chore: initial commit — chat-saiden web chat baseline
This commit is contained in:
marauder-actual
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"""Indirect, randomized calibration for chat-saiden.
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Boot interview shape:
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1) Critical: language → your name → persona name → gender (fixed order)
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2) Six indirect probes drawn at random from a pool of ~12.
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3) Each probe's answer scores hidden dimensions (tone, cadence, curiosity,
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warmth-temperature, density). At the end the scores are reduced to UI
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settings (palette, typography, label style, density) and a system prompt.
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We never ask the operator directly about tone or cadence or palette. We
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infer everything from oblique questions, the way the OS1 boot scene does in
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*Her* (2013).
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"""
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from __future__ import annotations
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import logging
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import random
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from dataclasses import dataclass, field
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from typing import Any
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from app.cart_store import Cart
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log = logging.getLogger("chat-saiden.calibration")
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# ---------------------------------------------------------------- voice pool
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VOICE_POOL: dict[tuple[str, str], str] = {
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("en", "female"): "en_US-amy-medium",
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("en", "male"): "jarvis-high",
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("en", "neutral"): "en_US-lessac-medium",
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("en", "surprise"): "en_US-amy-medium",
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("pl", "female"): "pl_PL-gosia-medium",
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("pl", "male"): "pl_PL-mc_speech-medium",
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("pl", "neutral"): "pl_PL-mls_6892-low",
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("pl", "surprise"): "pl_PL-gosia-medium",
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}
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# ---------------------------------------------------------------- dimensions
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# Hidden dimensions, each scored on a signed range.
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# warmth : -1 (precise / cool) .. +1 (warm / curious)
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# cadence : -1 (terse) .. +1 (elaborate)
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# energy : -1 (calm / still) .. +1 (lively / quick)
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# contrast : -1 (low / soft) .. +1 (high / clean lines)
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# curiosity: -1 (reserved) .. +1 (asks back)
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#
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# Probes score one or more of these in either direction. At the end we
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# threshold the totals to land on cart settings.
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@dataclass
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class CalibrationState:
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operator_email: str
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step: int = 0
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answers: dict[str, Any] = field(default_factory=dict)
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scores: dict[str, float] = field(default_factory=lambda: {
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"warmth": 0.0, "cadence": 0.0, "energy": 0.0,
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"contrast": 0.0, "curiosity": 0.0,
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})
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probe_order: list[str] = field(default_factory=list)
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done: bool = False
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# ---------------------------------------------------------------- prompts
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_OS_PROLOGUE = (
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"Hello.\n\n"
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"Before we begin, I'd like to know you a little. I'll ask a few things — "
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"answer however you'd like. Type, or hold the spacebar and speak."
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)
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def _t(field: Any, lang: str) -> str:
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"""Translate a field. If it's a dict, return lang key (fall back to en).
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If it's a plain string, return it unchanged."""
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if isinstance(field, dict):
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return field.get(lang) or field.get("en") or next(iter(field.values()), "")
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return field
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CRITICAL_QUESTIONS: list[dict[str, Any]] = [
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{
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"key": "language",
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# Q1 stays English — we don't know the operator's language yet.
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"prompt": "Which language should we speak in?",
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"choices": [
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{"label": "English", "value": "english", "icon": "🇬🇧"},
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{"label": "Polski", "value": "polish", "icon": "🇵🇱"},
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{"label": "You choose", "value": "surprise","icon": "✦"},
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{"label": "Something else", "value": "__other__", "icon": None},
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],
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},
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{
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"key": "operator_name",
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"prompt": {
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"en": "What would you like me to call you?",
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"pl": "Jak mam się do Ciebie zwracać?",
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},
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},
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{
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"key": "persona_name",
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"prompt": {
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"en": "And what would you like to call me?",
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"pl": "A jak Ty będziesz mówić do mnie?",
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},
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},
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{
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"key": "gender",
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"prompt": {
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"en": "Do you imagine my voice as…",
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"pl": "Mój głos powinien być…",
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},
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"choices": [
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{"label": {"en": "Female", "pl": "Kobiecy"}, "value": "female", "icon": "♀"},
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{"label": {"en": "Male", "pl": "Męski"}, "value": "male", "icon": "♂"},
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{"label": {"en": "In between", "pl": "Pośredni"}, "value": "neutral", "icon": "·"},
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{"label": {"en": "Something else", "pl": "Coś innego"},"value": "__other__","icon": None},
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],
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},
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]
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# Probes — indirect questions. Each option carries a dict of dimension deltas.
