update color pt3

utils.py

@@ -68,7 +68,7 @@ def _build_palette():
             palette.append('#' + b + g + r)
     return palette   # 24 colors
 
-_PALETTE = _build_palette()
+_PALETTE = _build_palette() + ["#999DA0"]   # index 24: grey for No Voice
 
 
 def colorsCSS(n, startingHue=None, pool=None):
@@ -331,21 +331,39 @@ def build_fig_pie2(df4, speakerNames, speakerColors, catColors, get_display_name
     return fig
 
 
+def _voice_color_map(df5_labels, speakerColors):
+    """Build the label->color map for sunburst/treemap charts.
+
+    Single Voice  → palette index 0 (always)
+    Multi Voice   → palette index 4 (always)
+    No Voice      → palette index 8 (neutral, always)
+    Speakers      → cycle through every index EXCEPT 0 and 4 so they never
+                    blend into their parent category layer.
+    """
+    reserved = {0, 4}
+    speaker_indices = [i for i in range(24) if i not in reserved]  # exclude grey index
+
+    top_labels = ["No Voice", "Single Voice", "Multi Voice"]
+    color_map = {
+        "Single Voice": _PALETTE[0],
+        "Multi Voice":  _PALETTE[4],
+        "No Voice":     _PALETTE[24],  # grey
+    }
+    speaker_labels = [l for l in df5_labels if l not in top_labels]
+    for i, lbl in enumerate(speaker_labels):
+        color_map[lbl] = speaker_indices[i % len(speaker_indices)]
+        # speaker_indices contains palette indices; convert to hex
+        color_map[lbl] = _PALETTE[speaker_indices[i % len(speaker_indices)]]
+    return color_map
+
+
 def build_fig_sunburst(df5, catTypeColors, speakerColors, get_display_name_fn, currFile):
     """Sunburst voice-category chart."""
     df5 = df5.copy()
     df5["labels"]      = df5["labels"].apply(lambda s: get_display_name_fn(s, currFile))
     df5["parentNames"] = df5["parentNames"].apply(lambda s: get_display_name_fn(s, currFile))
 
-    # Build an explicit label->color map so every node gets a guaranteed color
-    # regardless of encounter order. color_discrete_sequence is position-based
-    # and can silently drop nodes when label count exceeds sequence length.
-    top_labels = ["No Voice", "Single Voice", "Multi Voice"]
-    speaker_labels = [l for l in df5["labels"] if l not in top_labels]
-    color_map = {lbl: catTypeColors[i % len(catTypeColors)]
-                 for i, lbl in enumerate(top_labels)}
-    for i, lbl in enumerate(speaker_labels):
-        color_map[lbl] = speakerColors[i % len(speakerColors)]
+    color_map = _voice_color_map(df5["labels"], speakerColors)
 
     fig = px.sunburst(
         df5,
@@ -374,14 +392,7 @@ def build_fig_treemap(df5, catTypeColors, speakerColors, get_display_name_fn, cu
     df5["labels"]      = df5["labels"].apply(lambda s: get_display_name_fn(s, currFile))
     df5["parentNames"] = df5["parentNames"].apply(lambda s: get_display_name_fn(s, currFile))
 
-    # Same explicit color map as sunburst — avoids silent node drops from
-    # position-based color_discrete_sequence running out of colors.
-    top_labels = ["No Voice", "Single Voice", "Multi Voice"]
-    speaker_labels = [l for l in df5["labels"] if l not in top_labels]
-    color_map = {lbl: catTypeColors[i % len(catTypeColors)]
-                 for i, lbl in enumerate(top_labels)}
-    for i, lbl in enumerate(speaker_labels):
-        color_map[lbl] = speakerColors[i % len(speakerColors)]
+    color_map = _voice_color_map(df5["labels"], speakerColors)
 
     fig = px.treemap(
         df5,