File size: 3,431 Bytes
893d6fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | """Speaker chip palette, styler, and grouped-row time mapping for transcript DataFrame."""
import re
import pandas as pd
SPEAKER_PALETTE = [
"#FFD23F", # yellow
"#FF6B6B", # coral
"#5CE1E6", # cyan
"#A4F47A", # lime
"#FF6FC0", # magenta
"#FF9F1C", # orange
]
def _extract_index(label: str) -> int:
"""Pull a stable integer index out of a speaker label.
SPEAKER_00 → 0, SPK-1 → 1, "Alice" → hash-fallback so renames keep deterministic colors
only when caller passes original label; once renamed without a digit, fall back to hash.
"""
if not label:
return 0
m = re.search(r"(\d+)", str(label))
if m:
return int(m.group(1))
return abs(hash(str(label))) % len(SPEAKER_PALETTE)
def color_for_speaker(label: str) -> str:
return SPEAKER_PALETTE[_extract_index(label) % len(SPEAKER_PALETTE)]
def render_chip(label: str) -> str:
color = color_for_speaker(label)
return f'<span class="brut-chip" style="background:{color}">▣ {label}</span>'
def style_transcript(df: pd.DataFrame):
"""Return a Styler that paints the Speaker column cell background per palette."""
if df is None or df.empty or "Speaker" not in df.columns:
return df
def _color(value):
c = color_for_speaker(value)
return f"background-color: {c}; font-family: 'Archivo Black', sans-serif;"
try:
return df.style.applymap(_color, subset=["Speaker"])
except AttributeError:
# pandas >= 2.1 deprecates applymap → use map
return df.style.map(_color, subset=["Speaker"])
def grouped_row_start_seconds(merged: list, row_idx: int) -> float:
"""Walk merged segments, group consecutive same-speaker turns, return start seconds of group at row_idx."""
if not merged or row_idx is None or row_idx < 0:
return 0.0
group_idx = -1
prev_speaker = None
for seg in merged:
spk = seg.get("speaker")
if spk != prev_speaker:
group_idx += 1
if group_idx == row_idx:
return float(seg.get("start", 0.0))
prev_speaker = spk
return 0.0
def build_label_map(edited_df, merged: list) -> dict:
"""From edited DataFrame Speaker column + original merged segments, build {orig_label → new_label}.
Walks grouped segments in order, mapping each group's original speaker label to the
corresponding row's edited Speaker value.
"""
if edited_df is None or merged is None:
return {}
if hasattr(edited_df, "values"):
rows = edited_df.values.tolist() if isinstance(edited_df, pd.DataFrame) else edited_df
if isinstance(edited_df, pd.DataFrame) and "Speaker" in edited_df.columns:
new_labels = edited_df["Speaker"].tolist()
else:
new_labels = [r[2] for r in rows if len(r) > 2]
elif isinstance(edited_df, list):
new_labels = [r[2] for r in edited_df if len(r) > 2]
else:
return {}
label_map: dict = {}
group_idx = -1
prev_speaker = None
for seg in merged:
spk = seg.get("speaker")
if spk != prev_speaker:
group_idx += 1
if group_idx < len(new_labels):
new_label = str(new_labels[group_idx]).strip()
if new_label and new_label != spk:
label_map[spk] = new_label
prev_speaker = spk
return label_map
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