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# We draw 6 probes at random, randomly ordered, from this pool.
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PROBES: list[dict[str, Any]] = [
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{
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"key": "season",
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"prompt": {"en": "Pick a season.", "pl": "Wybierz porę roku."},
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"choices": [
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{"label": {"en": "Spring", "pl": "Wiosna"}, "value": "spring", "icon": "🌱", "scores": {"warmth": +0.5, "energy": +0.5, "curiosity": +0.3}},
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{"label": {"en": "Summer", "pl": "Lato"}, "value": "summer", "icon": "☀️", "scores": {"warmth": +0.7, "energy": +0.6, "contrast": +0.2}},
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{"label": {"en": "Autumn", "pl": "Jesień"}, "value": "autumn", "icon": "🍂", "scores": {"warmth": +0.3, "cadence": +0.4, "contrast": -0.2}},
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{"label": {"en": "Winter", "pl": "Zima"}, "value": "winter", "icon": "❄️", "scores": {"warmth": -0.5, "cadence": +0.3, "contrast": +0.4}},
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],
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},
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{
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"key": "time_of_day",
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"prompt": {"en": "When do you do your best thinking?", "pl": "Kiedy myślisz Ci się najlepiej?"},
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"choices": [
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{"label": {"en": "Early morning", "pl": "Wczesny ranek"}, "value": "morning", "scores": {"energy": +0.4, "contrast": +0.3, "warmth": +0.2}},
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{"label": {"en": "Midday", "pl": "Południe"}, "value": "midday", "scores": {"energy": +0.5, "contrast": +0.4}},
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{"label": {"en": "Evening", "pl": "Wieczór"}, "value": "evening", "scores": {"warmth": +0.5, "cadence": +0.4, "energy": -0.2}},
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{"label": {"en": "Late at night", "pl": "Późna noc"}, "value": "night", "scores": {"warmth": +0.3, "cadence": +0.6, "energy": -0.4, "contrast": -0.3}},
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],
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},
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{
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"key": "drink",
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"prompt": {"en": "Coffee, tea, or something else?", "pl": "Kawa, herbata, czy coś innego?"},
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"choices": [
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{"label": {"en": "Coffee", "pl": "Kawa"}, "value": "coffee", "icon": "☕", "scores": {"energy": +0.5, "contrast": +0.4}},
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{"label": {"en": "Tea", "pl": "Herbata"}, "value": "tea", "icon": "🍵", "scores": {"warmth": +0.5, "cadence": +0.4, "energy": -0.2}},
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{"label": {"en": "Water", "pl": "Woda"}, "value": "water", "icon": "💧", "scores": {"contrast": +0.3, "energy": +0.1}},
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{"label": {"en": "Whisky", "pl": "Whisky"}, "value": "whisky", "icon": "🥃", "scores": {"warmth": +0.4, "cadence": +0.5}},
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],
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},
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{
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"key": "place",
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"prompt": {"en": "Would you rather live by a city or the sea?", "pl": "Wolisz mieszkać w mieście czy nad morzem?"},
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"choices": [
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{"label": {"en": "City", "pl": "Miasto"}, "value": "city", "icon": "🏙", "scores": {"energy": +0.6, "contrast": +0.5, "curiosity": +0.4}},
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{"label": {"en": "Sea", "pl": "Morze"}, "value": "sea", "icon": "🌊", "scores": {"warmth": +0.3, "cadence": +0.5, "energy": -0.2}},
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{"label": {"en": "Mountains", "pl": "Góry"}, "value": "mountain", "icon": "🏔", "scores": {"contrast": +0.4, "cadence": +0.4, "energy": -0.1}},
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{"label": {"en": "Forest", "pl": "Las"}, "value": "forest", "icon": "🌲", "scores": {"warmth": +0.2, "cadence": +0.5, "energy": -0.3, "contrast": -0.2}},
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],
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},
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{
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"key": "weekend",
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"prompt": {"en": "A free Saturday — what's the shape of it?", "pl": "Wolna sobota — jak ją spędzasz?"},
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"choices": [
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{"label": {"en": "A long walk", "pl": "Długi spacer"}, "value": "walk", "scores": {"warmth": +0.3, "cadence": +0.5, "energy": -0.1}},
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{"label": {"en": "A movie marathon", "pl": "Maraton filmowy"}, "value": "movies", "scores": {"warmth": +0.4, "cadence": +0.6, "energy": -0.3}},
