style: auto-format with black + isort + ruff + prettier [skip ci-format-check]

apps/worker/worker_app/tasks/viral_video.py

@@ -789,6 +789,7 @@ def _analyze_single_image(
             # 不是 XML 也不是 dict：尝试当作纯 JSON 字符串再解析一次
             try:
                 import json as _j2
+
                 _jd = _j2.loads(raw)
                 if isinstance(_jd, dict):
                     return _normalize(_jd, source)
@@ -893,6 +894,7 @@ def _step_image_analysis(job: ViralVideoJob) -> dict:
 
     # ========== V2 路径（VISION_V2_ENABLED=true）==========
     import os as _os_v2
+
     _v2_enabled = _os_v2.environ.get("VISION_V2_ENABLED", "false").lower() in ("1", "true", "yes", "on")
     if _v2_enabled:
         try:
@@ -905,6 +907,7 @@ def _step_image_analysis(job: ViralVideoJob) -> dict:
                 _aiv2 = None  # type: ignore
         if _aiv2 is not None:
             from packages.shared.ai_client import get_doubao_client as _gdc_v2
+
             _cli = _gdc_v2()
             _orig_retries_v2 = _cli.max_retries
             _cli.max_retries = 0

apps/worker/worker_app/tasks/vision/__init__.py

@@ -12,4 +12,5 @@
 输出 dict 格式与 viral_video._normalize() 完全一致，下游信任链/t2i 零改动。
 灰度开关：VISION_V2_ENABLED=true（默认 false，走旧 #2198/#2199 竞速逻辑）。
 """
+
 from .fast_path import analyze_image_v2, analyze_images_v2  # noqa: F401

apps/worker/worker_app/tasks/vision/assembler.py

@@ -5,6 +5,7 @@
 必出字段：name, brand, category, appearance, packaging, text_on_package,
          key_features, scene, mood, portrait_prompt, summary, _source
 """
+
 from __future__ import annotations
 
 from typing import Any
@@ -170,6 +171,7 @@ def _build_portrait_prompt(fj: dict[str, Any]) -> str:
 
 # ---------- 商品字段 ----------
 
+
 def _infer_name(fj: dict[str, Any], ocr_texts: list[str]) -> str:
     pname = fj.get("product_name")
     if pname and pname != "未识别":
@@ -209,7 +211,7 @@ def _infer_category(fj: dict[str, Any]) -> str:
 def _build_appearance(fj: dict[str, Any]) -> str:
     """外观描述：颜色+款式+材质+图案 拼成一段。"""
     parts: list[str] = []
-    for key, label in [
+    for key, _label in [
         ("upper_color", "主色"),
         ("upper_wear", "款式"),
         ("material", "材质"),
@@ -227,8 +229,17 @@ def _build_appearance(fj: dict[str, Any]) -> str:
 
 def _build_key_features(fj: dict[str, Any], ocr_texts: list[str]) -> list[str]:
     feats: list[str] = []
-    for key in ("upper_wear", "lower_wear", "upper_color", "lower_color", "dress_color",
-                "material", "pattern", "style", "accessories"):
+    for key in (
+        "upper_wear",
+        "lower_wear",
+        "upper_color",
+        "lower_color",
+        "dress_color",
+        "material",
+        "pattern",
+        "style",
+        "accessories",
+    ):
         v = fj.get(key)
         if not v:
             continue

apps/worker/worker_app/tasks/vision/fast_path.py

@@ -5,6 +5,7 @@
 外层 8 图全并发，目标 8 图 <15s。
 终极兜底：复用旧 _analyze_single_image 完整 pro VLM 逻辑。
 """
+
 from __future__ import annotations
 
 import logging

apps/worker/worker_app/tasks/vision/ocr_volc.py

@@ -9,6 +9,7 @@
 目标：识别商品包装/Logo/水印上的文字，作为 fast_json VLM 的补充。
 返回值：识别到的文本字符串列表（失败返回 []）。
 """
+
 from __future__ import annotations
 
 import logging

apps/worker/worker_app/tasks/vision/vlm_fast_json.py

@@ -9,6 +9,7 @@
 - timeout=8s（够快，失败则由外层走 pro VLM 兜底）
 - 期望返回纯 JSON object（无 ```json 包裹、无解释文字）
 """
+
 from __future__ import annotations
 
 import json