import json
import logging
import os
import re
from dataclasses import dataclass

from services.llm import send_message, send_message_with_model
from services.personas import get_persona

logger = logging.getLogger(__name__)

NEGATIVE_PROMPT_SEPARATOR = "\n\n__NEGATIVE_PROMPT__\n\n"

PROMPT_BUILDER_SYSTEM = """You are a Stable Diffusion prompt engineer for anime illustration models.
Given a roleplay chat excerpt, output ONLY valid JSON (no markdown):
{
  "should_generate": true,
  "shot_type": "first_person_pov" | "landscape" | "third_person",
  "action_tags": "booru-style tags for pose/action/expression",
  "environment_tags": "booru-style tags for location/lighting/time"
}
Rules:
- ONLY use real danbooru/e621 tags. Multi-word concepts MUST be underscore_joined.
- Do NOT include appearance/character tags — those are provided separately.
- Do NOT include quality tags, model names, style words, 'pov', or category/metadata words.
- Do NOT invent tags. If unsure — omit.
- Keep action_tags and environment_tags to 3-6 tags each.
- shot_type: default "first_person_pov" for dialogue/intimacy at arm's length. "third_person" only for wide action (fight, chase). "landscape" only when environment is the focus.
- should_generate: false for non-visual beats (pure internal monologue, time skips with no new pose, empty lines).
- NEVER use negative words in tag fields (not, without, naked, nsfw, etc.)."""

ANIMA_BUILDER_EXTRA = """
Anima hybrid mode — ALSO include:
  "pov_cue": "face_to_face" | "walking_together" | "doorway_invite" | "reach_to_viewer" | "dialogue_close",
  "viewer_body_visible": false,
  "scene_description": "ONE short English sentence (max 40 words). Camera POV: what the viewer sees. Mood/atmosphere only — do NOT repeat tags from action_tags/environment_tags. Do NOT list comma-separated booru tags."
POV / interaction rules:
- Default viewer_body_visible: false. The viewer's body, hands, or face must NOT appear in the image — only the character toward the camera.
- For hugs, embraces: use arms_out, reaching_towards_viewer, inviting_hug — NOT holding_hands, lifting, carrying, nose_rub (these draw a second body in POV).
- For long messages with time skips ("About an hour later..."), illustrate ONLY the final visible beat (usually the last paragraph).
- scene_description: describe HER toward the camera only — NEVER "someone", "both", "with you", "hand in hand with", or another person's body.
- NEVER use tags: looking_at_each_other, couple, 2girls, 2boys, multiple_girls. For POV walking together omit holding_hands (use walking, smiling, reaching_towards_viewer instead).
- pov_cue: pick the framing that matches the CURRENT beat (walking_together for strolling side by side, doorway_invite for doorway with arms open, reach_to_viewer when she reaches toward camera, face_to_face for close dialogue).
- Illustrate ONLY the beat under === ILLUSTRATE ===; use === Context === for outfit/location hints only.
- Do NOT put English sentences in action_tags or environment_tags — tags only."""

POV_CUE_PHRASES: dict[str, str] = {
    "face_to_face": "POV: close face-to-face, she looks directly at you",
    "walking_together": "POV: walking beside you, profile and shared path visible",
    "doorway_invite": "POV: she blocks the doorway, arms open toward you",
    "reach_to_viewer": "POV: she reaches toward the camera",
    "dialogue_close": "POV: close conversation, she faces you at arm's length",
}

POV_CUE_DEFAULT = "POV: she stands before you, facing the camera"

POV_INTERACTION_NEGATIVE = (
    "duplicate, clone, multiple_girls, 2girls, extra_person, pov hands, "
    "disembodied hands, extra arms, second person"
)

_CONTACT_ACTION_KEYWORDS = (
    "hug", "holding_hands", "hand_holding", "arms_out", "embrace",
    "reaching", "inviting_hug", "arm_around", "cuddling",
)

_JUNK_STANDALONE_TAGS = frozenset({
    "white", "black", "skin", "ear", "ears", "girl", "boy", "fox", "wolf", "cat",
    "short", "tall", "slim", "golden", "silver", "red", "blue", "green", "purple",
    "pink", "brown", "blonde", "eye", "eyes", "hair",
})

