feat: automate image pipeline and simplify gemini flow

core/orchestrator.py

@@ -18,6 +18,7 @@ from db.pending_transfer_db import (
     retry_pending_transfer,
 )
 from services.dispatch_service import dispatch_service
+from services.service_auto_image_pipeline import auto_image_pipeline_service
 
 logger = logging.getLogger("cs_agent")
 
@@ -86,6 +87,7 @@ class SystemOrchestrator:
         self._user_locks: Dict[str, asyncio.Lock] = {}
         self._pending_transfer_task: Optional[asyncio.Task] = None
         self._last_retry_transfer_time: Dict[str, float] = {}
+        self._auto_pipeline_jobs: Dict[str, float] = {}
 
         bus.subscribe("MESSAGE_OUTBOUND", self.handle_outbound_event)
 
@@ -223,6 +225,102 @@ class SystemOrchestrator:
                 return "我在帮你看记录，稍等哈"
         return cleaned
 
+    @staticmethod
+    def _extract_designer_name(transfer_cmd: str) -> str:
+        text = str(transfer_cmd or "").strip()
+        match = re.search(r"\[转移会话\],([^,]+),", text)
+        return str(match.group(1)).strip() if match else ""
+
+    @staticmethod
+    def _infer_processing_intent(requirement_text: str, history: Optional[List[dict]] = None) -> str:
+        combined_parts = [str(requirement_text or "").lower()]
+        for item in history or []:
+            if item.get("role") == "user":
+                combined_parts.append(str(item.get("content") or "").lower())
+        combined = "\n".join(combined_parts)
+        repair_keywords = ("修复", "高清", "清晰", "放大", "老照片")
+        if any(k in combined for k in repair_keywords):
+            return "repair"
+        return "find_original"
+
+    @staticmethod
+    def _collect_recent_image_urls(history: List[dict], fallback_urls: Optional[List[str]] = None) -> List[str]:
+        urls: List[str] = []
+        seen = set()
+
+        def add_url(url: str):
+            value = str(url or "").strip()
+            if not value or value in seen:
+                return
+            seen.add(value)
+            urls.append(value)
+
+        for url in fallback_urls or []:
+            add_url(url)
+
+        for item in reversed(history or []):
+            if item.get("role") != "user":
+                continue
+            raw_urls = item.get("image_urls") or []
+            if isinstance(raw_urls, str):
+                for part in re.split(r"[\n#]+", raw_urls):
+                    add_url(part)
+            elif isinstance(raw_urls, list):
+                for part in raw_urls:
+                    add_url(part)
+            content = str(item.get("content") or "")
+            for match in re.findall(r"https?://[^\s#]+", content):
+                add_url(match)
+            if len(urls) >= 5:
+                break
+        return urls
+
+    def _schedule_auto_pipeline(
+        self,
+        *,
+        session_key: str,
+        customer_id: str,
+        acc_id: str,
+        designer_name: str,
+        requirement_text: str,
+        history: List[dict],
+        image_urls: Optional[List[str]] = None,
+    ):
+        resolved_urls = self._collect_recent_image_urls(history, image_urls)
+        if not resolved_urls:
+            logger.info(f"[Orchestrator] 自动处理跳过：未找到客户图片 user={customer_id} acc={acc_id}")
+            return
+
+        intent = self._infer_processing_intent(requirement_text, history)
+        signature_src = f"{session_key}|{designer_name}|{intent}|{'|'.join(resolved_urls)}"
+        signature = str(abs(hash(signature_src)))
+        now = time.time()
+        last_run = self._auto_pipeline_jobs.get(signature, 0.0)
+        if now - last_run < 600:
+            logger.info(f"[Orchestrator] 自动处理已在近期触发，跳过重复任务 user={customer_id} acc={acc_id}")
+            return
+        self._auto_pipeline_jobs[signature] = now
+
+        async def _runner():
+            try:
+                result = await auto_image_pipeline_service.process_and_notify(
+                    session_key=session_key,
+                    customer_id=customer_id,
+                    acc_id=acc_id,
+                    designer_name=designer_name,
+                    requirement_text=requirement_text,
+                    image_urls=resolved_urls,
+                    intent=intent,
+                )
+                logger.info(
+                    f"[Orchestrator] 自动处理完成 user={customer_id} acc={acc_id} "
+                    f"ok={result.get('success')} uploaded={len(result.get('uploaded') or [])}"
+                )
+            except Exception as e:
+                logger.warning(f"[Orchestrator] 自动处理失败 user={customer_id} acc={acc_id}: {e}")
+
+        asyncio.create_task(_runner())
+
     async def on_raw_message_received(self, platform: str, raw_data: dict):
         """链路入口"""
         try:
@@ -339,7 +437,7 @@ class SystemOrchestrator:
         except Exception as e:
             logger.warning(f"[Orchestrator] 订单消息处理异常: {e}")
 
