fix(stability): R2上传改为懒加载，缺boto3时降级不阻塞启动

modules/script_gen.py

@@ -9,10 +9,10 @@ import os
 import requests
 from typing import Dict, Any, List, Optional
 from pathlib import Path
+from openai import OpenAI
 
 import config
 from modules.db_manager import db
-
 logger = logging.getLogger(__name__)
 
 class ScriptGenerator:
@@ -24,6 +24,11 @@ class ScriptGenerator:
         # 根据 demo: https://api.shubiaobiao.cn/v1beta/models/gemini-3-pro-preview:generateContent
         # 这里我们先假设 base_url 是 v1beta/models/
         self.endpoint = "https://api.shubiaobiao.cn/v1beta/models/gemini-3-pro-preview:generateContent"
+        # OpenAI-compatible client for ShuBiaoBiao (supports multiple models incl. GPT)
+        self.shubiaobiao_client = OpenAI(
+            api_key=config.SHUBIAOBIAO_KEY,
+            base_url=config.SHUBIAOBIAO_BASE_URL
+        )
         
         # Default System Prompt
         self.default_system_prompt = """
@@ -145,11 +150,28 @@ class ScriptGenerator:
         system_prompt = db.get_config("prompt_script_gen", self.default_system_prompt)
         user_prompt = self._build_user_prompt(product_name, product_info)
         
-        # Branch for Doubao
+        # Branch for Doubao (Volcengine)
         if model_provider == "doubao":
-            return self._generate_script_doubao(system_prompt, user_prompt, image_paths)
+            script = self._generate_script_doubao(system_prompt, user_prompt, image_paths)
+            if script:
+                script["selling_points"] = self._postprocess_selling_points(product_info, script.get("selling_points"))
+            return script
 
-        # ... Existing Shubiaobiao Logic ...
+        # Branch for ShuBiaoBiao GPT (OpenAI-compatible multimodal)
+        if model_provider == "shubiaobiao_gpt":
+            script = self._generate_script_shubiaobiao_openai(system_prompt, user_prompt, image_paths, model_name="gpt-5.2")
+            if script:
+                script["selling_points"] = self._postprocess_selling_points(product_info, script.get("selling_points"))
+            return script
+
+        # Branch for ShuBiaoBiao Gemini (OpenAI-compatible; use image URLs instead of base64)
+        if model_provider == "shubiaobiao":
+            script = self._generate_script_shubiaobiao_openai(system_prompt, user_prompt, image_paths, model_name=config.SHUBIAOBIAO_MODEL_TEXT)
+            if script:
+                script["selling_points"] = self._postprocess_selling_points(product_info, script.get("selling_points"))
+            return script
+
+        # Fallback (should not normally reach here)
         
         # 调试: 检查是否使用了自定义 Prompt
         if system_prompt != self.default_system_prompt:
@@ -218,6 +240,8 @@ class ScriptGenerator:
                     return None
                     
                 final_script = self._validate_and_fix_script(script_json)
+                # 不改 prompt 的前提下：对卖点做轻量规则化（更具体、更可执行）
+                final_script["selling_points"] = self._postprocess_selling_points(product_info, final_script.get("selling_points"))
                 
                 # Add Debug Info (包含原始输出)
                 final_script["_debug"] = {
@@ -237,6 +261,165 @@ class ScriptGenerator:
                 logger.error(f"Response content: {response.text}")
             return None
 