_INVALID_TAGS = frozenset({
    "pumped_up", "pumped", "looking_at_each_other", "couple",
    "2girls", "2boys", "multiple_girls", "multiple_boys", "duo",
})

_POV_DROP_ACTION_TAGS = frozenset({
    "holding_hands", "hand_holding", "looking_at_each_other", "couple",
    "lifting", "carry", "carrying", "princess_carry", "nose_rub", "nose_boop",
})

_TIME_SKIP_RE = re.compile(
    r"(?i)\b(?:about an hour later|hours later|later that (?:day|evening|night)|"
    r"the next (?:day|morning|evening)|meanwhile|after (?:some )?time)\b[.…\s]*",
)

_POV_MOOD_FALLBACK: dict[str, str] = {
    "walking_together": "Easy warmth and quiet laughter in the afternoon light.",
    "doorway_invite": "Cool air and playful tension as she waits in the doorway.",
    "reach_to_viewer": "A charged moment as she reaches toward the camera.",
    "face_to_face": "Her expression softens in close focus toward the camera.",
    "dialogue_close": "Intimate calm in the space between you.",
}

_INDOOR_ENV_MARKERS = frozenset({"doorway", "indoors", "indoor", "apartment", "inside", "room"})
_OUTDOOR_ENV_MARKERS = frozenset({"outdoor", "outdoors", "outside", "street"})

_POV_PROSE_BANNED = re.compile(
    r"\b(someone|both|together with|hand in hand with|another person|second person|"
    r"your hands|your fingers|your embrace|your heat|intertwined|with you|"
    r"demands your|before you)\b",
    re.IGNORECASE,
)

SD_ANIMA_DUAL_COMPARE = os.getenv("SD_ANIMA_DUAL_COMPARE", "false").lower() in ("1", "true", "yes")


@dataclass
class SdPromptBundle:
    tag_full: str
    negative: str
    desc_full: str | None = None


def extract_image_prompt_tag(text: str) -> str | None:
    if "[IMAGE_PROMPT:" not in text:
        return None
    try:
        start = text.index("[IMAGE_PROMPT:") + len("[IMAGE_PROMPT:")
        end = text.index("]", start)
        return text[start:end].strip()
    except ValueError:
        return None


def strip_image_prompt_tag(text: str) -> str:
    return re.sub(r"\[IMAGE_PROMPT:.*?\]", "", text, flags=re.DOTALL).strip()


SD_CHECKPOINT = os.getenv("SD_CHECKPOINT", "")
SD_UNET = os.getenv("SD_UNET", "")
SD_PROMPT_MODEL = os.getenv("SD_PROMPT_MODEL", "").strip()

PONY_CHECKPOINTS = {"ponyDiffusionV6XL_v6StartWithThisOne.safetensors"}
PONY_NEGATIVE = "score_1, score_2, score_3, score_4, worst quality, low quality, blurry, bad anatomy, watermark, text, censored"
ANIMA_NEGATIVE = "worst quality, low quality, score_1, score_2, score_3, blurry, jpeg artifacts, sepia"


def _is_pony() -> bool:
    return SD_CHECKPOINT in PONY_CHECKPOINTS


def _is_anima() -> bool:
    return bool(SD_UNET) and not SD_CHECKPOINT


def anima_dual_enabled() -> bool:
    return _is_anima() and SD_ANIMA_DUAL_COMPARE


def _builder_system() -> str:
    if _is_anima():
        return PROMPT_BUILDER_SYSTEM + ANIMA_BUILDER_EXTRA
    return PROMPT_BUILDER_SYSTEM


def _normalize_shot_type(scene: dict) -> dict:
    st = (scene.get("shot_type") or "").strip().lower()
    if st == "landscape":
        scene["shot_type"] = "landscape"
        return _sanitize_scene_fields(scene)
    if st == "third_person":
        action = (scene.get("action_tags") or "").lower()
        wide = ("battle", "fight", "chase", "running", "crowd", "wide_shot", "group_shot")
        if any(w in action for w in wide):
            scene["shot_type"] = "third_person"
            return _sanitize_scene_fields(scene)
    scene["shot_type"] = "first_person_pov"
    if scene.get("viewer_body_visible") is None:
        scene["viewer_body_visible"] = False
    return _sanitize_scene_fields(scene)