-    async def _analyze_images_background(self, session_key: str, image_urls: List[str]):
+    async def _analyze_images_background(self, session_key: str, image_urls: List[str], requirement_text: str = ""):
         """后台静默分析图片，存入用户数据库用于数据标定"""
         try:
             from services.service_image_analyzer import image_analyzer_service
@@ -348,9 +446,9 @@ class SystemOrchestrator:
             db = CustomerDatabase()
             profile = db.get_customer(session_key)
             
-            for url in image_urls:
+            for url in (image_urls or [])[:1]:
                 try:
-                    result = await image_analyzer_service.analyze(url)
+                    result = await image_analyzer_service.analyze(url, customer_requirement=requirement_text)
                     result_json = json.dumps(result, ensure_ascii=False)
                     
                     # 更新最近一次分析
@@ -454,7 +552,7 @@ class SystemOrchestrator:
                 
                 # B2. 后台图片分析（不阻塞主流程，用于数据标定）
                 if all_image_urls:
-                    asyncio.create_task(self._analyze_images_background(session_key, all_image_urls))
+                    asyncio.create_task(self._analyze_images_background(session_key, all_image_urls, combined_content))
 
                 history_start = time.time()
                 history = await repo.get_chat_history(user_id, limit=12, acc_id=acc_id)
@@ -504,6 +602,7 @@ class SystemOrchestrator:
 
                     # 转接场景：先发一句安抚话，再发转接指令
                     if "[转移会话]" in std_res.reply_content:
+                        designer_name = self._extract_designer_name(std_res.reply_content)
                         transfer_prelude = str(std_res.metadata.get("transfer_prelude") or "").strip()
                         greet = StandardResponse(
                             reply_content=transfer_prelude or "收到，我叫设计师来看下哈",
@@ -547,6 +646,15 @@ class SystemOrchestrator:
 
                     if "[转移会话]" in std_res.reply_content:
                         self._last_transfer_time[session_key] = time.time()
+                        self._schedule_auto_pipeline(
+                            session_key=session_key,
+                            customer_id=user_id,
+                            acc_id=acc_id,
+                            designer_name=self._extract_designer_name(std_res.reply_content),
+                            requirement_text=combined_content,
+                            history=history,
+                            image_urls=all_image_urls,
+                        )
 
         except asyncio.CancelledError: pass
         except Exception as e: logger.exception(f"[Orchestrator] 处理失败: {e}")
@@ -618,6 +726,15 @@ class SystemOrchestrator:
                         )
 
