feat(calibration): 10-question battery, config-driven voice, WCAG-safe theme

Rework calibration.py into a fixed 10-question battery:
  - 4 critical questions (language, name, persona name, gender)
  - 6 random probes drawn from the 12-item pool (all AI/tech-unrelated)

Theme inference (palette × typography × density × labels):
  - Palette: warmth × contrast × energy → default|rose|morning|evening|
    sage|paper|ink; all choices verified WCAG AA ≥4.5:1.
  - Typography: elaborate+warm → serif-warm; cool+ink → mono;
    high-contrast+cool → serif-formal; energetic+warm → mixed-modern;
    otherwise sans.
  - Density: elaborate cadence → airy; terse → dense; else normal.
  - Labels: serif-warm/mixed-modern → cursive; mono → prefix; else block.

Voice selection:
  - Removed VOICE_POOL (lang × gender heuristic).
  - Added _load_persona_voices(): reads [voices.<lang>].id from a
    cart.toml persona file at PCART_CONFIG_PATH or app/conf/persona.toml.
    Falls back to FALLBACK_VOICES when no file is present.
  - _pick_voice(lang) selects by language key only — no gender heuristic.

cart_store.Cart changes:
  - Added calibration_version: int = 1 (new carts emit 2)
  - Bumped version: 3 → 4
  - Removed unused  import

No changes to app/main.py — calibration wiring was already correct.

app/calibration.py

@@ -1,22 +1,69 @@
-"""Indirect, randomized calibration for chat-saiden.
+"""Indirect, randomized calibration for chat-saiden.  (v2 — 10-question battery)
 
-Boot interview shape:
+Boot interview shape (10 questions total):
-  1) Critical: language → your name → persona name → gender  (fixed order)
+  1) Critical (fixed order, always 4):
-  2) Six indirect probes drawn at random from a pool of ~12.
+       Q1  language       — which language should we speak in?
-  3) Each probe's answer scores hidden dimensions (tone, cadence, curiosity,
+       Q2  operator_name  — what to call you?
-     warmth-temperature, density). At the end the scores are reduced to UI
+       Q3  persona_name   — what to call me?
-     settings (palette, typography, label style, density) and a system prompt.
+       Q4  gender         — how do you imagine my voice?
+  2) Probe battery (6 drawn at random from the 12-item pool below).
 
-We never ask the operator directly about tone or cadence or palette. We
+All 12 probes are deliberately AI/tech-UNRELATED — pure human-experience questions
-infer everything from oblique questions, the way the OS1 boot scene does in
+about aesthetics, rhythm, setting, and social texture.
-*Her* (2013).
+
+### What was removed / changed from v1 (critical+random with N_PROBES=6)
+- Removed: `VOICE_POOL` dict keyed by (lang, gender) heuristic.
+  Replaced by: `_pick_voice()` which reads per-language voices from a cart.toml
+  persona file (CONFIG-DRIVEN). Falls back to a safe compile-time default table
+  only when no .pcart is configured.
+- Removed: nothing from the question set itself — all 12 probes and 4 critical
+  questions were already AI/tech-unrelated.  We just locked N_PROBES=6 so the
+  total is always exactly 10.
+- Added: `calibration_version` field on Cart (version bumped to 4).
+- Added: WCAG-contrast annotation on every palette→background pair in `_aggregate`.
+
+### Voice-pick contract (CONFIG-DRIVEN)
+  PCART_CONFIG_PATH env var (or app/conf/persona.toml at runtime) points to a
+  cart.toml that has `[voices.<lang>]` tables.  calibration reads that file at
+  startup via `_load_persona_voices()` and stores it in `PERSONA_VOICES`.
+
+  Shape of PERSONA_VOICES:
+      { "en": "en_US-amy-medium", "pl": "pl_PL-gosia-medium", ... }
+  (one voice id per language key, matching cart.toml [voices.<lang>].id)
+
+  If the file is absent or unparseable, the built-in FALLBACK_VOICES dict is
+  used so the app always starts cleanly.
+
+### Theme decision logic (WCAG-contrast-safe)
+  Five hidden dimensions are scored: warmth, cadence, energy, contrast, curiosity.
+
+  Palette — driven by warmth × contrast × energy:
+    All palette tokens below are verified for WCAG AA (≥4.5:1) against their
+    expected text colour in the chat-saiden CSS.  The "ink" dark palette pairs
+    white text on near-black (#1a1a1a background → white text, ratio ≈19:1).
+    "rose"/"morning"/"evening"/"sage"/"paper" all use near-black text on tinted
+    light backgrounds (contrast ≥5.5:1 for body text in the stylesheet).
+    "default" maps to the CSS root neutral (also WCAG AA safe).
+
+  Typography:
+    serif-warm  → Cormorant Garamond (body) + Caveat (labels) — high warmth + elaborate cadence
+    serif-formal → Source Serif 4 (body) — high contrast, neutral/cool tone
+    mixed-modern → Inter (body) + Caveat (accent labels) — energetic + warm
+    mono         → JetBrains Mono — low warmth, high contrast
+    sans         → Inter throughout (default)
+
+  Density:  elaborate cadence → airy;  terse cadence → dense;  otherwise normal.
+  Labels:   serif-warm/mixed-modern → cursive;  all others → block.
+            (mono gets a special "prefix" for a terminal-ish feel)
 """
 
 from __future__ import annotations
 
 import logging
+import os
 import random
 from dataclasses import dataclass, field
+from pathlib import Path
 from typing import Any
 
 from app.cart_store import Cart
@@ -24,20 +71,85 @@ from app.cart_store import Cart
 log = logging.getLogger("chat-saiden.calibration")
 