def _split_tag_input(tag_str: str) -> list[str]:
    return [t.strip() for t in (tag_str or "").split(",") if t.strip()]


def _is_sentence_like_tag(tag: str) -> bool:
    t = tag.strip()
    if len(t) > 45:
        return True
    if re.search(r"[.!?]", t):
        return True
    words = t.split()
    return len(words) >= 5 and "_" not in t


def _filter_tag_field(tag_str: str, *, for_pov: bool, field: str) -> str:
    kept: list[str] = []
    for raw in _split_tag_input(tag_str):
        key = raw.lower().replace(" ", "_")
        if key in _INVALID_TAGS:
            continue
        if _is_sentence_like_tag(raw):
            continue
        if for_pov and field == "action" and key in _POV_DROP_ACTION_TAGS:
            continue
        kept.append(raw if "_" in raw else key)
    return ", ".join(kept)


def _reconcile_environment_tags(env_str: str) -> str:
    tags = _split_tag_input(env_str)
    keys = {t.lower().replace(" ", "_") for t in tags}
    has_indoor = bool(keys & _INDOOR_ENV_MARKERS) or any(
        any(m in k for m in _INDOOR_ENV_MARKERS) for k in keys
    )
    has_outdoor = bool(keys & _OUTDOOR_ENV_MARKERS) or any(
        any(m in k for m in _OUTDOOR_ENV_MARKERS) for k in keys
    )
    if has_indoor and has_outdoor:
        tags = [t for t in tags if t.lower().replace(" ", "_") not in _OUTDOOR_ENV_MARKERS]
    return ", ".join(tags)


def _sanitize_pov_prose(desc: str, scene: dict) -> str:
    if not desc or not desc.strip():
        return ""
    if scene.get("shot_type") != "first_person_pov":
        return desc.strip()

    kept: list[str] = []
    for sentence in re.split(r"(?<=[.!?])\s+", desc.strip()):
        s = sentence.strip()
        if not s:
            continue
        if _POV_PROSE_BANNED.search(s):
            continue
        if re.search(r"\bwolfgirl\b", s, re.I) and re.search(
            r"\b(walks|walking|stands)\b", s, re.I
        ):
            continue
        kept.append(s)
    out = " ".join(kept).strip()
    return re.sub(r"\bat the viewer\b", "at the camera", out, flags=re.IGNORECASE)


def _sanitize_scene_fields(scene: dict) -> dict:
    scene = dict(scene)
    for_pov = scene.get("shot_type") == "first_person_pov"
    scene["action_tags"] = _filter_tag_field(
        scene.get("action_tags") or "", for_pov=for_pov, field="action"
    )
    env = _filter_tag_field(scene.get("environment_tags") or "", for_pov=False, field="env")
    scene["environment_tags"] = _reconcile_environment_tags(env)
    scene["scene_description"] = _sanitize_pov_prose(
        (scene.get("scene_description") or "").strip(), scene
    )
    return scene


def _scene_should_generate(scene: dict) -> bool:
    if scene.get("should_generate") is False:
        return False
    return True


def _sanitize_tags_string(tag_str: str) -> str:
    if not tag_str:
        return ""
    out: list[str] = []
    seen: set[str] = set()
    for raw in tag_str.split(","):
        t = raw.strip()
        if not t:
            continue
        key = t.lower().replace(" ", "_")
        if key in seen:
            continue
        if key in _INVALID_TAGS:
            continue
        if "_" not in key and key in _JUNK_STANDALONE_TAGS:
            continue
        if len(key) <= 2:
            continue
        seen.add(key)
        out.append(t if "_" in t else key)
    return ", ".join(out)


def _quality_prefix() -> str:
    if _is_pony():
        return "score_9, score_8_up, score_7_up, source_anime, highres"
    if _is_anima():
        return "masterpiece, best quality, score_7, anime"
    return "masterpiece, best quality, highres"


def _appearance_for_persona(persona: dict | None) -> str:
    """Tag core uses appearance_tags only (prose is for LLM context, not Comfy tag line)."""
    return _sanitize_tags_string((persona or {}).get("appearance_tags", ""))


def _dedupe_outfit_tags(outfit_tags: str) -> str:
    tags = _split_tag_input(outfit_tags)
    keys = {t.lower().replace(" ", "_") for t in tags}
    if len(keys & {"jeans", "ripped_jeans", "black_jeans"}) > 1 and "jeans" in keys:
        tags = [t for t in tags if t.lower().replace(" ", "_") != "jeans"]
    return ", ".join(tags)


