Add a new color logic for plotly (Needs some more adjustments later)

utils.py

@@ -40,50 +40,75 @@ TRANSPARENT_BG = dict(
 # ---------------------------------------------------------------------------
 
 # ---------------------------------------------------------------------------
-# Fixed 24-color palette: 6 hues × 4 saturation/brightness tiers.
-# Hue order: red, yellow, green, cyan, blue, magenta — the 6 perceptually
-# distinct primaries. Cycling through all 6 hues before repeating a tier
-# means no two adjacent speakers ever share a similar hue.
-# Tiers (OpenCV HSV sat, val):
-#   1 — vivid saturated        (255, 200)
-#   2 — medium bright          (180, 230)
-#   3 — deep dark              (255, 140)
-#   4 — pale tint              (100, 240)
-# The palette is interleaved hue-first so index 0=red-vivid, 1=yellow-vivid,
-# 2=green-vivid ... 6=red-medium, 7=yellow-medium, etc.
+# Color palette — loaded from plotly_colorwheel.txt at import time.
+# Edit that file to change colors; no code changes needed.
+# Structure: 8 colors × 3 shades (indices 0-23) + 1 grey (index 24).
+# Reserved indices:
+#   0  = Single Voice  (red shade 0)
+#   3  = Multi Voice   (green shade 0)
+#   24 = No Voice      (grey)
+# Speakers cycle through all other indices in order.
 # ---------------------------------------------------------------------------
-def _build_palette():
-    # OpenCV HSV hue is 0-179 (half-degrees)
-    hues_deg = [0, 30, 60, 90, 120, 150]   # red, yellow, green, cyan, blue, magenta
-    tiers    = [(255, 200), (180, 230), (255, 140), (100, 240)]
-    palette  = []
-    for sat, val in tiers:
-        for h_deg in hues_deg:
-            h_cv = h_deg  # already 0-179 range
-            hsv = np.uint8([[[h_cv, sat, val]]])
-            bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
-            b = f'{bgr[0][0][0].item():02x}'
-            g = f'{bgr[0][0][1].item():02x}'
-            r = f'{bgr[0][0][2].item():02x}'
-            palette.append('#' + b + g + r)
-    return palette   # 24 colors
-
-_PALETTE = _build_palette() + ["#999DA0"]   # index 24: grey for No Voice
 
+def _load_palette(path="plotly_colorwheel.txt"):
+    """Parse plotly_colorwheel.txt (id, color, shade, hex table) into a list.
 
-def colorsCSS(n, startingHue=None, pool=None):
-    """Return n CSS hex colors drawn from the fixed 24-color palette.
+    Returns a list indexed by id so _PALETTE[id] gives the hex color directly.
+    Falls back to hardcoded defaults if file is missing.
+    """
+    FALLBACK = [
+        "#de2d26","#fc9272","#fee0d2",  # red    0-2
+        "#e6550d","#fdae6b","#feedde",  # orange 3-5
+        "#d9b300","#fdd44d","#fff7bc",  # yellow 6-8
+        "#31a354","#a1d99b","#e5f5e0",  # green  9-11
+        "#1a9e9e","#66c2c2","#ccecec",  # teal   12-14
+        "#3182bd","#9ecae1","#deebf7",  # blue   15-17
+        "#756bb1","#bcbddc","#efedf5",  # purple 18-20
+        "#d4679a","#f1b6d1","#fce4f0",  # pink   21-23
+        "#999DA0",                       # grey   24
+    ]
+    try:
+        entries = {}
+        with open(path, "r") as f:
+            for line in f:
+                line = line.strip()
+                if not line or line.startswith("#"):
+                    continue
+                parts = [p.strip() for p in line.split(",")]
+                if len(parts) < 4:
+                    continue
+                try:
+                    idx = int(parts[0])
+                    hex_color = parts[3]
+                    if hex_color.startswith("#"):
+                        entries[idx] = hex_color
+                except ValueError:
+                    continue
+        if not entries:
+            return FALLBACK
+        max_idx = max(entries.keys())
+        palette = [entries.get(i, "#cccccc") for i in range(max_idx + 1)]
+        return palette
+    except FileNotFoundError:
+        return FALLBACK
+
+_PALETTE = _load_palette()
+# Reserved: 0 = Single Voice (red shade 0), 9 = Multi Voice (green shade 0),
+#           24 = No Voice (grey). Speakers use all other indices.
+_RESERVED = {0, 9, len(_PALETTE) - 1}
+_SPEAKER_PALETTE = [c for i, c in enumerate(_PALETTE) if i not in _RESERVED]
 
