215 lines
7.9 KiB
Python
215 lines
7.9 KiB
Python
import json
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from app.core.logging import get_logger
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import re
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from typing import Any, Optional
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from .json_payload import extract_json_payload
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from .settings import MemoirImageSettings
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logger = get_logger(__name__)
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_CJK_RE = re.compile(r"[\u3400-\u4dbf\u4e00-\u9fff\uf900-\ufaff]")
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class MemoirImagePromptService:
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CATEGORY_STYLE_MAP = {
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"childhood": "watercolor",
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"family": "watercolor",
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"career_early": "realistic",
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"career_achievement": "realistic",
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"career_challenge": "realistic",
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"beliefs": "editorial illustration",
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"summary": "editorial illustration",
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}
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CATEGORY_FALLBACK_SUBJECT_MAP = {
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"childhood": "childhood memory",
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"education": "school memory",
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"career_early": "early career memory",
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"career_achievement": "career achievement memory",
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"career_challenge": "career challenge memory",
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"family": "family memory",
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"beliefs": "reflective life memory",
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"summary": "memoir summary scene",
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}
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def __init__(self, llm: Optional[Any], settings: MemoirImageSettings):
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self.llm = llm
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self.settings = settings
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def build_prompt(
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self,
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chapter_title: str,
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chapter_category: str,
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description: str,
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context_excerpt: str,
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) -> dict[str, str]:
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style = self.CATEGORY_STYLE_MAP.get(chapter_category, self.settings.default_style)
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prompt_context = f"{chapter_category}: {chapter_title}"
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llm_input = {
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"chapter_title": chapter_title,
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"chapter_category": chapter_category,
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"description": description,
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"context_excerpt": context_excerpt,
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"default_style": style,
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"default_size": self.settings.default_size,
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}
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if self.llm:
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raw_response = None
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try:
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response = self.llm.invoke(
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"Return JSON only with keys prompt, style, size. "
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"Convert the memoir scene into an image-generation prompt.\n"
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+ json.dumps(llm_input, ensure_ascii=False)
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)
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raw_response = response.content
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parsed = json.loads(extract_json_payload(raw_response))
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return {
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"prompt": _ensure_style_in_prompt(parsed["prompt"], parsed.get("style", style)),
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"style": parsed.get("style", style),
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"size": parsed.get("size", self.settings.default_size),
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"prompt_context": prompt_context,
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}
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except Exception as exc:
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logger.warning(
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"图片 prompt 生成回退到默认模板: chapter_category=%s, title=%s, error=%s",
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chapter_category,
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chapter_title,
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exc,
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)
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return {
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"prompt": _ensure_style_in_prompt(
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self._build_fallback_prompt(
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chapter_category=chapter_category,
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description=description,
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context_excerpt=context_excerpt,
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style=style,
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),
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style,
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),
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"style": style,
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"size": self.settings.default_size,
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"prompt_context": prompt_context,
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}
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def build_cover_prompt(
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self,
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chapter_title: str,
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chapter_category: str,
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context_excerpt: str,
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) -> dict[str, str]:
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"""生成章节封面图的 image-generation prompt。"""
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style = self.CATEGORY_STYLE_MAP.get(chapter_category, self.settings.default_style)
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prompt_context = f"{chapter_category}: {chapter_title}"
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llm_input = {
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"chapter_title": chapter_title,
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"chapter_category": chapter_category,
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"context_excerpt": context_excerpt,
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"default_style": style,
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"default_size": self.settings.default_size,
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}
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if self.llm:
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try:
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response = self.llm.invoke(
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"Return JSON only with keys prompt, style, size. "
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"Create an image-generation prompt for a memoir chapter COVER. "
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"Emphasize: hero composition, evocative scene, chapter cover aesthetic.\n"
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+ json.dumps(llm_input, ensure_ascii=False)
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)
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parsed = json.loads(extract_json_payload(response.content))
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return {
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"prompt": _ensure_style_in_prompt(
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parsed["prompt"], parsed.get("style", style)
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),
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"style": parsed.get("style", style),
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"size": parsed.get("size", self.settings.default_size),
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"prompt_context": prompt_context,
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}
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except Exception as exc:
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logger.warning(
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"封面 prompt 生成回退到默认模板: chapter_category=%s, title=%s, error=%s",
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chapter_category,
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chapter_title,
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exc,
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)
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return {
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"prompt": _ensure_style_in_prompt(
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self._build_cover_fallback_prompt(
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chapter_category=chapter_category,
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context_excerpt=context_excerpt,
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style=style,
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),
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style,
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),
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"style": style,
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"size": self.settings.default_size,
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"prompt_context": prompt_context,
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}
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def _build_cover_fallback_prompt(
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self,
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chapter_category: str,
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context_excerpt: str,
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style: str,
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) -> str:
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subject = self.CATEGORY_FALLBACK_SUBJECT_MAP.get(
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chapter_category, "memoir scene"
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)
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if _contains_cjk(context_excerpt):
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return (
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f"A {style} chapter cover illustration of a {subject}, "
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"hero composition, evocative scene, emotionally resonant, "
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"cinematic framing, natural lighting, no text overlay."
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)
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details = (context_excerpt or "").strip()[:500]
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if not details:
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details = "A personal life story scene with authentic emotional detail"
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return (
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f"A {style} chapter cover illustration of a {subject}. "
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f"Scene hint: {details}. "
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"Hero composition, evocative scene, cinematic framing, no text overlay."
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)
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def _build_fallback_prompt(
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self,
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chapter_category: str,
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description: str,
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context_excerpt: str,
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style: str,
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) -> str:
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subject = self.CATEGORY_FALLBACK_SUBJECT_MAP.get(chapter_category, "memoir scene")
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if _contains_cjk(description) or _contains_cjk(context_excerpt):
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return (
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f"A {style} illustration of a {subject}, emotionally resonant, cinematic composition, "
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"authentic everyday details, natural lighting, expressive environment, no text overlay."
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)
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details = ". ".join(part.strip() for part in (description, context_excerpt) if part.strip())
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if not details:
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details = "A personal life story scene with authentic emotional detail"
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return (
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f"A {style} illustration of a {subject}. "
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f"Scene details: {details}. "
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"Cinematic composition, authentic emotions, natural lighting, no text overlay."
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)
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def _contains_cjk(value: str) -> bool:
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return bool(_CJK_RE.search(value or ""))
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def _ensure_style_in_prompt(prompt: str, style: str) -> str:
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cleaned_prompt = (prompt or "").strip()
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cleaned_style = (style or "").strip()
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if not cleaned_style:
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return cleaned_prompt
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if cleaned_style.lower() in cleaned_prompt.lower():
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return cleaned_prompt
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if not cleaned_prompt:
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return cleaned_style
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return f"{cleaned_style}, {cleaned_prompt}"
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