                         self._last_transfer_time[f"{customer_id}@{acc_id}"] = time.time()
+                        history = await repo.get_chat_history(customer_id, limit=12, acc_id=acc_id)
+                        self._schedule_auto_pipeline(
+                            session_key=f"{customer_id}@{acc_id}",
+                            customer_id=customer_id,
+                            acc_id=acc_id,
+                            designer_name=designer_name,
+                            requirement_text=reason,
+                            history=history,
+                        )
                         await asyncio.to_thread(complete_pending_transfer, row_id)
                         logger.info(
                             f"[Orchestrator] 待转接自动完成: pending_id={row_id} user={customer_id} designer={designer_name} reason={reason}"

services/service_auto_image_pipeline.py

@@ -0,0 +1,232 @@
+import asyncio
+import hashlib
+import json
+import logging
+import os
+import re
+from pathlib import Path
+from typing import Dict, List, Optional
+from urllib.parse import urlparse
+
+import httpx
+from dotenv import load_dotenv
+
+from db.customer_db import CustomerDatabase
+from db.image_tasks_db import TaskStatus, db as task_db
+from services.service_gemini import GeminiExtractV2Service
+from services.service_tuhui_upload import upload_to_tuhui
+from services.service_wecom_bot import wecom_bot_service
+
+load_dotenv()
+logger = logging.getLogger("cs_agent")
+
+AUTO_PROCESS_PRICE = int(os.getenv("AUTO_PROCESS_DEFAULT_PRICE", "12"))
+AUTO_PROCESS_CATEGORY = os.getenv("AUTO_PROCESS_CATEGORY", "设计素材")
+AUTO_PROCESS_ROOT = Path(
+    os.getenv("AUTO_PROCESS_ROOT", str(Path(__file__).resolve().parents[1] / "runtime" / "auto_processed"))
+)
+
+
+def _safe_name(text: str, fallback: str = "image") -> str:
+    cleaned = re.sub(r"[^0-9A-Za-z\u4e00-\u9fa5_-]+", "_", str(text or "").strip())
+    cleaned = cleaned.strip("_")
+    return cleaned[:40] or fallback
+
+
+def _suffix_from_url(url: str) -> str:
+    path = urlparse(str(url or "")).path
+    suffix = Path(path).suffix.lower()
+    if suffix in {".png", ".jpg", ".jpeg", ".webp"}:
+        return suffix
+    return ".png"
+
+
+def _build_processing_prompt(intent: str, requirement_text: str, analysis: Dict) -> str:
+    base_prompt = str((analysis or {}).get("gemini_prompt") or "").strip()
+    req = str(requirement_text or "").strip()
+    if base_prompt:
+        return base_prompt
+    if intent == "repair":
+        return f"根据客户需求“{req or '高清修复'}”，保留主体和构图，做高清修复并补足细节。"
+    return f"根据客户需求“{req or '找原图'}”，严格参考原图元素与构图，生成完整干净的高质量素材图。"
+
+
+class AutoImagePipelineService:
+    def __init__(self):
+        self.customer_db = CustomerDatabase()
+
+    async def _download_image(self, image_url: str, dest_path: Path) -> Path:
+        dest_path.parent.mkdir(parents=True, exist_ok=True)
+        async with httpx.AsyncClient(timeout=60.0, follow_redirects=True) as client:
+            response = await client.get(image_url)
+            response.raise_for_status()
+        dest_path.write_bytes(response.content)
+        return dest_path
+
+    @staticmethod
+    def _format_transfer_notice(
+        customer_id: str,
+        acc_id: str,
+        designer_name: str,
+        requirement_text: str,
+        intent: str,
+        image_urls: List[str],
+    ) -> str:
+        lines = [
+            "【AI自动转设计师】",
+            f"店铺：{acc_id or '-'}",
+            f"客户：{customer_id or '-'}",
+            f"设计师：{designer_name or '-'}",
+            f"需求：{requirement_text or '-'}",
+            f"类型：{'高清修复' if intent == 'repair' else '找原图'}",
+            f"默认价格：{AUTO_PROCESS_PRICE}元",
+        ]
+        if image_urls:
+            lines.append("原图URL：")
+            lines.extend(image_urls[:5])
+        return "\n".join(lines)
+
+    @staticmethod
+    def _format_finish_notice(
+        customer_id: str,
+        acc_id: str,
+        designer_name: str,
+        links: List[Dict[str, str]],
+        failures: List[str],
+    ) -> str:
+        lines = [
+            "【AI处理完成】",
+            f"店铺：{acc_id or '-'}",
+            f"客户：{customer_id or '-'}",
+            f"设计师：{designer_name or '-'}",
+            f"默认价格：{AUTO_PROCESS_PRICE}元",
+        ]
+        if links:
+            lines.append("处理结果：")
+            for idx, item in enumerate(links, 1):
+                lines.append(f"{idx}. 图绘链接：{item.get('download_url') or '-'}")
+                lines.append(f"   原图URL：{item.get('source_url') or '-'}")
+        if failures:
+            lines.append("失败项：")
+            lines.extend(failures[:5])
+        return "\n".join(lines)
+
+    async def process_and_notify(
+        self,
+        *,
+        session_key: str,