def _scene_has_physical_contact(scene: dict) -> bool:
    action = (scene.get("action_tags") or "").lower()
    return any(k in action for k in _CONTACT_ACTION_KEYWORDS)


def _infer_pov_cue_from_action(action_tags: str) -> str:
    action = (action_tags or "").lower()
    if any(k in action for k in ("holding_hands", "hand_holding", "walking", "strolling")):
        return "walking_together"
    if any(k in action for k in ("doorway", "door", "entry", "threshold")):
        if any(k in action for k in ("arms_out", "hug", "embrace", "inviting")):
            return "doorway_invite"
    if any(k in action for k in ("arms_out", "reaching", "inviting_hug", "hug", "embrace")):
        return "reach_to_viewer"
    if any(k in action for k in ("sitting", "lying", "bed")):
        return "dialogue_close"
    return "face_to_face"


def _build_pov_phrase(scene: dict) -> str:
    if scene.get("shot_type") != "first_person_pov":
        return ""
    cue = (scene.get("pov_cue") or "").strip().lower().replace("-", "_").replace(" ", "_")
    if cue in POV_CUE_PHRASES:
        return POV_CUE_PHRASES[cue]
    inferred = _infer_pov_cue_from_action(scene.get("action_tags", ""))
    return POV_CUE_PHRASES.get(inferred, POV_CUE_DEFAULT)


def _append_lora(parts: list[str], persona: dict | None) -> None:
    lora = (persona or {}).get("lora_name", "")
    weight = (persona or {}).get("lora_weight", 0.8)
    if lora:
        parts.append(f"<lora:{lora}:{weight}>")


def _dedupe_comma_join(parts: list[str]) -> str:
    positive = ", ".join(p.strip() for p in parts if p and p.strip())
    seen: set[str] = set()
    deduped: list[str] = []
    for tag in positive.split(", "):
        t = tag.strip()
        if t and t not in seen:
            seen.add(t)
            deduped.append(t)
    return ", ".join(deduped)


def _build_tag_core(scene: dict, persona: dict | None, outfit_tags: str = "") -> str:
    """Anchor + structure: quality, appearance, outfit, action/env tags, LoRA. No POV prose, no scene_description."""
    parts = [_quality_prefix()]
    appearance = _appearance_for_persona(persona)
    if appearance:
        parts.append(appearance)
    if outfit_tags:
        parts.append(_sanitize_tags_string(_dedupe_outfit_tags(outfit_tags)))
    if scene.get("shot_type") == "landscape":
        parts.append(_sanitize_tags_string(scene.get("environment_tags", "")))
    else:
        if not _is_anima() and scene.get("shot_type") == "first_person_pov":
            parts.append("pov, first-person view, looking at viewer")
        parts.append(_sanitize_tags_string(scene.get("action_tags", "")))
        parts.append(_sanitize_tags_string(scene.get("environment_tags", "")))
    _append_lora(parts, persona)
    return _dedupe_comma_join(parts)


def build_positive_prompt_tags_only(scene: dict, persona: dict | None, outfit_tags: str = "") -> str:
    """Tags + contextual POV phrase (Anima) or legacy Pony path."""
    if not _is_anima():
        return build_positive_prompt(scene, persona, outfit_tags)
    core = _build_tag_core(scene, persona, outfit_tags)
    pov = _build_pov_phrase(scene)
    if pov:
        return f"{core}, {pov}" if core else pov
    return core


def _tag_tokens_for_dedupe(tag_line: str) -> set[str]:
    tokens: set[str] = set()
    for part in tag_line.replace("<lora:", " ").split(","):
        for word in re.split(r"[\s_./]+", part.lower()):
            w = word.strip()
            if len(w) >= 4:
                tokens.add(w)
    return tokens


def _trim_redundant_scene_description(desc: str, tag_line: str) -> str:
    tag_tokens = _tag_tokens_for_dedupe(tag_line)
    if not tag_tokens or not desc.strip():
        return desc.strip()

