新增 Pinterest 参考模式：独立于 Google Trends 的完整链路（12国种子词池 / LLM搜索词json_schema+防重复+已用词限100 / 并发爬图 / 多模态分析→原创简报 / 生图带爬取图参考图生图 / UI流程选择）

graph/llms/mock_backend.py

@@ -145,3 +145,46 @@ class MockBackend:
             "style_seeds": _dedup_limit(style, max_style),
             "related_seeds": _dedup_limit(related, max_related),
         }
+
+    def generate_pinterest_terms(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        """规则生成 Pinterest 搜索词（零 API 成本）：从种子词池随机取 + 两两组合增加多样性。"""
+        import random
+        seeds = [str(s).strip() for s in (context.get("seeds") or []) if str(s).strip()]
+        used = {str(u).strip().lower() for u in (context.get("used_terms") or [])}
+        count = int(context.get("count", 10))
+        pool = [s for s in seeds if s.lower() not in used]
+        random.shuffle(pool)
+        terms = pool[:count]
+        # 不足时用「种子词 + 风格词」组合补足（视觉导向，避免与已用重复）
+        style_tail = ["aesthetic", "style", "inspiration", "design", "vibe", "art"]
+        i = 0
+        while len(terms) < count and pool:
+            combo = f"{pool[i % len(pool)]} {style_tail[(i // len(pool)) % len(style_tail)]}"
+            if combo.lower() not in used and combo not in terms:
+                terms.append(combo)
+            i += 1
+        return {"search_terms": terms}
+
+    def analyze_pinterest_images(self, image_paths, term="", country=""):
+        """规则生成设计简报（零 API 成本）：按搜索词启发式推导风格/配色/构图。"""
+        from ..classify import classify, prompt_suggestion
+        cat = classify(term)
+        art_style, palette = derive_style_palette(term, country, category=cat)
+        motif = prompt_suggestion(term, cat).split(" --no ")[0].split(",")[0].strip()
+        composition = derive_composition(term, cat)
+        negative = ("no real people, no likeness of any person, no copyrighted characters, "
+                    "no brand logos, no trademarks, no celebrity, no readable text unless safe")
+        n = max(1, len(image_paths or []))
+        paths = list(image_paths or [])
+        return [{
+            "topic": term,
+            "concept": f"（启发式兜底）围绕「{term}」做原创{art_style}风格印花",
+            "motif": motif,
+            "art_style": art_style,
+            "color_palette": palette,
+            "composition": composition,
+            "negative_prompt": negative,
+            # 生图参考：每条简报对应其来源爬取图（mock 按图逐张产出简报，顺序一一对应）
+            "ref_images": [str(paths[i])] if i < len(paths) else [],
+            "source": "pinterest",
+        } for i in range(n)]

graph/llms/openai_compat_backend.py

@@ -233,6 +233,118 @@ def build_user_prompt(country, topics, aesthetic_hint):
     )
 