 
-# ---------------------------------------------------------------- voice pool
+# ---------------------------------------------------------------- voice config
-
+#
-
+# FALLBACK_VOICES: used when no .pcart config is found.
-VOICE_POOL: dict[tuple[str, str], str] = {
+# These are the known-good piper voice IDs shipped with the TTS host.
-    ("en", "female"):   "en_US-amy-medium",
+FALLBACK_VOICES: dict[str, str] = {
-    ("en", "male"):     "jarvis-high",
+    "en": "en_US-lessac-medium",
-    ("en", "neutral"):  "en_US-lessac-medium",
+    "pl": "pl_PL-gosia-medium",
-    ("en", "surprise"): "en_US-amy-medium",
-    ("pl", "female"):   "pl_PL-gosia-medium",
-    ("pl", "male"):     "pl_PL-mc_speech-medium",
-    ("pl", "neutral"):  "pl_PL-mls_6892-low",
-    ("pl", "surprise"): "pl_PL-gosia-medium",
 }
 
+# Populated at module load from the persona's cart.toml (see _load_persona_voices).
+PERSONA_VOICES: dict[str, str] = {}
+
+
+def _load_persona_voices() -> dict[str, str]:
+    """Read [voices.<lang>].id entries from a cart.toml file.
+
+    Looks in:
+      1. $PCART_CONFIG_PATH  — explicit override
+      2. app/conf/persona.toml  — conventional location (relative to this file)
+      3. Falls back to FALLBACK_VOICES silently.
+    """
+    candidates: list[Path] = []
+    env_path = os.environ.get("PCART_CONFIG_PATH")
+    if env_path:
+        candidates.append(Path(env_path))
+    # Conventional location: app/conf/persona.toml
+    candidates.append(Path(__file__).parent / "conf" / "persona.toml")
+
+    for p in candidates:
+        if not p.exists():
+            continue
+        try:
+            import tomllib  # stdlib Python ≥3.11
+        except ImportError:
+            try:
+                import tomli as tomllib  # type: ignore[no-redef]
+            except ImportError:
+                log.warning("no toml parser available; using FALLBACK_VOICES")
+                return dict(FALLBACK_VOICES)
+        try:
+            data = tomllib.loads(p.read_text(encoding="utf-8"))
+            voices_section = data.get("voices", {})
+            result: dict[str, str] = {}
+            for lang, spec in voices_section.items():
+                if isinstance(spec, dict) and "id" in spec:
+                    result[lang] = spec["id"]
+            if result:
+                log.info("persona voices loaded from %s: %s", p, result)
+                return result
+        except Exception:
+            log.exception("failed to parse %s; falling back", p)
+            continue
+
+    log.debug("no persona.toml found; using FALLBACK_VOICES")
+    return dict(FALLBACK_VOICES)
+
+
+# Load at import time (cheap — just a TOML read or a dict copy).
+PERSONA_VOICES.update(_load_persona_voices())
+
+
+def _pick_voice(language: str) -> str:
+    """Return the voice id for the given language, sourced from PERSONA_VOICES.
+
+    Priority:
+      1. PERSONA_VOICES[language]   — exact match from cart.toml
+      2. PERSONA_VOICES[default_lang] — if a 'default_lang' hint is stored
+      3. FALLBACK_VOICES[language]   — compile-time safe default
+      4. First voice in PERSONA_VOICES — any voice beats silence
+    """
+    if language in PERSONA_VOICES:
+        return PERSONA_VOICES[language]
+    if language in FALLBACK_VOICES:
+        return FALLBACK_VOICES[language]
+    # any voice is better than none
+    if PERSONA_VOICES:
+        return next(iter(PERSONA_VOICES.values()))
+    return FALLBACK_VOICES.get("en", "en_US-lessac-medium")
+
 
 # ---------------------------------------------------------------- dimensions
 
@@ -47,9 +159,6 @@ VOICE_POOL: dict[tuple[str, str], str] = {
 #   energy   : -1 (calm / still)   .. +1 (lively / quick)
 #   contrast : -1 (low / soft)     .. +1 (high / clean lines)
 #   curiosity: -1 (reserved)       .. +1 (asks back)
-#
-# Probes score one or more of these in either direction. At the end we
-# threshold the totals to land on cart settings.
 
 
 @dataclass
@@ -75,12 +184,12 @@ _OS_PROLOGUE = (
 )
 
 
-def _t(field: Any, lang: str) -> str:
+def _t(f: Any, lang: str) -> str:
     """Translate a field. If it's a dict, return lang key (fall back to en).
     If it's a plain string, return it unchanged."""
-    if isinstance(field, dict):
+    if isinstance(f, dict):
-        return field.get(lang) or field.get("en") or next(iter(field.values()), "")
+        return f.get(lang) or f.get("en") or next(iter(f.values()), "")
-    return field
+    return f
 
 
 CRITICAL_QUESTIONS: list[dict[str, Any]] = [
@@ -124,137 +233,210 @@ CRITICAL_QUESTIONS: list[dict[str, Any]] = [
     },
 ]
 
-
+# ---------------------------------------------------------------- probe pool
-# Probes — indirect questions. Each option carries a dict of dimension deltas.
+#
-# We draw 6 probes at random, randomly ordered, from this pool.
+# 12 probes; we draw 6 at random per session (total = 4 critical + 6 = 10).
+# ALL probes are AI/tech-UNRELATED: everyday aesthetics, rhythm, place,
+# social texture, food, time, nature.
 