+        customer_id: str,
+        acc_id: str,
+        designer_name: str,
+        requirement_text: str,
+        image_urls: List[str],
+        intent: str = "",
+    ) -> Dict:
+        image_urls = [str(url).strip() for url in (image_urls or []) if str(url).strip()]
+        if not image_urls:
+            return {"success": False, "message": "no_images"}
+        image_urls = image_urls[:1]
+
+        profile = self.customer_db.get_customer(session_key)
+        analysis = {}
+        if getattr(profile, "last_image_analysis", ""):
+            try:
+                analysis = json.loads(profile.last_image_analysis)
+            except Exception:
+                analysis = {}
+
+        if not intent:
+            intent = "repair" if "修复" in requirement_text else "find_original"
+
+        await wecom_bot_service.send_text(
+            self._format_transfer_notice(
+                customer_id=customer_id,
+                acc_id=acc_id,
+                designer_name=designer_name,
+                requirement_text=requirement_text,
+                intent=intent,
+                image_urls=image_urls,
+            )
+        )
+
+        pipeline_root = AUTO_PROCESS_ROOT / _safe_name(customer_id, "customer")
+        pipeline_root.mkdir(parents=True, exist_ok=True)
+        gemini_service = GeminiExtractV2Service()
+        uploaded_links: List[Dict[str, str]] = []
+        failures: List[str] = []
+
+        for idx, image_url in enumerate(image_urls, 1):
+            digest = hashlib.md5(f"{customer_id}|{acc_id}|{image_url}".encode("utf-8")).hexdigest()[:10]
+            input_path = pipeline_root / f"{digest}_src{_suffix_from_url(image_url)}"
+            output_path = pipeline_root / f"{digest}_out.png"
+            title = f"{_safe_name(customer_id, '客户')}_{'修复' if intent == 'repair' else '原图'}_{idx}"
+            prompt = _build_processing_prompt(intent, requirement_text, analysis)
+            task_id = task_db.add_task(
+                customer_id=customer_id,
+                platform="qianniu",
+                original_image=image_url,
+                operation=intent or "auto_process",
+                requirements=requirement_text,
+                status=TaskStatus.PROCESSING.value,
+            )
+            try:
+                await self._download_image(image_url, input_path)
+                success, message, data = await gemini_service.extract_pattern(
+                    str(input_path),
+                    str(output_path),
+                    custom_prompt=prompt,
+                    aspect_ratio=str((analysis or {}).get("aspect_ratio") or "1:1"),
+                )
+                if not success or not output_path.exists():
+                    if task_id:
+                        task_db.update_status(task_id, TaskStatus.FAILED.value)
+                    failures.append(f"{idx}. Gemini失败：{message}")
+                    continue
+
+                upload_result = await upload_to_tuhui(
+                    image_path=str(output_path),
+                    title=title,
+                    description=requirement_text or prompt[:120],
+                    price=AUTO_PROCESS_PRICE,
+                    category=AUTO_PROCESS_CATEGORY,
+                    tags="AI处理,自动转接",
+                    designer_name=designer_name,
+                )
+                if not upload_result.success:
+                    if task_id:
+                        task_db.update_status(task_id, TaskStatus.FAILED.value)
+                    failures.append(f"{idx}. 图绘上传失败：{upload_result.message}")
+                    continue
+
+                if task_id:
+                    task_db.update_status(task_id, TaskStatus.COMPLETED.value, upload_result.download_url)
+                uploaded_links.append(
+                    {
+                        "download_url": upload_result.download_url,
+                        "source_url": image_url,
+                        "work_id": str(upload_result.work_id),
+                    }
+                )
+            except Exception as e:
+                if task_id:
+                    task_db.update_status(task_id, TaskStatus.FAILED.value)
+                failures.append(f"{idx}. 处理异常：{e}")
+
+        await wecom_bot_service.send_text(
+            self._format_finish_notice(
+                customer_id=customer_id,
+                acc_id=acc_id,
+                designer_name=designer_name,
+                links=uploaded_links,
+                failures=failures,
+            )
+        )
+
+        return {
+            "success": bool(uploaded_links),
+            "uploaded": uploaded_links,
+            "failures": failures,
+        }
+
+
+auto_image_pipeline_service = AutoImagePipelineService()