 
+# —— Pinterest 参考模式：搜索词生成（json_schema 结构化 + 动态注入已用词防重复）——
+PINTEREST_TERM_SYSTEM_PROMPT = """You are a Pinterest search-term generator for print-on-demand (POD) T-shirt design.
+You turn seed words into diverse, visual, Pinterest-friendly search terms that will be used to scrape inspiration images.
+
+RULES:
+- Generate EXACTLY the requested number of search terms.
+- Terms must be VISUAL / AESTHETIC concepts (style, motif, scene, color) suitable as T-shirt print inspiration.
+- Terms must be DIVERSE and NON-OVERLAPPING: never repeat a concept, never give near-synonyms of each other.
+- DO NOT repeat or closely paraphrase ANY of the "already used terms" provided in the user message.
+- Use the country's local language where natural (e.g. Japanese for JP, Spanish for ES/MX), else English.
+- Each term is 2-4 words, concise, no punctuation.
+- COPYRIGHT-SAFE: no brands, no logos, no characters, no celebrities, no real persons, no franchises.
+- AVOID: politics, religion, hate, violence, sexual content, alcohol, national flags.
+
+Return JSON with the field "search_terms" (array of strings)."""
+
+PINTEREST_TERM_SCHEMA = {
+    "name": "pinterest_search_terms",
+    "schema": {
+        "type": "object",
+        "properties": {
+            "search_terms": {
+                "type": "array",
+                "items": {"type": "string"},
+                "description": "Diverse, non-overlapping Pinterest search terms for T-shirt design inspiration",
+            }
+        },
+        "required": ["search_terms"],
+        "additionalProperties": False,
+    },
+}
+
+
+def build_pinterest_term_user_prompt(context: Dict[str, Any]) -> str:
+    """动态注入：种子词（灵感）+ 已用搜索词（禁止重复）+ 数量要求。"""
+    seeds = context.get("seeds", []) or []
+    used = context.get("used_terms", []) or []
+    count = int(context.get("count", 10))
+    lines = [
+        f"Country: {context.get('country', '')}",
+        f"Seed words (inspiration, may combine or extend): {', '.join(seeds)}",
+        "",
+        f"Already used terms — DO NOT repeat or paraphrase ANY of these: "
+        f"{', '.join(used) if used else '(none yet)'}",
+        "",
+        f"Generate {count} new, diverse, non-overlapping Pinterest search terms.",
+    ]
+    return "\n".join(lines)
+
+
+# —— Pinterest 参考模式：图片分析 → 原创设计简报（多模态）——
+PINTEREST_ANALYZE_SYSTEM_PROMPT = """You are a POD (print-on-demand) T-shirt design analyst.
+You receive Pinterest reference images for one search term. For each image, extract the VISUAL CONCEPT
+(style, mood, motif, color palette, composition) that makes it appealing, then produce an ORIGINAL
+T-shirt print design brief that captures that VIBE WITHOUT copying the image.
+
+RULES:
+- NEVER copy the image, never reproduce the exact artwork, characters, logos, or any text from it.
+- Extract only the abstract style/mood/motif concept as inspiration.
+- Produce an original, flat, print-ready design brief (no garment, no model, no background scene).
+- COPYRIGHT-SAFE: no brands, no logos, no characters, no celebrities, no real persons, no franchises.
+- AVOID: politics, religion, hate, violence, sexual content, alcohol, national flags.
+- motif: English, concrete central subject of the print (e.g. "a smiling cat with a fish", "geometric mountain layers").
+- art_style: English visual technique (e.g. "clean flat vector", "retro screen print").
+- color_palette: English colors (e.g. "sunset orange, cream, dusty blue").
+- composition: English layout (e.g. "centered emblem with balanced negative space").
+- concept: Chinese, one sentence describing the design idea.
+- negative_prompt: what to avoid (real people, likeness, characters, logos, text).
+
+Return JSON with the field "designs" (array of objects with keys:
+motif, art_style, color_palette, composition, concept, negative_prompt)."""
+
+PINTEREST_ANALYZE_SCHEMA = {
+    "name": "pinterest_design_briefs",
+    "schema": {
+        "type": "object",
+        "properties": {
+            "designs": {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "motif": {"type": "string"},
+                        "art_style": {"type": "string"},
+                        "color_palette": {"type": "string"},
+                        "composition": {"type": "string"},
+                        "concept": {"type": "string"},
+                        "negative_prompt": {"type": "string"},
+                    },
+                    "required": ["motif", "art_style", "color_palette", "composition",
+                                 "concept", "negative_prompt"],
+                    "additionalProperties": False,
+                },
+            }
+        },
+        "required": ["designs"],
+        "additionalProperties": False,
+    },
+}
+
+
+def build_pinterest_analyze_user_prompt(term: str, country: str, image_count: int) -> str:
+    return (
+        f"Country: {country}\n"
+        f"Pinterest search term: {term}\n"
+        f"Reference images attached: {image_count} images.\n\n"
+        f"Analyze the attached images and produce {image_count} ORIGINAL design briefs "
+        f"(one per image), each capturing the visual vibe as an original T-shirt print design. "
+        f"Do NOT copy the images."
+    )
+
+
 def call_openai_compatible(cfg, messages, timeout=90):
     base_url = str(cfg.get("base_url", "https://api.openai.com/v1")).rstrip("/")
     api_key = cfg.get("api_key", "")
@@ -251,6 +363,41 @@ def call_openai_compatible(cfg, messages, timeout=90):
     return data["choices"][0]["message"]["content"]
 