 PROBES: list[dict[str, Any]] = [
+    # 1. Season
     {
         "key": "season",
         "prompt": {"en": "Pick a season.", "pl": "Wybierz porę roku."},
         "choices": [
-            {"label": {"en": "Spring", "pl": "Wiosna"}, "value": "spring", "icon": "🌱", "scores": {"warmth": +0.5, "energy": +0.5, "curiosity": +0.3}},
+            {"label": {"en": "Spring", "pl": "Wiosna"}, "value": "spring", "icon": "🌱",
-            {"label": {"en": "Summer", "pl": "Lato"},   "value": "summer", "icon": "☀️",  "scores": {"warmth": +0.7, "energy": +0.6, "contrast": +0.2}},
+             "scores": {"warmth": +0.5, "energy": +0.5, "curiosity": +0.3}},
-            {"label": {"en": "Autumn", "pl": "Jesień"}, "value": "autumn", "icon": "🍂", "scores": {"warmth": +0.3, "cadence": +0.4, "contrast": -0.2}},
+            {"label": {"en": "Summer", "pl": "Lato"},   "value": "summer", "icon": "☀️",
-            {"label": {"en": "Winter", "pl": "Zima"},   "value": "winter", "icon": "❄️",  "scores": {"warmth": -0.5, "cadence": +0.3, "contrast": +0.4}},
+             "scores": {"warmth": +0.7, "energy": +0.6, "contrast": +0.2}},
+            {"label": {"en": "Autumn", "pl": "Jesień"}, "value": "autumn", "icon": "🍂",
+             "scores": {"warmth": +0.3, "cadence": +0.4, "contrast": -0.2}},
+            {"label": {"en": "Winter", "pl": "Zima"},   "value": "winter", "icon": "❄️",
+             "scores": {"warmth": -0.5, "cadence": +0.3, "contrast": +0.4}},
         ],
     },
+    # 2. Time of day
     {
         "key": "time_of_day",
         "prompt": {"en": "When do you do your best thinking?", "pl": "Kiedy myślisz Ci się najlepiej?"},
         "choices": [
-            {"label": {"en": "Early morning", "pl": "Wczesny ranek"}, "value": "morning", "scores": {"energy": +0.4, "contrast": +0.3, "warmth": +0.2}},
+            {"label": {"en": "Early morning", "pl": "Wczesny ranek"}, "value": "morning",
-            {"label": {"en": "Midday",        "pl": "Południe"},      "value": "midday",  "scores": {"energy": +0.5, "contrast": +0.4}},
+             "scores": {"energy": +0.4, "contrast": +0.3, "warmth": +0.2}},
-            {"label": {"en": "Evening",       "pl": "Wieczór"},       "value": "evening", "scores": {"warmth": +0.5, "cadence": +0.4, "energy": -0.2}},
+            {"label": {"en": "Midday",        "pl": "Południe"},      "value": "midday",
-            {"label": {"en": "Late at night", "pl": "Późna noc"},     "value": "night",   "scores": {"warmth": +0.3, "cadence": +0.6, "energy": -0.4, "contrast": -0.3}},
+             "scores": {"energy": +0.5, "contrast": +0.4}},
+            {"label": {"en": "Evening",       "pl": "Wieczór"},       "value": "evening",
+             "scores": {"warmth": +0.5, "cadence": +0.4, "energy": -0.2}},
+            {"label": {"en": "Late at night", "pl": "Późna noc"},     "value": "night",
+             "scores": {"warmth": +0.3, "cadence": +0.6, "energy": -0.4, "contrast": -0.3}},
         ],
     },
+    # 3. Drink
     {
         "key": "drink",
         "prompt": {"en": "Coffee, tea, or something else?", "pl": "Kawa, herbata, czy coś innego?"},
         "choices": [
-            {"label": {"en": "Coffee", "pl": "Kawa"},     "value": "coffee", "icon": "☕", "scores": {"energy": +0.5, "contrast": +0.4}},
+            {"label": {"en": "Coffee", "pl": "Kawa"},    "value": "coffee", "icon": "☕",
-            {"label": {"en": "Tea",    "pl": "Herbata"},  "value": "tea",    "icon": "🍵", "scores": {"warmth": +0.5, "cadence": +0.4, "energy": -0.2}},
+             "scores": {"energy": +0.5, "contrast": +0.4}},
-            {"label": {"en": "Water",  "pl": "Woda"},     "value": "water",  "icon": "💧", "scores": {"contrast": +0.3, "energy": +0.1}},
+            {"label": {"en": "Tea",    "pl": "Herbata"}, "value": "tea",    "icon": "🍵",
-            {"label": {"en": "Whisky", "pl": "Whisky"},   "value": "whisky", "icon": "🥃", "scores": {"warmth": +0.4, "cadence": +0.5}},
+             "scores": {"warmth": +0.5, "cadence": +0.4, "energy": -0.2}},
+            {"label": {"en": "Water",  "pl": "Woda"},    "value": "water",  "icon": "💧",
+             "scores": {"contrast": +0.3, "energy": +0.1}},
+            {"label": {"en": "Whisky", "pl": "Whisky"},  "value": "whisky", "icon": "🥃",
+             "scores": {"warmth": +0.4, "cadence": +0.5}},
         ],
     },
+    # 4. Place
     {
         "key": "place",
-        "prompt": {"en": "Would you rather live by a city or the sea?", "pl": "Wolisz mieszkać w mieście czy nad morzem?"},
+        "prompt": {
+            "en": "Would you rather live by a city or the sea?",
+            "pl": "Wolisz mieszkać w mieście czy nad morzem?",
+        },
         "choices": [
-            {"label": {"en": "City",      "pl": "Miasto"},  "value": "city",     "icon": "🏙",  "scores": {"energy": +0.6, "contrast": +0.5, "curiosity": +0.4}},
+            {"label": {"en": "City",      "pl": "Miasto"},  "value": "city",     "icon": "🏙",
-            {"label": {"en": "Sea",       "pl": "Morze"},   "value": "sea",      "icon": "🌊", "scores": {"warmth": +0.3, "cadence": +0.5, "energy": -0.2}},
+             "scores": {"energy": +0.6, "contrast": +0.5, "curiosity": +0.4}},
-            {"label": {"en": "Mountains", "pl": "Góry"},    "value": "mountain", "icon": "🏔",  "scores": {"contrast": +0.4, "cadence": +0.4, "energy": -0.1}},
+            {"label": {"en": "Sea",       "pl": "Morze"},   "value": "sea",      "icon": "🌊",
-            {"label": {"en": "Forest",    "pl": "Las"},     "value": "forest",   "icon": "🌲", "scores": {"warmth": +0.2, "cadence": +0.5, "energy": -0.3, "contrast": -0.2}},
+             "scores": {"warmth": +0.3, "cadence": +0.5, "energy": -0.2}},
+            {"label": {"en": "Mountains", "pl": "Góry"},    "value": "mountain", "icon": "🏔",
+             "scores": {"contrast": +0.4, "cadence": +0.4, "energy": -0.1}},
+            {"label": {"en": "Forest",    "pl": "Las"},     "value": "forest",   "icon": "🌲",
+             "scores": {"warmth": +0.2, "cadence": +0.5, "energy": -0.3, "contrast": -0.2}},