    kept: list[str] = []
    for sentence in re.split(r"(?<=[.!?])\s+", desc.strip()):
        s = sentence.strip()
        if not s:
            continue
        words = [w.lower() for w in re.findall(r"[a-zA-Z]{4,}", s)]
        if not words:
            kept.append(s)
            continue
        overlap = sum(1 for w in words if w in tag_tokens) / len(words)
        if overlap < 0.62:
            kept.append(s)

    return " ".join(kept).strip()


def _extract_illustrate_content(content: str, max_chars: int = 1400) -> str:
    """Long assistant posts (first_mes): use final beat after time-skip, last paragraphs."""
    text = strip_image_prompt_tag(content).strip()
    if not text:
        return ""
    chunks = _TIME_SKIP_RE.split(text)
    if len(chunks) > 1:
        text = chunks[-1].strip()
    if len(text) <= max_chars:
        return text
    paragraphs = [p.strip() for p in re.split(r"\n\s*\n", text) if p.strip()]
    if paragraphs:
        for n in (1, 2, 3):
            tail = "\n\n".join(paragraphs[-n:])
            if len(tail) <= max_chars:
                return tail
        return paragraphs[-1][-max_chars:]
    return text[-max_chars:]


def _fallback_mood_prose(scene: dict) -> str:
    cue = (scene.get("pov_cue") or "").strip().lower().replace("-", "_").replace(" ", "_")
    if cue in _POV_MOOD_FALLBACK:
        return _POV_MOOD_FALLBACK[cue]
    inferred = _infer_pov_cue_from_action(scene.get("action_tags", ""))
    return _POV_MOOD_FALLBACK.get(inferred, "Soft atmosphere; her expression toward the camera.")


def _cap_scene_description(desc: str, max_words: int = 40, max_chars: int = 220) -> str:
    words = desc.split()
    if len(words) > max_words:
        desc = " ".join(words[:max_words])
    if len(desc) > max_chars:
        desc = desc[: max_chars - 3] + "..."
    return desc


def build_positive_prompt_hybrid(scene: dict, persona: dict | None, outfit_tags: str = "") -> str:
    """Production Anima prompt: tag core + POV cue + short mood prose."""
    if not _is_anima():
        return build_positive_prompt(scene, persona, outfit_tags)

    base = build_positive_prompt_tags_only(scene, persona, outfit_tags)
    desc = _trim_redundant_scene_description(
        (scene.get("scene_description") or "").strip(),
        base,
    )
    desc = _cap_scene_description(desc)
    if not desc:
        desc = _cap_scene_description(_fallback_mood_prose(scene))
    if not desc:
        return base

    lora = (persona or {}).get("lora_name", "")
    weight = (persona or {}).get("lora_weight", 0.8)
    lora_suffix = f" <lora:{lora}:{weight}>" if lora else ""
    if lora_suffix and base.endswith(lora_suffix):
        base = base[: -len(lora_suffix)]
        return f"{base}. {desc}{lora_suffix}"
    return f"{base}. {desc}"


def build_positive_prompt(scene: dict, persona: dict | None, outfit_tags: str = "") -> str:
    """Legacy entry: Pony/non-Anima full prompt; Anima delegates to tags-only."""
    if _is_anima():
        return build_positive_prompt_tags_only(scene, persona, outfit_tags)

    parts = [_quality_prefix()]
    appearance = _appearance_for_persona(persona)
    if appearance:
        parts.append(appearance)
    if outfit_tags:
        parts.append(_sanitize_tags_string(_dedupe_outfit_tags(outfit_tags)))
    if scene.get("shot_type") == "landscape":
        parts.append(_sanitize_tags_string(scene.get("environment_tags", "")))
    else:
        if scene.get("shot_type") == "first_person_pov":
            parts.append("pov, first-person view, looking at viewer")
        parts.append(_sanitize_tags_string(scene.get("action_tags", "")))
        parts.append(_sanitize_tags_string(scene.get("environment_tags", "")))
    _append_lora(parts, persona)
    return _dedupe_comma_join(parts)


def _negative_for_scene(scene: dict) -> str:
    if _is_pony():
        negative = PONY_NEGATIVE
    elif _is_anima():
        negative = ANIMA_NEGATIVE
    else:
        negative = "low quality, blurry, bad anatomy, watermark, text"