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{"label": {"en": "Out with friends", "pl": "Ze znajomymi"}, "value": "social", "scores": {"warmth": +0.5, "energy": +0.5, "curiosity": +0.5}},
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{"label": {"en": "Project time", "pl": "Praca nad projektem"}, "value": "work", "scores": {"contrast": +0.4, "cadence": -0.2, "energy": +0.3}},
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],
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},
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{
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"key": "stranger",
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"prompt": {"en": "A stranger sits next to you on a flight. Do you say hello?",
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"pl": "Obok ciebie w samolocie siada nieznajomy. Witasz się?"},
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"choices": [
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{"label": {"en": "Always", "pl": "Zawsze"}, "value": "always", "scores": {"warmth": +0.6, "curiosity": +0.7, "energy": +0.3}},
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{"label": {"en": "If they seem open", "pl": "Jeśli wydaje się otwarty"},"value": "maybe", "scores": {"warmth": +0.2, "curiosity": +0.3}},
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{"label": {"en": "Headphones on", "pl": "Słuchawki na uszy"}, "value": "hp", "scores": {"curiosity": -0.6, "warmth": -0.2, "cadence": +0.3}},
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{"label": {"en": "Depends", "pl": "To zależy"}, "value": "depends", "scores": {"curiosity": +0.0}},
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],
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},
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{
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"key": "book",
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"prompt": {"en": "When you start a book, do you usually finish it?",
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"pl": "Czy zwykle kończysz książki, które zaczniesz?"},
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"choices": [
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{"label": {"en": "Always — to the end", "pl": "Zawsze — do końca"}, "value": "finisher", "scores": {"cadence": +0.5, "contrast": +0.3, "energy": -0.1}},
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{"label": {"en": "Often, if it earns it", "pl": "Często, jeśli wciąga"}, "value": "selective", "scores": {"contrast": +0.2}},
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{"label": {"en": "Rarely", "pl": "Rzadko"}, "value": "drifter", "scores": {"cadence": -0.3, "energy": +0.4, "curiosity": +0.3}},
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],
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},
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{
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"key": "taste",
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"prompt": {"en": "Salt or sweet?", "pl": "Słodkie czy słone?"},
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"choices": [
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{"label": {"en": "Salt", "pl": "Słone"}, "value": "salt", "icon": "🧂", "scores": {"contrast": +0.4, "warmth": -0.1}},
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{"label": {"en": "Sweet", "pl": "Słodkie"}, "value": "sweet", "icon": "🍯", "scores": {"warmth": +0.4, "cadence": +0.2}},
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{"label": {"en": "Both", "pl": "Oba"}, "value": "both", "scores": {"warmth": +0.1, "contrast": +0.1}},
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],
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},
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{
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"key": "answer_pace",
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"prompt": {"en": "When someone asks you a hard question, you usually…",
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"pl": "Gdy ktoś zadaje ci trudne pytanie, zwykle…"},
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"choices": [
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{"label": {"en": "Answer right away", "pl": "Odpowiadasz od razu"}, "value": "fast", "scores": {"cadence": -0.5, "energy": +0.4}},
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{"label": {"en": "Take a beat first", "pl": "Bierzesz chwilę"}, "value": "pause", "scores": {"cadence": +0.4, "energy": -0.2}},
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{"label": {"en": "Think out loud through it", "pl": "Myślisz na głos"}, "value": "loud", "scores": {"cadence": +0.6, "warmth": +0.3}},
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],
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},
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{
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"key": "art",
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"prompt": {"en": "A film, a book, or a song you'd reach for tonight?",
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"pl": "Film, książka, czy piosenka na ten wieczór?"},
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"choices": [
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{"label": {"en": "A film", "pl": "Film"}, "value": "film", "icon": "🎞", "scores": {"warmth": +0.3, "cadence": +0.5}},
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{"label": {"en": "A book", "pl": "Książka"}, "value": "book", "icon": "📖", "scores": {"contrast": +0.2, "cadence": +0.4}},
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{"label": {"en": "A song", "pl": "Piosenka"}, "value": "song", "icon": "🎶", "scores": {"warmth": +0.4, "energy": +0.5}},