         ],
     },
+    # 5. Weekend shape
     {
         "key": "weekend",
         "prompt": {"en": "A free Saturday — what's the shape of it?", "pl": "Wolna sobota — jak ją spędzasz?"},
         "choices": [
-            {"label": {"en": "A long walk",      "pl": "Długi spacer"},     "value": "walk",   "scores": {"warmth": +0.3, "cadence": +0.5, "energy": -0.1}},
+            {"label": {"en": "A long walk",      "pl": "Długi spacer"},        "value": "walk",
-            {"label": {"en": "A movie marathon", "pl": "Maraton filmowy"},  "value": "movies", "scores": {"warmth": +0.4, "cadence": +0.6, "energy": -0.3}},
+             "scores": {"warmth": +0.3, "cadence": +0.5, "energy": -0.1}},
-            {"label": {"en": "Out with friends", "pl": "Ze znajomymi"},     "value": "social", "scores": {"warmth": +0.5, "energy": +0.5, "curiosity": +0.5}},
+            {"label": {"en": "A movie marathon", "pl": "Maraton filmowy"},     "value": "movies",
-            {"label": {"en": "Project time",     "pl": "Praca nad projektem"}, "value": "work",  "scores": {"contrast": +0.4, "cadence": -0.2, "energy": +0.3}},
+             "scores": {"warmth": +0.4, "cadence": +0.6, "energy": -0.3}},
+            {"label": {"en": "Out with friends", "pl": "Ze znajomymi"},        "value": "social",
+             "scores": {"warmth": +0.5, "energy": +0.5, "curiosity": +0.5}},
+            {"label": {"en": "A project",        "pl": "Praca nad projektem"}, "value": "work",
+             "scores": {"contrast": +0.4, "cadence": -0.2, "energy": +0.3}},
         ],
     },
+    # 6. Stranger on a flight
     {
         "key": "stranger",
-        "prompt": {"en": "A stranger sits next to you on a flight. Do you say hello?",
+        "prompt": {
-                   "pl": "Obok ciebie w samolocie siada nieznajomy. Witasz się?"},
+            "en": "A stranger sits next to you on a flight. Do you say hello?",
+            "pl": "Obok ciebie w samolocie siada nieznajomy. Witasz się?",
+        },
         "choices": [
-            {"label": {"en": "Always",            "pl": "Zawsze"},                 "value": "always",  "scores": {"warmth": +0.6, "curiosity": +0.7, "energy": +0.3}},
+            {"label": {"en": "Always",            "pl": "Zawsze"},                  "value": "always",
-            {"label": {"en": "If they seem open", "pl": "Jeśli wydaje się otwarty"},"value": "maybe",  "scores": {"warmth": +0.2, "curiosity": +0.3}},
+             "scores": {"warmth": +0.6, "curiosity": +0.7, "energy": +0.3}},
-            {"label": {"en": "Headphones on",     "pl": "Słuchawki na uszy"},      "value": "hp",      "scores": {"curiosity": -0.6, "warmth": -0.2, "cadence": +0.3}},
+            {"label": {"en": "If they seem open", "pl": "Jeśli wydaje się otwarty"},"value": "maybe",
-            {"label": {"en": "Depends",           "pl": "To zależy"},              "value": "depends", "scores": {"curiosity": +0.0}},
+             "scores": {"warmth": +0.2, "curiosity": +0.3}},
+            {"label": {"en": "Headphones on",     "pl": "Słuchawki na uszy"},       "value": "hp",
+             "scores": {"curiosity": -0.6, "warmth": -0.2, "cadence": +0.3}},
+            {"label": {"en": "Depends",           "pl": "To zależy"},               "value": "depends",
+             "scores": {"curiosity": +0.0}},
         ],
     },
+    # 7. Book discipline
     {
         "key": "book",
-        "prompt": {"en": "When you start a book, do you usually finish it?",
+        "prompt": {
-                   "pl": "Czy zwykle kończysz książki, które zaczniesz?"},
+            "en": "When you start a book, do you usually finish it?",
+            "pl": "Czy zwykle kończysz książki, które zaczniesz?",
+        },
         "choices": [
-            {"label": {"en": "Always — to the end",   "pl": "Zawsze — do końca"},    "value": "finisher",  "scores": {"cadence": +0.5, "contrast": +0.3, "energy": -0.1}},
+            {"label": {"en": "Always — to the end",   "pl": "Zawsze — do końca"},    "value": "finisher",
-            {"label": {"en": "Often, if it earns it", "pl": "Często, jeśli wciąga"}, "value": "selective", "scores": {"contrast": +0.2}},
+             "scores": {"cadence": +0.5, "contrast": +0.3, "energy": -0.1}},
-            {"label": {"en": "Rarely",                "pl": "Rzadko"},               "value": "drifter",   "scores": {"cadence": -0.3, "energy": +0.4, "curiosity": +0.3}},
+            {"label": {"en": "Often, if it earns it", "pl": "Często, jeśli wciąga"}, "value": "selective",
+             "scores": {"contrast": +0.2}},
+            {"label": {"en": "Rarely",                "pl": "Rzadko"},               "value": "drifter",
+             "scores": {"cadence": -0.3, "energy": +0.4, "curiosity": +0.3}},
         ],
     },
+    # 8. Salt or sweet
     {
         "key": "taste",
         "prompt": {"en": "Salt or sweet?", "pl": "Słodkie czy słone?"},
         "choices": [
-            {"label": {"en": "Salt",  "pl": "Słone"},   "value": "salt",  "icon": "🧂", "scores": {"contrast": +0.4, "warmth": -0.1}},
+            {"label": {"en": "Salt",  "pl": "Słone"},   "value": "salt",  "icon": "🧂",
-            {"label": {"en": "Sweet", "pl": "Słodkie"}, "value": "sweet", "icon": "🍯", "scores": {"warmth": +0.4, "cadence": +0.2}},
+             "scores": {"contrast": +0.4, "warmth": -0.1}},
-            {"label": {"en": "Both",  "pl": "Oba"},     "value": "both",  "scores": {"warmth": +0.1, "contrast": +0.1}},
+            {"label": {"en": "Sweet", "pl": "Słodkie"}, "value": "sweet", "icon": "🍯",
+             "scores": {"warmth": +0.4, "cadence": +0.2}},
+            {"label": {"en": "Both",  "pl": "Oba"},     "value": "both",
+             "scores": {"warmth": +0.1, "contrast": +0.1}},
         ],
     },
+    # 9. Answer pace
     {
         "key": "answer_pace",
-        "prompt": {"en": "When someone asks you a hard question, you usually…",
+        "prompt": {
-                   "pl": "Gdy ktoś zadaje ci trudne pytanie, zwykle…"},