 
+def call_openai_compatible_structured(cfg, messages, json_schema, timeout=120):
+    """调用 LLM 并返回结构化 JSON 文本。
+
+    优先 json_schema（strict 结构化输出）；部分兼容厂商不支持 json_schema 时
+    自动回退 json_object（仍要求 JSON）。最终解析交给 _extract_json 兜底。
+    """
+    base_url = str(cfg.get("base_url", "https://api.openai.com/v1")).rstrip("/")
+    api_key = cfg.get("api_key", "")
+    model = cfg.get("model", "gpt-4o-mini")
+    url = f"{base_url}/chat/completions"
+    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+    payload = {
+        "model": model,
+        "messages": messages,
+        "temperature": float(cfg.get("temperature", 0.6)),
+        "response_format": {
+            "type": "json_schema",
+            "json_schema": {
+                "name": json_schema.get("name", "structured_output"),
+                "strict": True,
+                "schema": json_schema.get("schema", json_schema),
+            },
+        },
+    }
+    try:
+        resp = requests.post(url, json=payload, headers=headers, timeout=timeout, proxies=NO_PROXY)
+        resp.raise_for_status()
+        return resp.json()["choices"][0]["message"]["content"]
+    except Exception:  # noqa: BLE001  兼容厂商不支持 json_schema → 回退 json_object
+        payload["response_format"] = {"type": "json_object"}
+        resp = requests.post(url, json=payload, headers=headers, timeout=timeout, proxies=NO_PROXY)
+        resp.raise_for_status()
+        return resp.json()["choices"][0]["message"]["content"]
+
+
 def _retry(func, max_attempts=4, base_delay=4):
     last = None
     for attempt in range(max_attempts):
@@ -345,6 +492,124 @@ class OpenAICompatBackend(LLMBackend):
         _cache_set(cache_key, out)
         return out
 