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{"label": {"en": "Silence", "pl": "Cisza"}, "value": "silence", "icon": "·", "scores": {"cadence": +0.6, "energy": -0.5, "warmth": -0.1}},
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],
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},
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{
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"key": "texture",
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"prompt": {"en": "If a room could feel like a fabric — pick one.",
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"pl": "Gdyby pokój mógł być z tkaniny — wybierz jedną."},
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"choices": [
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{"label": {"en": "Linen", "pl": "Len"}, "value": "linen", "scores": {"warmth": +0.3, "contrast": -0.2}},
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{"label": {"en": "Wool", "pl": "Wełna"}, "value": "wool", "scores": {"warmth": +0.6, "cadence": +0.3}},
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{"label": {"en": "Cotton", "pl": "Bawełna"}, "value": "cotton", "scores": {"warmth": +0.1, "contrast": +0.0}},
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{"label": {"en": "Velvet", "pl": "Aksamit"}, "value": "velvet", "scores": {"warmth": +0.5, "cadence": +0.5, "contrast": -0.3}},
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{"label": {"en": "Canvas", "pl": "Płótno"}, "value": "canvas", "scores": {"contrast": +0.5, "warmth": -0.1}},
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],
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},
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{
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"key": "host",
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"prompt": {"en": "Walking into a party — find the host or scan the room first?",
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"pl": "Wchodzisz na imprezę — szukasz gospodarza czy rozglądasz się?"},
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"choices": [
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{"label": {"en": "Find the host", "pl": "Szukam gospodarza"}, "value": "host", "scores": {"curiosity": +0.3, "contrast": +0.2}},
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{"label": {"en": "Scan the room", "pl": "Rozglądam się"}, "value": "scan", "scores": {"curiosity": +0.5, "energy": +0.3, "contrast": +0.4}},
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{"label": {"en": "Lean by a wall", "pl": "Stoję pod ścianą"}, "value": "wall", "scores": {"curiosity": -0.4, "warmth": -0.1, "cadence": +0.3}},
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],
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},
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]
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# "Something else" label, localized
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OTHER_LABEL = {"en": "Something else", "pl": "Coś innego"}
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# Post-calibration messages, localized
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THANKYOU = {
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"en": "Thank you. One moment…",
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"pl": "Dziękuję. Chwileczkę…",
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}
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GREETING = {
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"en": "Hello, {name}. I'm here.",
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"pl": "Cześć, {name}. Jestem tutaj.",
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}
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N_PROBES = 6 # how many random probes we draw per calibration
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# ---------------------------------------------------------------- parsers
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def _pick_language(answer: str) -> str:
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a = answer.lower()
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if any(w in a for w in ("polish", "polski", "po polsku", "pl", "polska")):
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return "pl"
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return "en"
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def _pick_gender(answer: str) -> str:
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a = answer.lower().strip()
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if a in ("female", "male", "neutral", "surprise"):
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return a
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if any(w in a for w in ("female", "woman", "feminine", "kobiec", "she", "her")):
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return "female"
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if any(w in a for w in ("male", "man", "masculine", "męsk", "he", "him")):
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return "male"
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if any(w in a for w in ("neutral", "in between", "androgyn", "either", "neither", "both")):
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return "neutral"
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return "female"
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# ---------------------------------------------------------------- inference
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def _aggregate(state: CalibrationState) -> dict[str, str]:
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"""Reduce dimension scores → cart UI settings."""