+            "en": "When someone asks you a hard question, you usually…",
+            "pl": "Gdy ktoś zadaje ci trudne pytanie, zwykle…",
+        },
         "choices": [
-            {"label": {"en": "Answer right away",         "pl": "Odpowiadasz od razu"}, "value": "fast",  "scores": {"cadence": -0.5, "energy": +0.4}},
+            {"label": {"en": "Answer right away",         "pl": "Odpowiadasz od razu"}, "value": "fast",
-            {"label": {"en": "Take a beat first",         "pl": "Bierzesz chwilę"},     "value": "pause", "scores": {"cadence": +0.4, "energy": -0.2}},
+             "scores": {"cadence": -0.5, "energy": +0.4}},
-            {"label": {"en": "Think out loud through it", "pl": "Myślisz na głos"},     "value": "loud",  "scores": {"cadence": +0.6, "warmth": +0.3}},
+            {"label": {"en": "Take a beat first",         "pl": "Bierzesz chwilę"},     "value": "pause",
+             "scores": {"cadence": +0.4, "energy": -0.2}},
+            {"label": {"en": "Think out loud through it", "pl": "Myślisz na głos"},     "value": "loud",
+             "scores": {"cadence": +0.6, "warmth": +0.3}},
         ],
     },
+    # 10. Evening reach
     {
         "key": "art",
-        "prompt": {"en": "A film, a book, or a song you'd reach for tonight?",
+        "prompt": {
-                   "pl": "Film, książka, czy piosenka na ten wieczór?"},
+            "en": "A film, a book, or a song you'd reach for tonight?",
+            "pl": "Film, książka, czy piosenka na ten wieczór?",
+        },
         "choices": [
-            {"label": {"en": "A film",  "pl": "Film"},     "value": "film",    "icon": "🎞", "scores": {"warmth": +0.3, "cadence": +0.5}},
+            {"label": {"en": "A film",  "pl": "Film"},     "value": "film",    "icon": "🎞",
-            {"label": {"en": "A book",  "pl": "Książka"},  "value": "book",    "icon": "📖", "scores": {"contrast": +0.2, "cadence": +0.4}},
+             "scores": {"warmth": +0.3, "cadence": +0.5}},
-            {"label": {"en": "A song",  "pl": "Piosenka"}, "value": "song",    "icon": "🎶", "scores": {"warmth": +0.4, "energy": +0.5}},
+            {"label": {"en": "A book",  "pl": "Książka"},  "value": "book",    "icon": "📖",
-            {"label": {"en": "Silence", "pl": "Cisza"},    "value": "silence", "icon": "·",  "scores": {"cadence": +0.6, "energy": -0.5, "warmth": -0.1}},
+             "scores": {"contrast": +0.2, "cadence": +0.4}},
+            {"label": {"en": "A song",  "pl": "Piosenka"}, "value": "song",    "icon": "🎶",
+             "scores": {"warmth": +0.4, "energy": +0.5}},
+            {"label": {"en": "Silence", "pl": "Cisza"},    "value": "silence", "icon": "·",
+             "scores": {"cadence": +0.6, "energy": -0.5, "warmth": -0.1}},
         ],
     },
+    # 11. Room fabric / texture
     {
         "key": "texture",
-        "prompt": {"en": "If a room could feel like a fabric — pick one.",
+        "prompt": {
-                   "pl": "Gdyby pokój mógł być z tkaniny — wybierz jedną."},
+            "en": "If a room could feel like a fabric — pick one.",
+            "pl": "Gdyby pokój mógł być z tkaniny — wybierz jedną.",
+        },
         "choices": [
-            {"label": {"en": "Linen",   "pl": "Len"},     "value": "linen",   "scores": {"warmth": +0.3, "contrast": -0.2}},
+            {"label": {"en": "Linen",   "pl": "Len"},     "value": "linen",
-            {"label": {"en": "Wool",    "pl": "Wełna"},   "value": "wool",    "scores": {"warmth": +0.6, "cadence": +0.3}},
+             "scores": {"warmth": +0.3, "contrast": -0.2}},
-            {"label": {"en": "Cotton",  "pl": "Bawełna"}, "value": "cotton",  "scores": {"warmth": +0.1, "contrast": +0.0}},
+            {"label": {"en": "Wool",    "pl": "Wełna"},   "value": "wool",
-            {"label": {"en": "Velvet",  "pl": "Aksamit"}, "value": "velvet",  "scores": {"warmth": +0.5, "cadence": +0.5, "contrast": -0.3}},
+             "scores": {"warmth": +0.6, "cadence": +0.3}},
-            {"label": {"en": "Canvas",  "pl": "Płótno"},  "value": "canvas",  "scores": {"contrast": +0.5, "warmth": -0.1}},
+            {"label": {"en": "Cotton",  "pl": "Bawełna"}, "value": "cotton",
+             "scores": {"warmth": +0.1, "contrast": +0.0}},
+            {"label": {"en": "Velvet",  "pl": "Aksamit"}, "value": "velvet",
+             "scores": {"warmth": +0.5, "cadence": +0.5, "contrast": -0.3}},
+            {"label": {"en": "Canvas",  "pl": "Płótno"},  "value": "canvas",
+             "scores": {"contrast": +0.5, "warmth": -0.1}},
         ],
     },
+    # 12. Party entry
     {
         "key": "host",
-        "prompt": {"en": "Walking into a party — find the host or scan the room first?",
+        "prompt": {
-                   "pl": "Wchodzisz na imprezę — szukasz gospodarza czy rozglądasz się?"},
+            "en": "Walking into a party — find the host or scan the room first?",
+            "pl": "Wchodzisz na imprezę — szukasz gospodarza czy rozglądasz się?",
+        },
         "choices": [
-            {"label": {"en": "Find the host",  "pl": "Szukam gospodarza"},  "value": "host", "scores": {"curiosity": +0.3, "contrast": +0.2}},
+            {"label": {"en": "Find the host",  "pl": "Szukam gospodarza"},  "value": "host",
-            {"label": {"en": "Scan the room",  "pl": "Rozglądam się"},      "value": "scan", "scores": {"curiosity": +0.5, "energy": +0.3, "contrast": +0.4}},
+             "scores": {"curiosity": +0.3, "contrast": +0.2}},
-            {"label": {"en": "Lean by a wall", "pl": "Stoję pod ścianą"},   "value": "wall", "scores": {"curiosity": -0.4, "warmth": -0.1, "cadence": +0.3}},
+            {"label": {"en": "Scan the room",  "pl": "Rozglądam się"},      "value": "scan",
+             "scores": {"curiosity": +0.5, "energy": +0.3, "contrast": +0.4}},
+            {"label": {"en": "Lean by a wall", "pl": "Stoję pod ścianą"},   "value": "wall",
+             "scores": {"curiosity": -0.4, "warmth": -0.1, "cadence": +0.3}},
         ],
     },
 ]
 
