    if missing:
+        name = missing[0]
+        note = _status_html(concepts, alphas, prefix=f"Preparing slider for {name}; click Make slider if it is not ready.")
+        return note, results_html(_baseline_results(seed_track_id)), *_slider_components(concepts, alphas)
+    results = eng.multi_steer_and_retrieve(seed_track_id, active_pairs, k=K)
     return _status_html(concepts, alphas), results_html(results), *_slider_components(concepts, alphas)
 
 

core.py

@@ -88,9 +88,11 @@ class DemoEngine:
             text_encoder.device = target
         self._text_encoder_device = target
 
-    def _slider(self, concept: str) -> Slider:
+    def _slider(self, concept: str, *, create: bool = True) -> Slider:
         key = self._slider_key(concept)
         if key not in self._slider_cache:
+            if not create:
+                raise KeyError(f"Slider for concept {concept!r} has not been created yet.")
             target = self.text_device if str(self.text_device).startswith("cuda") and torch.cuda.is_available() else "cpu"
             self._set_text_encoder_device(target)
             try:
@@ -141,7 +143,7 @@ class DemoEngine:
             alpha = float(alpha)
             if not concept or abs(alpha) < 1e-6:
                 continue
-            slider_masks.append((self._slider(concept).mask, alpha))
+            slider_masks.append((self._slider(concept, create=False).mask, alpha))
 
         with torch.inference_mode():
             _, sparse, _, _ = self.model.inference(z)