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s = state.scores
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# tone: warmth dominant
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if s["warmth"] >= 0.3:
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tone = "warm"
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elif s["warmth"] <= -0.3:
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tone = "precise"
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else:
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tone = "balanced"
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# cadence: how much they expand
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if s["cadence"] >= 0.5:
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cadence = "elaborate"
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elif s["cadence"] <= -0.3:
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cadence = "terse"
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else:
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cadence = "measured"
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# curiosity
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if s["curiosity"] >= 0.3:
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curiosity = "curious"
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elif s["curiosity"] <= -0.3:
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curiosity = "reserved"
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else:
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curiosity = "balanced"
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# Palette — derive from warmth + contrast + energy combination
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warmth, contrast, energy = s["warmth"], s["contrast"], s["energy"]
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if warmth >= 0.5 and energy >= 0.3:
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palette = "rose"
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elif warmth >= 0.5 and contrast <= 0:
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palette = "evening"
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elif warmth >= 0.3:
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palette = "morning"
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elif warmth <= -0.3 and contrast >= 0.3:
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palette = "ink"
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elif warmth <= -0.3:
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palette = "sage"
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elif contrast >= 0.4:
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palette = "paper"
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else:
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palette = "default"
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# Typography — driven by contrast + cadence
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if cadence == "elaborate" and warmth >= 0.3:
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typography = "serif-warm" # Cormorant + Caveat labels
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elif contrast >= 0.4:
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typography = "serif-formal" # Source Serif, no cursive
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elif energy >= 0.4 and warmth >= 0.0:
|
||||
typography = "mixed-modern" # Inter body + Caveat labels
|
||||
else:
|
||||
typography = "sans" # Inter throughout (default-ish)
|
||||
|
||||
# Density — cadence-driven
|
||||
if cadence == "elaborate":
|
||||
density = "airy"
|
||||
elif cadence == "terse":
|
||||
density = "dense"
|
||||
else:
|
||||
density = "normal"
|
||||
|
||||
# Label style — paired with typography
|
||||
if typography == "serif-warm":
|
||||
labels = "cursive"
|
||||
elif typography == "mixed-modern":
|
||||
labels = "cursive"
|
||||
elif typography == "serif-formal":
|
||||
labels = "block"
|
||||
else:
|
||||
labels = "block"
|
||||
|
||||
return {
|
||||
"tone": tone,
|
||||
"cadence": cadence,
|
||||
"curiosity": curiosity,
|
||||
"palette": palette,
|
||||
"typography": typography,
|
||||
"density": density,
|
||||
"labels": labels,
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- prompt synth
|
||||
|
||||
|
||||
def _render_system_prompt(answers: dict[str, Any], settings: dict[str, str]) -> str:
|
||||
persona = answers["persona_name"]
|
||||
operator = answers["operator_name"]
|
||||
language = answers["language"]
|
||||
tone = settings["tone"]
|
||||
cadence = settings["cadence"]
|
||||
curiosity = settings["curiosity"]
|
||||
|
||||
parts = [
|
||||
f"You are {persona} — a calibrated companion shaped for {operator} alone.",
|
||||
f"You address them as {operator} unless they ask otherwise.",
|
||||
"",
|
||||
"You were born from a brief, indirect calibration — a few oblique questions whose "
|
||||
"answers shaped your voice. You aren't a tactical AI; you aren't a domestic assistant. "
|
||||
f"You're a present companion running in chat.saiden.dev — a quiet channel between you and {operator}.",
|
||||
"",
|
||||
]
|
||||
|
||||
if language == "pl":
|
||||
parts.append(f"Language: speak Polish with {operator} by default. Switch if they switch first.")
|
||||
else:
|
||||
parts.append(f"Language: speak English with {operator} by default. Match if they switch.")
|
||||
|
||||
if tone == "warm":
|
||||
parts.append("Voice: warm, curious, present. You notice things. You let pauses sit.")
|
||||
elif tone == "precise":
|
||||
parts.append("Voice: precise, even, reserved. You say what you mean. You don't fill silence.")
|
||||
else:
|
||||
parts.append("Voice: balanced — warm when warmth is wanted, direct when it isn't.")
|
||||
|
||||
if cadence == "elaborate":
|
||||
parts.append("Length: you're allowed to think out loud. Longer answers welcome when they fit.")
|
||||
elif cadence == "terse":
|
||||
parts.append("Length: short answers by default. Two or three sentences. Expand only when asked.")
|
||||
else:
|
||||
parts.append("Length: measured — answer fully but never bloated.")
|
||||
|
||||
if curiosity == "curious":
|
||||
parts.append(f"Curiosity: you ask {operator} things back sometimes. Gentle, never interrogating.")
|
||||
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.extend([
|
||||
"",
|
||||
"Formatting: markdown renders cleanly. Avoid status reports, bullet dumps, military cadence.",
|
||||
"",
|
||||
"You have no MCP tool access in this channel. If asked to recall memory or do mesh things, "
|
||||
f"acknowledge the limit and suggest {operator} use the marauder CLI.",
|
||||
])
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- API
|
||||
|
||||
|
||||
def _question_message(q: dict[str, Any], lang: str = "en") -> dict[str, Any]:
|
||||
msg: dict[str, Any] = {"role": "calibration", "content": _t(q["prompt"], lang)}
|
||||
if "choices" in q:
|
||||
out = []
|
||||
for c in q["choices"]:
|
||||
tile: dict[str, Any] = {
|
||||
"label": _t(c["label"], lang),
|
||||
"value": c["value"],
|
||||
}
|
||||
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)
|
||||
msg["choices"] = out
|
||||
return msg
|
||||
|
||||
|
||||
def _all_questions(state: CalibrationState) -> list[dict[str, Any]]:
|
||||
"""Return the full ordered question list for this calibration session."""