+    def generate_pinterest_terms(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        """生成 Pinterest 搜索词（json_schema 结构化 + 动态注入已用词防重复）。
+
+        context 字段：country, seeds, used_terms, count。
+        返回 {"search_terms": [str]}；失败抛异常由节点兜底（回退种子词）。
+        """
+        cfg = self._cfg
+        # 防御性上限：已用词最多注入 100 个，防 token 超限（节点层已截断，这里双保险）
+        ctx = dict(context or {})
+        used = [str(u) for u in (ctx.get("used_terms") or []) if str(u)]
+        max_used = int((cfg or {}).get("max_used_terms_in_prompt", 100) or 100)
+        if max_used > 0:
+            ctx["used_terms"] = used[-max_used:]
+        messages = [
+            {"role": "system", "content": PINTEREST_TERM_SYSTEM_PROMPT},
+            {"role": "user", "content": build_pinterest_term_user_prompt(ctx)},
+        ]
+        raw = _retry(lambda: call_openai_compatible_structured(cfg, messages, PINTEREST_TERM_SCHEMA, timeout=120))
+        parsed = _extract_json(raw)
+        terms = [str(x).strip() for x in (parsed.get("search_terms", []) or []) if str(x).strip()]
+        return {"search_terms": terms}
+
+    def analyze_pinterest_images(self, image_paths: List[str], term: str, country: str = "") -> List[Dict[str, Any]]:
+        """多模态分析 Pinterest 图片 → 原创设计简报列表。
+
+        图片输入不被模型支持（纯文本模型 400）时自动降级为纯文本分析（仅用搜索词）。
+        失败返回 []，由节点兜底（回退 mock 规则简报）。
+        """
+        cfg = self._cfg
+        api_key = cfg.get("api_key", "")
+        if not api_key:
+            print("[pinterest_analyze] 未配置 LLM api_key，跳过图片分析")
+            return []
+        base_url = str(cfg.get("base_url") or "https://api.openai.com/v1").rstrip("/")
+        model = cfg.get("model", "gpt-4o-mini")
+        url = f"{base_url}/chat/completions"
+        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+
+        # 图片 → base64 data URI（多模态输入）
+        data_uris: List[str] = []
+        for p in image_paths:
+            try:
+                import base64 as b64
+                mime = "image/png"
+                if Path(p).suffix.lower() in (".jpg", ".jpeg"):
+                    mime = "image/jpeg"
+                data_uris.append(f"data:{mime};base64,{b64.b64encode(Path(p).read_bytes()).decode()}")
+            except Exception as e:  # noqa: BLE001
+                print(f"[pinterest_analyze] 图片读取失败 {p}: {e}")
+
+        def _call(use_images: bool) -> str:
+            user_content: List[Any] = [
+                {"type": "text", "text": build_pinterest_analyze_user_prompt(term, country, len(data_uris))},
+            ]
+            if use_images:
+                user_content += [{"type": "image_url", "image_url": {"url": u}} for u in data_uris]
+            payload = {
+                "model": model,
+                "messages": [
+                    {"role": "system", "content": PINTEREST_ANALYZE_SYSTEM_PROMPT},
+                    {"role": "user", "content": user_content},
+                ],
+                "temperature": 0.5,
+                "response_format": {
+                    "type": "json_schema",
+                    "json_schema": {
+                        "name": PINTEREST_ANALYZE_SCHEMA["name"],
+                        "strict": True,
+                        "schema": PINTEREST_ANALYZE_SCHEMA["schema"],
+                    },
+                },
+            }
+            try:
+                resp = requests.post(url, json=payload, headers=headers, timeout=180, proxies=NO_PROXY)
+                resp.raise_for_status()
+                return str(resp.json()["choices"][0]["message"].get("content") or "")
+            except Exception:  # noqa: BLE001  兼容厂商不支持 json_schema
+                payload["response_format"] = {"type": "json_object"}
+                resp = requests.post(url, json=payload, headers=headers, timeout=180, proxies=NO_PROXY)
+                resp.raise_for_status()
+                return str(resp.json()["choices"][0]["message"].get("content") or "")
+
+        raw = ""
+        if data_uris:
+            try:
+                raw = _call(use_images=True)
+            except Exception as e:  # noqa: BLE001  纯文本模型不支持图片 → 降级纯文本
+                print(f"[pinterest_analyze] 图片输入失败，降级纯文本分析: {e}")
+                raw = ""
+        if not raw:
+            try:
+                raw = _call(use_images=False)
+            except Exception as e:  # noqa: BLE001
+                print(f"[pinterest_analyze] 分析失败: {e}")
+                return []
+        try:
+            parsed = _extract_json(raw)
+        except Exception as e:  # noqa: BLE001
+            print(f"[pinterest_analyze] 解析失败: {e}")
+            return []
+        designs = []
+        for i, d in enumerate(parsed.get("designs") or []):
+            if not isinstance(d, dict):
+                continue
+            designs.append({
+                "topic": term,
+                "concept": str(d.get("concept", "")).strip(),
+                "motif": str(d.get("motif", "")).strip(),
+                "art_style": str(d.get("art_style", "")).strip(),
+                "color_palette": str(d.get("color_palette", "")).strip(),
+                "composition": str(d.get("composition", "")).strip(),
+                "negative_prompt": str(d.get("negative_prompt", "")).strip(),
+                # 生图参考：每条简报对应其来源爬取图（LLM 按图逐张产出简报，顺序一一对应）
+                "ref_images": [str(image_paths[i])] if i < len(image_paths) else [],
+                "source": "pinterest",
+            })
+        return designs
+
     def generate_title(self, image_path: str, system_prompt: str = "", country: str = "",
                        fallback_text: str = "") -> Dict[str, Any]:
         """多模态标题生成；图片输入不被模型支持（如 qwen 纯文本模型 400）时，