+    def _upload_images_to_r2(self, image_paths: List[str], limit: int = 10) -> List[str]:
+        urls: List[str] = []
+        if not image_paths:
+            return urls
+        # NOTE: avoid hard import dependency at app startup.
+        # If boto3 / storage is not installed on the runtime, we should not crash Streamlit.
+        try:
+            from modules import storage  # lazy import
+        except Exception as e:
+            logger.warning(f"R2 upload disabled (storage/boto3 unavailable): {e}")
+            return urls
+        for p in image_paths[:limit]:
+            try:
+                if p and Path(p).exists():
+                    url = storage.upload_file(str(p))
+                    if url:
+                        urls.append(url)
+            except Exception as e:
+                logger.warning(f"Failed to upload script image to R2: {p} ({e})")
+        return urls
+
+    def _generate_script_shubiaobiao_openai(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        image_paths: List[str],
+        model_name: str,
+    ) -> Optional[Dict[str, Any]]:
+        """
+        ShuBiaoBiao OpenAI-compatible multimodal chat.
+        IMPORTANT: For ShuBiaoBiao models, we pass image URLs (R2 public URLs), not base64.
+        """
+        messages = [{"role": "system", "content": system_prompt}]
+        user_content: List[Dict[str, Any]] = []
+        # Images first (URL), then text
+        urls = self._upload_images_to_r2(image_paths or [], limit=10)
+        for url in urls:
+            user_content.append({"type": "image_url", "image_url": {"url": url}})
+        user_content.append({"type": "text", "text": user_prompt})
+        messages.append({"role": "user", "content": user_content})
+
+        try:
+            resp = self.shubiaobiao_client.chat.completions.create(
+                model=model_name,
+                messages=messages,
+                temperature=0.7,
+            )
+            content_text = (resp.choices[0].message.content or "").strip()
+            script_json = self._extract_json_from_response(content_text)
+            if script_json is None:
+                logger.error(f"Failed to extract JSON from shubiaobiao response({model_name}): {content_text[:500]}...")
+                return None
+            final_script = self._validate_and_fix_script(script_json)
+            final_script["_debug"] = {
+                "system_prompt": system_prompt,
+                "user_prompt": user_prompt,
+                "raw_output": content_text,
+                "provider": f"shubiaobiao:{model_name}",
+                "image_urls": urls,
+            }
+            return final_script
+        except Exception as e:
+            logger.error(f"shubiaobiao script generation failed ({model_name}): {e}")
+            return None
+
+    def _postprocess_selling_points(self, product_info: Dict[str, Any], selling_points: Any) -> List[str]:
+        """
+        Engineering-only postprocess (NO prompt change):
+        - De-duplicate
+        - Prefer specific points derived from tags/params when LLM points are too generic
+        """
+        def _norm_list(v: Any) -> List[str]:
+            if isinstance(v, list):
+                return [str(x).strip() for x in v if str(x).strip()]
+            if isinstance(v, str) and v.strip():
+                return [v.strip()]
+            return []
+
+        tags = str((product_info or {}).get("tags", "") or "")
+        params = str((product_info or {}).get("params", "") or "")
+        category = str((product_info or {}).get("category", "") or "")
+        # Candidates from tags/params/category
+        raw = " ".join([tags, params, category])
+        parts = []
+        for sep in ["|", "；", ";", "、", ",", "，", "\n"]:
+            raw = raw.replace(sep, "|")
+        for p in raw.split("|"):
+            p = p.strip()
+            if not p:
+                continue
+            # params like "key:value"
+            if ":" in p:
+                kv = [x.strip() for x in p.split(":", 1)]
+                if len(kv) == 2 and kv[1]:
+                    parts.append(kv[1])
+                else:
+                    parts.append(p)
+            else:
+                parts.append(p)
+
+        # Heuristic: keep more concrete phrases
+        generic_words = [
+            "好看", "百搭", "高级", "气质", "显白", "好用", "耐看", "时尚", "精致", "很棒", "不错", "喜欢", "推荐",
+            "性价比", "划算", "超值", "必入", "绝了",
+        ]
+        concrete_hints = [
+            "不掉", "牢固", "防滑", "加厚", "大号", "小号", "强力", "耐用", "稳固", "不勒", "不伤",
+            "树脂", "金属", "水钻", "夹", "材质", "尺寸", "弹簧", "夹力", "发量", "马尾",
+        ]
+        candidates: List[str] = []
+        seen = set()
+        for p in parts:
+            if not p or p in seen:
+                continue
+            seen.add(p)
+            if any(h in p for h in concrete_hints) and not any(g in p for g in ["喜欢", "推荐"]):
+                candidates.append(p)
+        # fallback: keep non-empty tags
+        if not candidates:
+            candidates = [p for p in parts if p][:8]
+
+        points = _norm_list(selling_points)
+        # de-dup preserving order
+        out: List[str] = []
+        used = set()
+        for p in points:
+            if p in used:
+                continue
+            used.add(p)
+            out.append(p)
+
+        def _is_generic(p: str) -> bool:
+            # treat as generic if only contains generic words and lacks any concrete hints
+            if any(h in p for h in concrete_hints):
+                return False
+            return any(g in p for g in generic_words)
+
+        # Replace overly generic points with better candidates (keep length <= 3-5 ideally)
+        cand_iter = (c for c in candidates if c not in used)
+        refined: List[str] = []
+        for p in out:
+            if _is_generic(p):
+                c = next(cand_iter, None)
+                if c:
+                    refined.append(c)
+                    used.add(c)
+                    continue
+            refined.append(p)
+
+        # Ensure at least 3 selling points if possible
+        while len(refined) < 3:
+            c = next(cand_iter, None)
+            if not c:
+                break
+            refined.append(c)
+            used.add(c)
+
+        return refined[:5]
+
     def _generate_script_doubao(
         self, 
         system_prompt: str,