services/service_gemini.py

@@ -1,105 +1,120 @@
 #!/usr/bin/env python3
-"""
+"""Gemini 出图服务。固定走老张 Gemini 原生出图接口。"""
-Gemini印花提取V2服务 - 使用服务
-更经济的选择：1.4毛/张
-"""
 
 import asyncio
-import aiohttp
 import base64
-import json
-import re
-import os
-import time
-from datetime import datetime
-from pathlib import Path
 import logging
+import mimetypes
+import os
+from typing import Dict
+
+import aiohttp
 from dotenv import load_dotenv
+
 from utils.metrics_tracker import emit as metrics_emit
-
-
 from utils.service_base import BaseService
 
 logger = logging.getLogger(__name__)
 load_dotenv()
-GEMINI_IMAGE_MODEL = os.getenv("GEMINI_IMAGE_MODEL", "gemini-3.1-flash-image-preview")
+
-GEMINI_IMAGE_FALLBACK_MODEL = os.getenv("GEMINI_IMAGE_FALLBACK_MODEL", "gemini-2.5-flash-image")
+GEMINI_API_KEY = os.getenv(
-GEMINI_IMAGE_SIZE = os.getenv("GEMINI_IMAGE_SIZE", "1K")
+    "GEMINI_API_KEY",
-GEMINI_THINKING_LEVEL = os.getenv("GEMINI_THINKING_LEVEL", "MINIMAL")
+    "sk-8i7uYE0RtnQwDImV8a5f7014DcAb46F6BcEb72Df92218aC8",
+)
+GEMINI_IMAGE_MODEL = os.getenv("GEMINI_IMAGE_MODEL", "gemini-3-pro-image-preview")
+GEMINI_IMAGE_SIZE = os.getenv("GEMINI_IMAGE_SIZE", "2K")
 GEMINI_PERSON_GENERATION = os.getenv("GEMINI_PERSON_GENERATION", "")
+GEMINI_THINKING_LEVEL = os.getenv("GEMINI_THINKING_LEVEL", "MINIMAL")
 
 
 class GeminiExtractV2Service(BaseService):
-    """Gemini印花提取V2服务类 - 使用服务，更经济"""
+    """固定单接口的 Gemini 出图服务。"""
 
     SERVICE_NAME = "gemini_extract_v2"
+    API_BASE_URL = "https://api.laozhang.ai/v1beta/models"
+    DEFAULT_PROMPT = (
+        "提取印花图案，把褶皱移除。补齐缺失的部分，要生成完整，细节丰富，"
+        "严格按照原图的元素位置生成平面的印花图，不要相似的，相似度要100%，生成高质量的印刷图"
+    )
 