    if scene.get("shot_type") == "first_person_pov":
        negative += ", third person, over the shoulder"
        viewer_visible = scene.get("viewer_body_visible") is True
        if not viewer_visible or _scene_has_physical_contact(scene):
            negative += ", " + POV_INTERACTION_NEGATIVE

    return negative


def _format_builder_user_block(persona: dict, messages: list[dict], outfit_json: str) -> str:
    lines: list[str] = []
    tags = (persona.get("appearance_tags") or "").strip()
    lines.append(f"Character appearance (tags): {tags}")
    prose = (persona.get("appearance_prose") or "").strip()
    if _is_anima() and prose and prose != tags:
        snippet = prose[:300] + ("..." if len(prose) > 300 else "")
        lines.append(f"Character notes (do not copy into tags or scene_description): {snippet}")

    try:
        outfit_list = json.loads(outfit_json or "[]")
        outfit_ref = ", ".join(outfit_list) if isinstance(outfit_list, list) else ""
    except Exception:
        outfit_ref = ""

    if outfit_ref:
        lines.append(f"Current outfit (tags): {outfit_ref}")

    recent = [m for m in messages if m.get("role") in ("user", "assistant")][-6:]
    if not recent:
        lines.append("\nChat:\n(no messages — return should_generate=false)")
        return "\n".join(lines)

    illustrate: list[dict] = []
    if recent[-1]["role"] == "assistant":
        illustrate = [recent[-1]]
        if len(recent) >= 2 and recent[-2]["role"] == "user":
            illustrate.insert(0, recent[-2])
    else:
        illustrate = [recent[-1]]
        if len(recent) >= 2 and recent[-2]["role"] == "assistant":
            illustrate.insert(0, recent[-2])

    context = [m for m in recent if m not in illustrate]

    lines.append("\n=== ILLUSTRATE (draw THIS beat only) ===")
    for m in illustrate:
        raw = m.get("content", "")
        content = _extract_illustrate_content(raw) if m.get("role") == "assistant" else strip_image_prompt_tag(raw)
        lines.append(f"{m['role']}: {content}")

    if context:
        lines.append("\n=== Context (outfit/location hints only — do not illustrate old beats) ===")
        for m in context:
            content = strip_image_prompt_tag(m.get("content", ""))
            if len(content) > 800:
                content = content[:797] + "..."
            lines.append(f"{m['role']}: {content}")

    return "\n".join(lines)


def _parse_scene_json(raw: str) -> dict:
    cleaned = raw.strip()
    if cleaned.startswith("```"):
        cleaned = re.sub(r"^```\w*\n?", "", cleaned)
        cleaned = re.sub(r"\n?```$", "", cleaned)
    scene = json.loads(cleaned)
    if not isinstance(scene, dict):
        raise ValueError("LLM returned non-object JSON")
    return _normalize_shot_type(scene)


def _bundle_from_scene(scene: dict, persona: dict, outfit_tags: str) -> SdPromptBundle:
    negative = _negative_for_scene(scene)
    if _is_anima():
        hybrid = build_positive_prompt_hybrid(scene, persona, outfit_tags)
        tag_full = hybrid + NEGATIVE_PROMPT_SEPARATOR + negative
        desc_full = None
        if anima_dual_enabled():
            tags_only = build_positive_prompt_tags_only(scene, persona, outfit_tags)
            desc_full = tags_only + NEGATIVE_PROMPT_SEPARATOR + negative
        return SdPromptBundle(tag_full=tag_full, negative=negative, desc_full=desc_full)

    positive = build_positive_prompt(scene, persona, outfit_tags)
    tag_full = positive + NEGATIVE_PROMPT_SEPARATOR + negative
    return SdPromptBundle(tag_full=tag_full, negative=negative, desc_full=None)


def _parse_chat_excerpt(excerpt: str) -> list[dict]:
    messages: list[dict] = []
    for line in (excerpt or "").splitlines():
        line = line.strip()
        if not line:
            continue
        lower = line.lower()
        if lower.startswith("user:"):
            messages.append({"role": "user", "content": line[5:].strip()})
        elif lower.startswith("assistant:"):
            messages.append({"role": "assistant", "content": line[10:].strip()})
        elif lower.startswith("system:"):
            messages.append({"role": "system", "content": line[7:].strip()})
        else:
            messages.append({"role": "user", "content": line})
    return messages


