上线版: 产品表单统一+form嵌套修复+用户管理+部署+三套叙事

- 产品编辑入口统一走 ProductFormFull(卖点/风格/人群/品牌词全字段);
  修复开任务页 <form> 套 <form> 致"编辑产品"报错、改不了、跳回首个产品
- dashboard 入口卡片对齐实际路由: 系统管理(/config) 与 工作配置(/settings) 分开;
  settings ?tab=products 直达改用挂载后读 URL, 消除 hydration mismatch
- 新增用户管理(users API/admin service/改密页) + alembic 022/023/024
- 上线部署: Dockerfile / docker-compose.prod+https / nginx https / .env.example
- A8 三套正交叙事(痛点/场景/成分背书) + beige 调色去AI化 + 飞轮 text_import 高权重信号

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

backend/app/api/v1/review.py

@@ -53,28 +53,78 @@ def _fmt_queue_item(t: GenerationTask, db: Session) -> dict:
     from app.api.v1.tasks import _score_array_to_obj
     product = db.query(Product).filter(Product.id == t.product_id).first()
     operator = db.query(User).filter(User.id == t.operator_id).first()
-    selected_text = db.query(TextCandidate).filter(
+    selected_texts = db.query(TextCandidate).filter(
         TextCandidate.task_id == t.id, TextCandidate.is_selected == True
-    ).first()
-    selected_image = db.query(ImageCandidate).filter(
+    ).order_by(TextCandidate.strategy.asc(), TextCandidate.id.asc()).all()
+    selected_images = db.query(ImageCandidate).filter(
         ImageCandidate.task_id == t.id, ImageCandidate.is_selected == True
-    ).first()
+    ).order_by(ImageCandidate.strategy.asc(), ImageCandidate.seq.asc()).all()
 
-    # content 列存 JSON dict，解包取 title/content 分字段返回
-    text_parsed: dict = {}
-    if selected_text and selected_text.content:
+    selected_text = selected_texts[0] if selected_texts else None
+    selected_image = selected_images[0] if selected_images else None
+
+    def _fmt_selected_text(selected_text: TextCandidate | None) -> dict | None:
+        if not selected_text:
+            return None
+        text_parsed: dict = {}
         try:
-            text_parsed = json.loads(selected_text.content)
+            text_parsed = json.loads(selected_text.content or "{}")
         except (json.JSONDecodeError, ValueError):
             text_parsed = {"content": selected_text.content}
+        text_score = None
+        if selected_text.score_json:
+            try:
+                text_score = _score_array_to_obj(json.loads(selected_text.score_json))
+            except (json.JSONDecodeError, ValueError):
+                text_score = None
+        return {
+            "candidate_id": selected_text.id,
+            "strategy": selected_text.strategy,
+            "angle_label": selected_text.angle_label,
+            "title": text_parsed.get("title", ""),
+            "content": text_parsed.get("content", ""),
+            "tags": text_parsed.get("tags", []),
+            "score": text_score,
+        }
+
+    def _fmt_selected_image(selected_image: ImageCandidate | None) -> dict | None:
+        if not selected_image:
+            return None
+        return {
+            "candidate_id": selected_image.id,
+            "strategy": selected_image.strategy,
+            "url": selected_image.url,
+            "role": selected_image.role,
+            "seq": selected_image.seq,
+        }
+
+    images_by_strategy: dict[str, list[ImageCandidate]] = {}
+    for img in selected_images:
+        images_by_strategy.setdefault(img.strategy or "_", []).append(img)
+
+    selected_sets = []
+    for strategy in ("A", "B", "C"):
+        text = next((tc for tc in selected_texts if tc.strategy == strategy), None)
+        imgs = images_by_strategy.get(strategy, [])
+        if text or imgs:
+            selected_sets.append({
+                "strategy": strategy,
+                "selected_text": _fmt_selected_text(text),
+                "selected_images": [_fmt_selected_image(img) for img in imgs],
+            })
 
     # 质量分:解析 score_json 明细数组算总分+透传维度(绝不读 eval_score,设计留NULL)
-    text_score = None
-    if selected_text and selected_text.score_json:
-        try:
-            text_score = _score_array_to_obj(json.loads(selected_text.score_json))
-        except (json.JSONDecodeError, ValueError):
-            text_score = None
+    legacy_text = _fmt_selected_text(selected_text)
+    legacy_image = _fmt_selected_image(selected_image)
+    # 兼容旧前端字段：selected_text/selected_image 仍返回第一套第一张。
+    if legacy_text:
+        legacy_text.pop("strategy", None)
+    if legacy_image:
+        legacy_image = {
+            "candidate_id": legacy_image["candidate_id"],
+            "strategy": legacy_image["strategy"],
+            "url": legacy_image["url"],
+        }
 
     return {
         "task_id": t.id,
@@ -82,19 +132,9 @@ def _fmt_queue_item(t: GenerationTask, db: Session) -> dict:
         "theme": t.theme,
         "submitted_at": t.updated_at.isoformat(),
         "operator_name": operator.username if operator else None,
-        "selected_text": {
-            "candidate_id": selected_text.id,
-            "angle_label": selected_text.angle_label,
-            "title": text_parsed.get("title", ""),
-            "content": text_parsed.get("content", ""),   # 纯正文
-            "tags": text_parsed.get("tags", []),
-            "score": text_score,   # 审核显分:总分+7维明细(C2)
-        } if selected_text else None,
-        "selected_image": {
-            "candidate_id": selected_image.id,
-            "strategy": selected_image.strategy,
-            "url": selected_image.url,
-        } if selected_image else None,
+        "selected_text": legacy_text,
+        "selected_image": legacy_image,
+        "selected_sets": selected_sets,
     }