import json
import logging
import re
from typing import Any, Optional

from .settings import MemoirImageSettings

logger = logging.getLogger(__name__)
_CJK_RE = re.compile(r"[\u3400-\u4dbf\u4e00-\u9fff\uf900-\ufaff]")


class MemoirImagePromptService:
    CATEGORY_STYLE_MAP = {
        "childhood": "watercolor",
        "family": "watercolor",
        "career_early": "realistic",
        "career_achievement": "realistic",
        "career_challenge": "realistic",
        "beliefs": "editorial illustration",
        "summary": "editorial illustration",
    }
    CATEGORY_FALLBACK_SUBJECT_MAP = {
        "childhood": "childhood memory",
        "education": "school memory",
        "career_early": "early career memory",
        "career_achievement": "career achievement memory",
        "career_challenge": "career challenge memory",
        "family": "family memory",
        "beliefs": "reflective life memory",
        "summary": "memoir summary scene",
    }

    def __init__(self, llm: Optional[Any], settings: MemoirImageSettings):
        self.llm = llm
        self.settings = settings

    def build_prompt(
        self,
        chapter_title: str,
        chapter_category: str,
        description: str,
        context_excerpt: str,
    ) -> dict[str, str]:
        style = self.CATEGORY_STYLE_MAP.get(chapter_category, self.settings.default_style)
        prompt_context = f"{chapter_category}: {chapter_title}"

        llm_input = {
            "chapter_title": chapter_title,
            "chapter_category": chapter_category,
            "description": description,
            "context_excerpt": context_excerpt,
            "default_style": style,
            "default_size": self.settings.default_size,
        }

        if self.llm:
            try:
                response = self.llm.invoke(
                    "Return JSON only with keys prompt, style, size. "
                    "Convert the memoir scene into an image-generation prompt.\n"
                    + json.dumps(llm_input, ensure_ascii=False)
                )
                parsed = json.loads(response.content)
                return {
                    "prompt": _ensure_style_in_prompt(parsed["prompt"], parsed.get("style", style)),
                    "style": parsed.get("style", style),
                    "size": parsed.get("size", self.settings.default_size),
                    "prompt_context": prompt_context,
                }
            except Exception as exc:
                logger.warning(
                    "图片 prompt 生成回退到默认模板: chapter_category=%s, title=%s, error=%s",
                    chapter_category,
                    chapter_title,
                    exc,
                )

        return {
            "prompt": _ensure_style_in_prompt(
                self._build_fallback_prompt(
                    chapter_category=chapter_category,
                    description=description,
                    context_excerpt=context_excerpt,
                    style=style,
                ),
                style,
            ),
            "style": style,
            "size": self.settings.default_size,
            "prompt_context": prompt_context,
        }

    def _build_fallback_prompt(
        self,
        chapter_category: str,
        description: str,
        context_excerpt: str,
        style: str,
    ) -> str:
        subject = self.CATEGORY_FALLBACK_SUBJECT_MAP.get(chapter_category, "memoir scene")
        if _contains_cjk(description) or _contains_cjk(context_excerpt):
            return (
                f"A {style} illustration of a {subject}, emotionally resonant, cinematic composition, "
                "authentic everyday details, natural lighting, expressive environment, no text overlay."
            )

        details = ". ".join(part.strip() for part in (description, context_excerpt) if part.strip())
        if not details:
            details = "A personal life story scene with authentic emotional detail"
        return (
            f"A {style} illustration of a {subject}. "
            f"Scene details: {details}. "
            "Cinematic composition, authentic emotions, natural lighting, no text overlay."
        )


def _contains_cjk(value: str) -> bool:
    return bool(_CJK_RE.search(value or ""))


def _ensure_style_in_prompt(prompt: str, style: str) -> str:
    cleaned_prompt = (prompt or "").strip()
    cleaned_style = (style or "").strip()
    if not cleaned_style:
        return cleaned_prompt
    if cleaned_style.lower() in cleaned_prompt.lower():
        return cleaned_prompt
    if not cleaned_prompt:
        return cleaned_style
    return f"{cleaned_style}, {cleaned_prompt}"