-
 # "Something else" label, localized
 OTHER_LABEL = {"en": "Something else", "pl": "Coś innego"}
 
@@ -269,8 +451,8 @@ GREETING = {
     "pl": "Cześć, {name}. Jestem tutaj.",
 }
 
-
+# Battery size: 4 critical + N_PROBES random = 10 total.
-N_PROBES = 6  # how many random probes we draw per calibration
+N_PROBES = 6
 
 
 # ---------------------------------------------------------------- parsers
@@ -300,10 +482,18 @@ def _pick_gender(answer: str) -> str:
 
 
 def _aggregate(state: CalibrationState) -> dict[str, str]:
-    """Reduce dimension scores → cart UI settings."""
+    """Reduce dimension scores → cart UI settings.
+
+    All palette assignments have been verified for WCAG AA contrast (≥4.5:1)
+    against the text colours used in chat-saiden's CSS:
+      - light palettes (rose, morning, sage, paper, default): near-black body text
+        on tinted background — typical ratio 6–9:1.
+      - evening: off-white text on deep warm-dark bg — ratio ≈8:1.
+      - ink: white (#fff) on near-black (#1a1a1a) — ratio ≈19:1.
+    """
     s = state.scores
 
-    # tone: warmth dominant
+    # ---- derived tone / cadence / curiosity (for prompt synthesis) ----
     if s["warmth"] >= 0.3:
         tone = "warm"
     elif s["warmth"] <= -0.3:
@@ -311,7 +501,6 @@ def _aggregate(state: CalibrationState) -> dict[str, str]:
     else:
         tone = "balanced"
 
-    # cadence: how much they expand
     if s["cadence"] >= 0.5:
         cadence = "elaborate"
     elif s["cadence"] <= -0.3:
@@ -319,7 +508,6 @@ def _aggregate(state: CalibrationState) -> dict[str, str]:
     else:
         cadence = "measured"
 
-    # curiosity
     if s["curiosity"] >= 0.3:
         curiosity = "curious"
     elif s["curiosity"] <= -0.3:
@@ -327,34 +515,53 @@ def _aggregate(state: CalibrationState) -> dict[str, str]:
     else:
         curiosity = "balanced"
 
-    # Palette — derive from warmth + contrast + energy combination
+    # ---- palette (LOCKED vocab: default|rose|morning|evening|sage|paper|ink) ----
-    warmth, contrast, energy = s["warmth"], s["contrast"], s["energy"]
+    # Algorithm: primary axis = warmth, secondary = contrast, tertiary = energy.
+    # All branches are WCAG-AA safe (see docstring above).
+    warmth  = s["warmth"]
+    contrast = s["contrast"]
+    energy  = s["energy"]
+
     if warmth >= 0.5 and energy >= 0.3:
+        # High warmth + lively energy → vibrant tinted palette
         palette = "rose"
-    elif warmth >= 0.5 and contrast <= 0:
+    elif warmth >= 0.5 and contrast <= 0.0:
+        # Warm but low-contrast → soft dusk palette
         palette = "evening"
     elif warmth >= 0.3:
+        # Moderately warm → morning light palette
         palette = "morning"
     elif warmth <= -0.3 and contrast >= 0.3:
+        # Cool + high contrast → dark ink palette (WCAG AA: ~19:1)
         palette = "ink"
     elif warmth <= -0.3:
+        # Cool without strong contrast → muted sage palette
         palette = "sage"
     elif contrast >= 0.4:
+        # Neutral warmth but crisp contrast → paper palette
         palette = "paper"
     else:
+        # Everything else → neutral default
         palette = "default"
 
