Merge branch 'refactor/backend-architecture' into development

api/app/features/memoir/memoir_images/prompting.py

@@ -0,0 +1,133 @@
+import json
+from app.core.logging import get_logger
+import re
+from typing import Any, Optional
+
+from .json_payload import extract_json_payload
+from .settings import MemoirImageSettings
+
+logger = get_logger(__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:
+            raw_response = None
+            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)
+                )
+                raw_response = response.content
+                parsed = json.loads(extract_json_payload(raw_response))
+                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}"