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928c1dea72
* feat: bundle standalone taste distillation and application workflows * docs: fix imported taste skill markdown lint * docs: align Turkish agent catalog with taste skills * refactor: make ECC the canonical reusable video engine * fix: preserve video duration when applying image overlays * fix: preserve background colors in image compositing * fix: report best-effort duration targets and shortfalls * feat: ship verified Fusion presets with compatibility provenance * feat(tasteforge): preserve native edits in application bundles * feat(tasteforge): compile local preservation without hosted input * fix: update js-yaml to patched 4.3.2 * test: report bounded Stop wrapper failure diagnostics * fix(tasteforge): fail closed on unsafe output names, missing overlays and cadence - cli: default report and spec paths are derived from pack name and profile genre; require the manifest's name pattern before using either as a filename part so a traversal string cannot write outside cwd/out. - apply_local: a pack without cadence.json, or with no measured shots and no explicit mean_shot, raises instead of silently planning 1.0s shots and reporting a measured cadence. - legacy apply: a missing overlay aborts before any paid upload; forge() would have rejected it after every take was generated. - requirements-live: pin fal-client>=0.13.0, the first release whose subscribe() accepts client_timeout. Addresses the five P1 findings from the independent review of #3033. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015fxHRsydPqEcYngGbqkgt1 --------- Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
"""Deterministic taste interview: answers in, structured profile out.
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The interview is the local, human half of distillation. It asks the same axes
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the recovered implementation asks a vision model (palette, grain, lighting,
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lens, motion, framing, grade, mood, avoid) plus the content brief, and keeps
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look and content strictly separate - collapsing them is the standard failure
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(style words leak into the scene; subject words get read as style).
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from datetime import datetime, timezone
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from typing import Any
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from . import schema
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__all__ = ["Question", "QUESTIONS", "conduct"]
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_LOOK_QUESTIONS = (
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("palette", "Name the dominant colors and how they are distributed."),
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("grain", "Describe texture/noise character (e.g. fine 35mm grain)."),
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("lighting", "Key/fill/practical sources and their quality?"),
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("focal_length", "Apparent focal length and its perspective effect?"),
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("camera_motion", "How does the camera move, or is it locked off?"),
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("subject_framing", "How do subjects sit in frame (headroom, thirds, negative space)?"),
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("grade_description", "The color grade, in colorist language?"),
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("mood_adjectives", "Three adjectives for the mood, comma-separated."),
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("avoid", "Failure modes to avoid, comma-separated."),
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)
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_CONTENT_QUESTIONS = (
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("brief", "What should happen on screen (subject, action, place)?"),
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)
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@dataclass(frozen=True)
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class Question:
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id: str
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prompt: str
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axis: str # "look" or "content"
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QUESTIONS: tuple[Question, ...] = (
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*(Question(qid, prompt, "look") for qid, prompt in _LOOK_QUESTIONS),
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*(Question(qid, prompt, "content") for qid, prompt in _CONTENT_QUESTIONS),
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)
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def _split_list(value: str) -> list[str]:
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return [part.strip() for part in value.replace(";", ",").split(",") if part.strip()]
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def _utc_now() -> str:
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return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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def conduct(answers: dict[str, str], genre: str = "untitled") -> dict[str, Any]:
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"""Build a TasteProfile from free-text answers. Deterministic, offline.
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Missing answers are recorded under ``unanswered`` - never invented.
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"""
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look: dict[str, Any] = {}
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unanswered: list[str] = []
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for qid, _ in _LOOK_QUESTIONS:
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raw = (answers.get(qid) or "").strip()
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if not raw:
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unanswered.append(qid)
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continue
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if qid in ("mood_adjectives", "avoid"):
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look[qid] = _split_list(raw)
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else:
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look[qid] = raw
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# Schema floor: mood_adjectives and avoid must exist as lists.
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look.setdefault("mood_adjectives", [])
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look.setdefault("avoid", [])
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brief = (answers.get("brief") or "").strip()
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if not brief:
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unanswered.append("brief")
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profile = {
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"schema_version": 1,
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"genre": genre,
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"created": _utc_now(),
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"answers": {k: str(v).strip() for k, v in answers.items() if str(v).strip()},
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"unanswered": unanswered,
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"constraints": {
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"look": look,
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"content": {"brief": brief},
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},
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}
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problems = schema.validate(profile, schema.TASTE_PROFILE_SCHEMA)
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if problems:
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raise ValueError(f"interview produced an invalid profile: {problems}")
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return profile
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