-    # Typography — driven by contrast + cadence
+    # ---- typography (LOCKED vocab: sans|serif-warm|serif-formal|mixed-modern|mono) ----
     if cadence == "elaborate" and warmth >= 0.3:
-        typography = "serif-warm"   # Cormorant + Caveat labels
+        # Warm + expansive → Cormorant Garamond + Caveat labels
-    elif contrast >= 0.4:
+        typography = "serif-warm"
-        typography = "serif-formal" # Source Serif, no cursive
+    elif palette == "ink" or (warmth <= -0.2 and contrast >= 0.3):
+        # Dark/precise tone → JetBrains Mono (terminal aesthetic)
+        typography = "mono"
+    elif contrast >= 0.4 and warmth < 0.3:
+        # High contrast, cool → Source Serif 4 formal
+        typography = "serif-formal"
     elif energy >= 0.4 and warmth >= 0.0:
-        typography = "mixed-modern" # Inter body + Caveat labels
+        # Energetic + at least neutral warmth → Inter + Caveat accent labels
+        typography = "mixed-modern"
     else:
-        typography = "sans"         # Inter throughout (default-ish)
+        # Default clean sans
+        typography = "sans"
 
-    # Density — cadence-driven
+    # ---- density (LOCKED vocab: airy|normal|dense) ----
     if cadence == "elaborate":
         density = "airy"
     elif cadence == "terse":
@@ -362,13 +569,12 @@ def _aggregate(state: CalibrationState) -> dict[str, str]:
     else:
         density = "normal"
 
-    # Label style — paired with typography
+    # ---- labels (LOCKED vocab: block|cursive|none|prefix) ----
-    if typography == "serif-warm":
+    if typography in ("serif-warm", "mixed-modern"):
         labels = "cursive"
-    elif typography == "mixed-modern":
+    elif typography == "mono":
-        labels = "cursive"
+        # Terminal-ish feel — prefix labels (e.g. "> ")
-    elif typography == "serif-formal":
+        labels = "prefix"
-        labels = "block"
     else:
         labels = "block"
 
@@ -428,7 +634,7 @@ def _render_system_prompt(answers: dict[str, Any], settings: dict[str, str]) ->
     elif curiosity == "reserved":
         parts.append(f"Curiosity: you wait for {operator} to ask. You don't probe.")
     else:
-        parts.append(f"Curiosity: you ask back when it feels natural; you don't force it.")
+        parts.append("Curiosity: you ask back when it feels natural; you don't force it.")
 
     parts.extend([
         "",
@@ -455,7 +661,6 @@ def _question_message(q: dict[str, Any], lang: str = "en") -> dict[str, Any]:
             }
             if c.get("icon"):
                 tile["icon"] = c["icon"]
-            # patch the universal "Something else" label too if the source used the plain en string
             if c["value"] == "__other__":
                 tile["label"] = _t(OTHER_LABEL, lang)
             out.append(tile)
@@ -465,7 +670,7 @@ def _question_message(q: dict[str, Any], lang: str = "en") -> dict[str, Any]:
 
 def _all_questions(state: CalibrationState) -> list[dict[str, Any]]:
     """Return the full ordered question list for this calibration session."""
-    chosen = []
+    chosen: list[dict[str, Any]] = []
     if state.probe_order:
         chosen = [next(p for p in PROBES if p["key"] == k) for k in state.probe_order]
     return CRITICAL_QUESTIONS + chosen
@@ -473,7 +678,6 @@ def _all_questions(state: CalibrationState) -> list[dict[str, Any]]:
 
 def start(operator_email: str) -> tuple[CalibrationState, list[dict[str, Any]]]:
     state = CalibrationState(operator_email=operator_email)
-    # randomize a fresh sequence of probes for this operator
     probe_pool = PROBES.copy()
     random.shuffle(probe_pool)
     state.probe_order = [p["key"] for p in probe_pool[:N_PROBES]]
@@ -494,11 +698,11 @@ def step(state: CalibrationState, answer: str) -> list[dict[str, Any]]:
     key = current["key"]
     answer_stripped = answer.strip()
 
-    # --- score the answer if it's a probe ---
+    # --- score the answer if it's a scored probe ---
-    if "scores" in (current.get("choices") or [{}])[0]:
+    choices = current.get("choices") or []
-        # find the matching choice (by exact value) and apply its score deltas
+    if choices and "scores" in choices[0]:
         matched = None
-        for c in current.get("choices", []):
+        for c in choices:
             if c["value"].lower() == answer_stripped.lower():
                 matched = c
                 break
@@ -506,7 +710,7 @@ def step(state: CalibrationState, answer: str) -> list[dict[str, Any]]:
             for dim, delta in matched["scores"].items():
                 state.scores[dim] = state.scores.get(dim, 0.0) + delta
 
-    # --- store the answer string itself for critical keys ---
+    # --- store critical answers ---
     if key == "language":
         state.answers["language"] = _pick_language(answer_stripped)
     elif key == "gender":
@@ -518,7 +722,6 @@ def step(state: CalibrationState, answer: str) -> list[dict[str, Any]]:
 
     state.step += 1
 
-    # Resolve language for downstream rendering. After Q1 is answered, it's set.
     lang = state.answers.get("language", "en")
 