-    # 多API配置，按优先级排序（便宜的优先使用）
-    API_CONFIGS = [
-
-
-
-
-        # {
-        #     "name": "西风接口$0.003逆向",
-        #     "api_key": "sk-UT9aupbfHI4rc3RUn8x5D8gN5Kk31yvLZQu8M3BCY5Nja1Fc",
-        #     "api_url": "https://api.apiqik.com/v1/chat/completions" ,
-        #     "api_model": "gemini-2.5-flash-image",
-        #     "max_retries": 3,  # 贵接口少重试
-        #     "cost": "低"
-        # },
-        
-
-        {
-            "name": "西风接口$0.014",
-            "api_key": "sk-uRuvzLfIHsc3BiHZ2cyebk0cYsZ8NR9rLL326QqXCKIy9EpK",
-            "api_url": "https://api.apiqik.online/v1beta/models",
-            "api_model": GEMINI_IMAGE_MODEL,
-            "max_retries": 2,
-            "cost": "中",
-            "use_gemini_format": True  # 使用Gemini原生API格式
-        },
-        {
-            "name": "西风接口Fallback",
-            "api_key": "sk-uRuvzLfIHsc3BiHZ2cyebk0cYsZ8NR9rLL326QqXCKIy9EpK",
-            "api_url": "https://api.apiqik.online/v1beta/models",
-            "api_model": GEMINI_IMAGE_FALLBACK_MODEL,
-            "max_retries": 1,
-            "cost": "中",
-            "use_gemini_format": True
-        },
-
-        {
-            "name": "最贵的",
-            "api_key": "sk-8i7uYE0RtnQwDImV8a5f7014DcAb46F6BcEb72Df92218aC8",
-            "api_url": "https://api.laozhang.ai/v1/chat/completions", 
-            "api_model": GEMINI_IMAGE_MODEL,
-            "max_retries": 1,
-            "cost": "高"
-        }
-    ]
-
-    # 默认提示词
-    DEFAULT_PROMPT = "提取印花图案，把褶皱移除。补齐缺失的部分，要生成完整，细节丰富，严格按照原图的元素位置生成平面的印花图，不要相似的，相似度要100%，生成高质量的印刷图"
-  #  DEFAULT_PROMPT = "生成图片，把衣服的图案展开起来做成数码印花印刷平面图。去掉皱褶，生成图案增强细节。排除衣服图案以外内容"
     def __init__(self):
-        super().__init__(name="gemini_extract_v2")
+        super().__init__(name=self.SERVICE_NAME)
-        self.session = None
 