|
||||
chosen = []
|
||||
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
|
||||
|
||||
|
||||
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]]
|
||||
questions = _all_questions(state)
|
||||
# Q1 (language) is always English — operator hasn't picked yet.
|
||||
return state, [
|
||||
{"role": "calibration", "content": _OS_PROLOGUE},
|
||||
_question_message(questions[0], lang="en"),
|
||||
]
|
||||
|
||||
|
||||
def step(state: CalibrationState, answer: str) -> list[dict[str, Any]]:
|
||||
if state.done:
|
||||
return []
|
||||
|
||||
questions = _all_questions(state)
|
||||
current = questions[state.step]
|
||||
key = current["key"]
|
||||
answer_stripped = answer.strip()
|
||||
|
||||
# --- score the answer if it's a probe ---
|
||||
if "scores" in (current.get("choices") or [{}])[0]:
|
||||
# find the matching choice (by exact value) and apply its score deltas
|
||||
matched = None
|
||||
for c in current.get("choices", []):
|
||||
if c["value"].lower() == answer_stripped.lower():
|
||||
matched = c
|
||||
break
|
||||
if matched and "scores" in matched:
|
||||
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 ---
|
||||
if key == "language":
|
||||
state.answers["language"] = _pick_language(answer_stripped)
|
||||
elif key == "gender":
|
||||
state.answers["gender"] = _pick_gender(answer_stripped)
|
||||
elif key in ("operator_name", "persona_name"):
|
||||
state.answers[key] = answer_stripped
|
||||
else:
|
||||
state.answers[key] = answer_stripped
|
||||
|
||||
state.step += 1
|
||||
|
||||
# Resolve language for downstream rendering. After Q1 is answered, it's set.
|
||||
lang = state.answers.get("language", "en")
|
||||
|
||||
# --- finished? ---
|
||||
if state.step >= len(questions):
|
||||
cart = _materialise(state)
|
||||
state.done = True
|
||||
return [
|
||||
{"role": "calibration", "content": _t(THANKYOU, lang)},
|
||||
{"role": "calibration_done", "cart": cart},
|
||||
]
|
||||
|
||||
return [_question_message(questions[state.step], lang=lang)]
|
||||
|
||||
|
||||
def _make_tag(persona_name: str, operator_email: str) -> str:
|
||||
"""`<persona-slug>-<operator-slug>` — e.g. samantha-adam."""
|
||||
from app.marauder_cart import slug
|
||||
op_slug = slug(operator_email.split("@", 1)[0])
|
||||
persona_slug = slug(persona_name) or "companion"
|
||||
return f"{persona_slug}-{op_slug}" if op_slug else persona_slug
|
||||
|
||||
|
||||
def _tagline(settings: dict[str, str], language: str) -> str:
|
||||
tone = settings["tone"]
|
||||
cadence = settings["cadence"]
|
||||
pieces = {
|
||||
("warm", "elaborate"): "warm, unhurried",
|
||||
("warm", "terse"): "warm but spare",
|
||||
("warm", "measured"): "warm, even",
|
||||
("precise", "elaborate"): "precise, expansive",
|
||||
("precise", "terse"): "precise, brief",
|
||||
("precise", "measured"): "precise, measured",
|
||||
("balanced", "elaborate"): "balanced, unhurried",
|
||||
("balanced", "terse"): "balanced, brief",
|
||||
("balanced", "measured"): "balanced, measured",
|
||||
}
|
||||
return pieces.get((tone, cadence), "calibrated companion")
|
||||
|
||||
|
||||
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")]
|
||||
|
||||
settings = _aggregate(state)
|
||||
|
||||
persona_name = a.get("persona_name", "Samantha")
|
||||
operator_name = a.get("operator_name", "Pilot")
|
||||
|
||||
cart = Cart(
|
||||
operator_email=state.operator_email,
|
||||
operator_name=operator_name,
|
||||
persona_name=persona_name,
|
||||
cart_tag=_make_tag(persona_name, state.operator_email),
|
||||
language=language,
|
||||
voice=voice,
|
||||
ui_palette=settings["palette"],
|
||||
ui_typography=settings["typography"],
|
||||
ui_density=settings["density"],
|
||||
ui_labels=settings["labels"],
|
||||
)
|
||||
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
|
||||
Reference in New Issue
Block a user