diff --git a/explorer.py b/explorer.py
index b4bf50c..056a1ae 100644
--- a/explorer.py
+++ b/explorer.py
@@ -1963,6 +1963,7 @@ def build_trajectories_tab(db_path: str, window_size: str) -> None:
 
     fig = go.Figure()
     trace_count = 0
+    helper_succeeded = False
     # New: delegate plotting selection to helper for testability
     # Note: select_trajectory_plot_data returns (fig, trace_count, banner_text)
     try:
@@ -1973,6 +1974,7 @@ def build_trajectories_tab(db_path: str, window_size: str) -> None:
         if fig2 is not None:
             fig = fig2
             trace_count = trace_count2
+            helper_succeeded = True
             if banner_text:
                 try:
                     st.caption(banner_text)
@@ -2002,38 +2004,40 @@ def build_trajectories_tab(db_path: str, window_size: str) -> None:
                 st.text_area("select_trajectory_plot_data traceback", tb, height=240)
             except Exception:
                 pass
-    for party in selected_parties:
-        if party not in centroids:
-            continue
-        wids_sorted = sorted(centroids[party].keys())
-        xs_raw = [centroids[party][w][0] for w in wids_sorted]
-        ys_raw = [centroids[party][w][1] for w in wids_sorted]
-        xs = _ema_smooth(xs_raw, smooth_alpha)
-        ys = _ema_smooth(ys_raw, smooth_alpha)
-        # Preserve raw (unsmoothed) values per-point so hover can show both raw and smoothed
-        custom_raw = [(float(rx), float(ry)) for rx, ry in zip(xs_raw, ys_raw)]
-        colour = PARTY_COLOURS.get(party, "#9E9E9E")
-        fig.add_trace(
-            go.Scatter(
-                x=xs,
-                y=ys,
-                mode="lines+markers",
-                name=party,
-                text=wids_sorted,  # full window ID for hover
-                customdata=custom_raw,
-                line=dict(color=colour, shape="spline", smoothing=1.3),
-                marker=dict(color=colour, size=8),
-                hovertemplate=(
-                    f"<b>{party}</b><br>"
-                    "venster: %{text}<br>"
-                    "x (smoothed): %{x:.3f}<br>"
-                    "x (raw): %{customdata[0]:.3f}<br>"
-                    "y (smoothed): %{y:.3f}<br>"
-                    "y (raw): %{customdata[1]:.3f}<extra></extra>"
-                ),
+    print(f"[TRAJ DEBUG] helper_succeeded={helper_succeeded}")
+    if not helper_succeeded:
+        for party in selected_parties:
+            if party not in centroids:
+                continue
+            wids_sorted = sorted(centroids[party].keys())
+            xs_raw = [centroids[party][w][0] for w in wids_sorted]
+            ys_raw = [centroids[party][w][1] for w in wids_sorted]
+            xs = _ema_smooth(xs_raw, smooth_alpha)
+            ys = _ema_smooth(ys_raw, smooth_alpha)
+            # Preserve raw (unsmoothed) values per-point so hover can show both raw and smoothed
+            custom_raw = [(float(rx), float(ry)) for rx, ry in zip(xs_raw, ys_raw)]
+            colour = PARTY_COLOURS.get(party, "#9E9E9E")
+            fig.add_trace(
+                go.Scatter(
+                    x=xs,
+                    y=ys,
+                    mode="lines+markers",
+                    name=party,
+                    text=wids_sorted,  # full window ID for hover
+                    customdata=custom_raw,
+                    line=dict(color=colour, shape="spline", smoothing=1.3),
+                    marker=dict(color=colour, size=8),
+                    hovertemplate=(
+                        f"<b>{party}</b><br>"
+                        "venster: %{text}<br>"
+                        "x (smoothed): %{x:.3f}<br>"
+                        "x (raw): %{customdata[0]:.3f}<br>"
+                        "y (smoothed): %{y:.3f}<br>"
+                        "y (raw): %{customdata[1]:.3f}<extra></extra>"
+                    ),
+                )
             )
-        )
-        trace_count += 1
+            trace_count += 1
 
     # For trajectories, the chart spans multiple windows. Use the classifier's
     # per-window confidences aggregated (mean) to decide whether to use the