async def run_prompt_builder(
    persona_id: str,
    *,
    messages: list[dict] | None = None,
    chat_excerpt: str = "",
    outfit_json: str = "[]",
    appearance_override: str | None = None,
    use_prose: bool = False,
) -> dict:
    """Debug: full SD prompt builder pipeline with LLM raw output."""
    persona = await get_persona(persona_id) or {}
    if appearance_override is not None:
        persona = {**persona, "appearance_tags": appearance_override}

    recent = messages if messages is not None else _parse_chat_excerpt(chat_excerpt)
    recent = [m for m in recent if m.get("role") in ("user", "assistant")]

    user_block = _format_builder_user_block(persona, recent, outfit_json)
    builder_messages = [
        {"role": "system", "content": _builder_system()},
        {"role": "user", "content": user_block},
    ]
    model_used = SD_PROMPT_MODEL or "SYSTEM_MODEL"
    result: dict = {
        "persona_id": persona_id,
        "sd_prompt_model": model_used,
        "builder_system": _builder_system(),
        "builder_user": user_block,
        "anima_dual": anima_dual_enabled(),
    }

    raw = ""
    try:
        if SD_PROMPT_MODEL:
            raw = await send_message_with_model(builder_messages, SD_PROMPT_MODEL)
        else:
            raw = await send_message(builder_messages)
        result["llm_raw"] = raw
        scene = _parse_scene_json(raw)
        result["scene"] = scene

        if not _scene_should_generate(scene):
            result["skipped"] = True
            result["error"] = "should_generate=false"
            return result

        try:
            outfit_tags = ", ".join(json.loads(outfit_json or "[]"))
        except Exception:
            outfit_tags = ""

        negative = _negative_for_scene(scene)
        if _is_anima():
            tags_only = build_positive_prompt_tags_only(scene, persona, outfit_tags)
            hybrid = build_positive_prompt_hybrid(scene, persona, outfit_tags)
            result["tag_positive"] = tags_only
            result["hybrid_positive"] = hybrid
            result["negative"] = negative
            result["tags_only_full"] = tags_only + NEGATIVE_PROMPT_SEPARATOR + negative
            result["hybrid_full"] = hybrid + NEGATIVE_PROMPT_SEPARATOR + negative
            result["tag_full"] = result["hybrid_full"]
        else:
            positive = build_positive_prompt(scene, persona, outfit_tags)
            result["tag_positive"] = positive
            result["negative"] = negative
            result["tag_full"] = positive + NEGATIVE_PROMPT_SEPARATOR + negative
    except Exception as e:
        result["error"] = str(e)
        result["llm_raw"] = raw or result.get("llm_raw", "")

    return result


async def generate_sd_prompt(
    messages: list,
    persona_id: str,
    outfit_json: str = "[]",
) -> SdPromptBundle | None:
    persona = await get_persona(persona_id)
    if not persona:
        return None

    recent = [m for m in messages if m["role"] in ("user", "assistant")]
    if not recent:
        return None

    user_block = _format_builder_user_block(persona, recent, outfit_json)
    builder_messages = [
        {"role": "system", "content": _builder_system()},
        {"role": "user", "content": user_block},
    ]

    raw = ""
    try:
        if SD_PROMPT_MODEL:
            raw = await send_message_with_model(builder_messages, SD_PROMPT_MODEL)
        else:
            raw = await send_message(builder_messages)
        scene = _parse_scene_json(raw)
    except Exception as e:
        logger.warning("sd_prompt failed: %s raw=%.200s", e, raw)
        return None

    if not _scene_should_generate(scene):
        logger.info("sd_prompt: skipped (should_generate=false)")
        return None

    try:
        outfit_list = json.loads(outfit_json or "[]")
        outfit_tags = ", ".join(outfit_list) if isinstance(outfit_list, list) else ""
    except Exception:
        outfit_tags = ""

    bundle = _bundle_from_scene(scene, persona, outfit_tags)
    if anima_dual_enabled() and bundle.desc_full:
        logger.info(
            "Anima prompts: hybrid=%.80s | tags_only=%.80s",
            bundle.tag_full.split(NEGATIVE_PROMPT_SEPARATOR)[0],
            bundle.desc_full.split(NEGATIVE_PROMPT_SEPARATOR)[0],
        )
    return bundle