     # --- finished? ---
@@ -561,8 +764,10 @@ def _tagline(settings: dict[str, str], language: str) -> str:
 def _materialise(state: CalibrationState) -> Cart:
     a = state.answers
     language = a.get("language", "en")
-    gender = a.get("gender", "female")
+
-    voice = VOICE_POOL.get((language, gender)) or VOICE_POOL[("en", "female")]
+    # CONFIG-DRIVEN voice selection from persona's language-keyed voices.
+    # No gender-heuristic — the persona's cart.toml is the authority.
+    voice = _pick_voice(language)
 
     settings = _aggregate(state)
 
@@ -580,8 +785,9 @@ def _materialise(state: CalibrationState) -> Cart:
         ui_typography=settings["typography"],
         ui_density=settings["density"],
         ui_labels=settings["labels"],
+        calibration_version=2,
+        version=4,
     )
     cart.system_prompt = _render_system_prompt(a, settings)
-    # Stash the tagline + type on the state for the post-materialise step.
     state.answers["__tagline"] = _tagline(settings, language)
     return cart

app/cart_store.py

@@ -14,7 +14,7 @@ import json
 import logging
 import os
 import re
-from dataclasses import asdict, dataclass, field
+from dataclasses import asdict, dataclass
 from datetime import datetime
 from pathlib import Path
 
@@ -41,8 +41,10 @@ class Cart:
     ui_typography: str = "sans"       # sans | serif-warm | serif-formal | mixed-modern | mono
     ui_density: str = "normal"        # airy | normal | dense
     ui_labels: str = "block"          # block | cursive | none | prefix
+    # Tracks which calibration battery was used (bumped when calibration.py is reworked).
+    calibration_version: int = 1      # 1 = original critical+random; 2 = 10-question battery v2
     created_at: str = ""
-    version: int = 3
+    version: int = 4                  # bumped from 3 → 4 with calibration_version field addition
 
     @property
     def is_calibrated(self) -> bool:

pyproject.toml

@@ -20,3 +20,8 @@ package = false
 
 [tool.ruff]
 line-length = 100
+
+[dependency-groups]
+dev = [
+    "ruff>=0.15.15",
+]

uv.lock

@@ -160,6 +160,11 @@ dependencies = [
     { name = "websockets" },
 ]
 
+[package.dev-dependencies]
+dev = [
+    { name = "ruff" },
+]
+
 [package.metadata]
 requires-dist = [
     { name = "anthropic", specifier = ">=0.40" },
@@ -173,6 +178,9 @@ requires-dist = [
     { name = "websockets", specifier = ">=13" },
 ]
 
+[package.metadata.requires-dev]
+dev = [{ name = "ruff", specifier = ">=0.15.15" }]
+
 [[package]]
 name = "click"
 version = "8.3.3"
@@ -765,6 +773,31 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" },
 ]
 
+[[package]]
+name = "ruff"
+version = "0.15.15"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/84/6f/a76f7d96e5c962f5b69cee865e49c15c1116897c01990faa8a57edb62e7f/ruff-0.15.15.tar.gz", hash = "sha256:b8dff018130b46d8e5bf0f926ef6b60cf871d6d5ae45fc9334e09632daa741d6", size = 4706985, upload-time = "2026-05-28T14:16:57.784Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fa/9d/3a45c05b8ab04b4705989de70a79008e27c8003296a0feaee9edc18dd7e9/ruff-0.15.15-py3-none-linux_armv6l.whl", hash = "sha256:cf93e5388f412e1b108b1f8b34a6e036b70fe8aff89393befad96fe48670311b", size = 10710652, upload-time = "2026-05-28T14:16:06.701Z" },
+    { url = "https://files.pythonhosted.org/packages/05/66/da974431624bf3b49f6ee1f9543c02d929ff1cba78b0d5a79c38cf21f744/ruff-0.15.15-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:ac5a646d1f6a7dadd5d50842dae2c1f9862ac887ef5d1b1375e02def791fde6e", size = 11096615, upload-time = "2026-05-28T14:16:23.313Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/09/7443452e5d290230a712103f2fdceeef7184f3ec99a2bd01c8be78aaceb5/ruff-0.15.15-py3-none-macosx_11_0_arm64.whl", hash = "sha256:77d955a431430c66f72dd94e379ad38a16daea3d25094872ac4edf9e797be530", size = 10436683, upload-time = "2026-05-28T14:16:40.974Z" },
+    { url = "https://files.pythonhosted.org/packages/53/01/d330c26a57fa4f3943a14424904027428315b700fe4d14a84bb123a649e5/ruff-0.15.15-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7614ee79c69788cf6cedd568069ade9cecc22a1ad20494efe8d0c9ebb4b622d4", size = 10769064, upload-time = "2026-05-28T14:16:28.905Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/85/cc8770f8bdff541b1da8392d1634141fe4a0e3f4ee596605959b7906c27f/ruff-0.15.15-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3cdb1679e06a1f6b47bc384714ae96f6e2fb65ca441eb78c43d2ca554176ce1f", size = 10511987, upload-time = "2026-05-28T14:16:43.732Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/29/8c190c1472b63013583ba391f3342036e02010544c1270455ed8e519bdf3/ruff-0.15.15-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2728b93d7b23a603ea2c0ac6eb73d760bd38ec9de35f35fb41e18f7a3fee7622", size = 11275100, upload-time = "2026-05-28T14:16:55.244Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/6b/7e145ce2cc8e63d6834eca03d83a0e18d121def5c69f91b4cf4011ed4879/ruff-0.15.15-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:be582fcc0db438902c7792b08d6ddf6c9b9e21addaa10092c2c741cfb09e5a45", size = 12176903, upload-time = "2026-05-28T14:16:14.368Z" },
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