-    Colors are taken in palette order (red→yellow→green→cyan→blue→magenta,
-    then repeating across brightness tiers) so adjacent speakers always have
-    maximally different hues. Cycles if n > 24.
 
-    startingHue and pool are accepted for backwards compatibility but ignored —
-    the palette is fixed so colors are always distinct and deterministic.
+def colorsCSS(n, startingHue=None, pool=None):
+    """Return n CSS hex colors from the speaker palette.
+
+    Cycles if n > len(_SPEAKER_PALETTE).
+    startingHue and pool accepted for backwards compatibility but ignored.
     """
     if n == 0:
         return []
-    return [_PALETTE[i % len(_PALETTE)] for i in range(n)]
+    pal = _SPEAKER_PALETTE if _SPEAKER_PALETTE else _PALETTE
+    return [pal[i % len(pal)] for i in range(n)]
 
 
 def extract_clip_bytes(waveform, sample_rate, seg_start, seg_end):
@@ -324,7 +349,7 @@ def build_fig_pie2(df4, speakerNames, speakerColors, catColors, get_display_name
     fig = go.Figure()
     fig.update_layout(
         title_text="Percentage of Speakers per Role",
-        colorway=catColors + figColors,
+        colorway=_SPEAKER_PALETTE,
         **TRANSPARENT_BG,
     )
     fig.add_trace(go.Pie(values=df4["values"], labels=df4["names"], sort=False))
@@ -334,26 +359,20 @@ def build_fig_pie2(df4, speakerNames, speakerColors, catColors, get_display_name
 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.
+    Single Voice → _PALETTE[0]  (red shade 0, reserved)
+    Multi Voice  → _PALETTE[3]  (green shade 0, reserved)
+    No Voice     → _PALETTE[-1] (grey, reserved)
+    Speakers     → _SPEAKER_PALETTE in order (never collides with reserved)
     """
-    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
+        "Single Voice": _PALETTE[0],   # red shade 0
+        "Multi Voice":  _PALETTE[9],   # green shade 0
+        "No Voice":     _PALETTE[-1],  # 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)]]
+        color_map[lbl] = _SPEAKER_PALETTE[i % len(_SPEAKER_PALETTE)]
     return color_map
 
 
@@ -436,7 +455,7 @@ def build_fig_timeline(speakers_dataFrame, currTotalTime, speakerColors, get_dis
     fig = px.timeline(
         df, x_start="Start", x_end="Finish", y="Resource", color="Resource",
         title="Timeline of Audio with Speakers",
-        color_discrete_sequence=speakerColors,
+        color_discrete_sequence=_SPEAKER_PALETTE,
     )
     fig.update_yaxes(autorange="reversed")
 
@@ -486,7 +505,7 @@ def build_fig_bar(df2, speakerNames, catColors, speakerColors, get_display_name_
         df2, x="values", y="names", color="names", orientation="h",
         custom_data=["names", "time_label"],
         title="Time Spoken by each Speaker",
-        color_discrete_sequence=catColors + speakerColors,
+        color_discrete_sequence=_SPEAKER_PALETTE,
     )
     # Hide x-axis tick labels — values are crowded with many speakers.
     # The exact time is still visible on hover via the hovertemplate.