| """Plain-language vocabulary for the Executive Dynamics workspace/landing/company |
| tabs — the "analyst workspace" side of the app. Everything imports from here |
| instead of hardcoding strings, so "no ML jargon in the product surface" is enforced |
| structurally (one file to review) rather than by convention. |
| |
| The Research side (Benchmarks/Methodology tabs) keeps its existing technical |
| language and is NOT governed by this module. |
| """ |
|
|
| from theme import SIG_NEGATIVE, SIG_NEUTRAL, SIG_POSITIVE |
|
|
| DIMENSION_LABELS = { |
| "confidence": "Confidence", |
| "stress": "Stress", |
| "specificity": "Specificity", |
| "alignment": "Alignment", |
| "alignment_drift": "Alignment", |
| } |
|
|
| |
| |
| RISK_TIER_DISPLAY = {"low": "Low", "medium": "Elevated", "high": "High"} |
| RISK_TIER_COLORS = {"low": SIG_POSITIVE, "medium": SIG_NEUTRAL, "high": SIG_NEGATIVE} |
|
|
|
|
| def risk_tier_label(risk_tier: str | None) -> str: |
| return RISK_TIER_DISPLAY.get(risk_tier, "Unknown") |
|
|
|
|
| def risk_tier_color(risk_tier: str | None) -> str: |
| return RISK_TIER_COLORS.get(risk_tier, SIG_NEUTRAL) |
|
|
|
|
| def largest_friction_label(dimension: str | None) -> str: |
| return DIMENSION_LABELS.get(dimension, "—") |
|
|
|
|
| def company_name(ticker: str, ticker_names: dict[str, str]) -> str: |
| """Full company name for a ticker, falling back to the raw ticker if unmapped.""" |
| return ticker_names.get(ticker, ticker) |
|
|
|
|
| def company_dropdown_choices(tickers: list[str], ticker_names: dict[str, str]) -> list[tuple[str, str]]: |
| """(label, value) pairs for gr.Dropdown — label is 'Company Name (TICKER)', value stays |
| the raw ticker so filtering/lookup logic downstream is unchanged. Sorted by company name.""" |
| pairs = [(f"{company_name(t, ticker_names)} ({t})", t) for t in tickers] |
| return sorted(pairs, key=lambda p: p[0]) |
|
|
|
|
| def risk_driver_bullets(turn: dict) -> list[str]: |
| """Plain-language driver bullets for the Evidence panel. |
| |
| Prefers the exported `risk_drivers` field (already human-readable strings from |
| src/evaluation/behavioral_signals.py). Falls back to templating from the turn's |
| own scores if that field is missing/empty, so the Evidence panel still shows |
| something sensible before/without a fresh data export. |
| """ |
| drivers = turn.get("risk_drivers") |
| if drivers: |
| return list(drivers) |
|
|
| fallback = [] |
| if turn.get("stress", 0) > 0.6: |
| fallback.append("elevated vocal/language stress") |
| if (turn.get("alignment_drift") or 0) > 0.6: |
| fallback.append("tone diverged from prepared remarks") |
| if turn.get("specificity", 1.0) < 0.3: |
| fallback.append("low specificity") |
| return fallback |
|
|