-    def image_to_base64(self, image_path: str) -> str:
+    @staticmethod
-        """将图片文件转换为base64编码字符串"""
+    def _image_to_base64(image_path: str) -> str:
-        try:
         if not os.path.exists(image_path):
             logger.error(f"文件不存在: {image_path}")
-                return None
+            return ""
-            
+        try:
             with open(image_path, "rb") as image_file:
-                encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
+                return base64.b64encode(image_file.read()).decode("utf-8")
-                return encoded_string
-        
         except Exception as e:
             logger.error(f"Base64转换失败: {e}")
-            return None
+            return ""
+
+    @staticmethod
+    def _guess_mime_type(image_path: str) -> str:
+        mime_type, _ = mimetypes.guess_type(str(image_path))
+        return mime_type or "image/png"
+
+    @staticmethod
+    def _build_generation_config(
+        aspect_ratio: str,
+        image_size: str,
+        person_generation: str,
+        thinking_level: str,
+    ) -> Dict:
+        valid_ratios = {"1:1", "9:16", "16:9", "3:4", "4:3", "3:2", "2:3", "5:4", "4:5"}
+        valid_sizes = {"1K", "2K", "4K"}
+        valid_thinking = {"MINIMAL", "LOW", "MEDIUM", "HIGH"}
+
+        image_config = {}
+        if aspect_ratio in valid_ratios:
+            image_config["aspectRatio"] = aspect_ratio
+        size_val = (image_size or GEMINI_IMAGE_SIZE or "").upper().strip()
+        if size_val in valid_sizes:
+            image_config["imageSize"] = size_val
+        person_val = (person_generation or GEMINI_PERSON_GENERATION or "").strip()
+        if person_val:
+            image_config["personGeneration"] = person_val
+
+        generation_config = {"responseModalities": ["IMAGE"]}
+        if image_config:
+            generation_config["imageConfig"] = image_config
+
+        thinking_val = (thinking_level or GEMINI_THINKING_LEVEL or "").upper().strip()
+        if thinking_val in valid_thinking:
+            generation_config["thinkingConfig"] = {"thinkingLevel": thinking_val}
+
+        return generation_config
+
+    @staticmethod
+    def _extract_image_bytes(result: Dict) -> bytes:
+        candidates = result.get("candidates") or []
+        if not candidates:
+            raise ValueError("响应缺少 candidates")
+        parts = ((candidates[0] or {}).get("content") or {}).get("parts") or []
+        for part in parts:
+            inline_data = part.get("inlineData") or {}
+            encoded = inline_data.get("data")
+            if encoded:
+                return base64.b64decode(encoded)
+        finish_reason = candidates[0].get("finishReason") or ""
+        if finish_reason == "NO_IMAGE":
+            raise ValueError("模型未返回图片(NO_IMAGE)")
+        raise ValueError("响应中未找到 inlineData 图片")
+
+    @staticmethod
+    def _save_image(image_data: bytes, output_path: str) -> Dict:
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+        with open(output_path, "wb") as f:
+            f.write(image_data)
+        file_size = os.path.getsize(output_path)
+        return {
+            "output_path": output_path,
+            "file_size": file_size,
+            "api_used": "laozhang_gemini_native",
+        }
 
     async def extract_pattern(
         self,
@@ -111,385 +126,68 @@ class GeminiExtractV2Service(BaseService):
         person_generation: str = "",
         thinking_level: str = "",
     ) -> tuple[bool, str, dict]:
-        """
+        img64 = self._image_to_base64(input_path)
-        使用多API配置进行印花图案提取
-        
-        Args:
-            input_path: 输入图片路径
-            output_path: 输出图片路径
-            custom_prompt: 自定义提示词
-        
-        Returns:
-            tuple: (success, message, data)
-        """
-        # 转换图片为Base64
-        img64 = self.image_to_base64(input_path)
         if not img64:
             return False, "图片编码失败", {}
 
-        # 使用自定义提示词或默认提示词
+        prompt = str(custom_prompt or self.DEFAULT_PROMPT).strip()
-        prompt = custom_prompt or self.DEFAULT_PROMPT
+        api_url = f"{self.API_BASE_URL}/{GEMINI_IMAGE_MODEL}:generateContent"
-        
-        # 按优先级逐个尝试API配置
-        for config_index, config in enumerate(self.API_CONFIGS):
-            logger.info(f"尝试使用API: {config['name']} (成本: {config['cost']})")
-            metrics_emit("gemini_request", model=config.get("api_model", ""), provider=config.get("name", ""))
-            
-            # 对每个API配置进行重试
-            for attempt in range(config['max_retries']):
-                try:
-                    logger.info(f"开始Gemini V2印花提取 - {config['name']} (第{attempt + 1}/{config['max_retries']}次尝试): {input_path}")
-                    
-                    # 准备请求数据和URL
-                    if config.get('use_gemini_format', False):
-                        # Gemini原生API格式
-                        api_url = f"{config['api_url']}/{config['api_model']}:generateContent?key={config['api_key']}"
         headers = {
-                            "Content-Type": "application/json"
+            "Authorization": f"Bearer {GEMINI_API_KEY}",
+            "Content-Type": "application/json",
         }
-
+        payload = {
-                        # 有效比例列表（Auto 不传 aspectRatio）
-                        valid_ratios = {"1:1", "9:16", "16:9", "3:4", "4:3", "3:2", "2:3", "5:4", "4:5"}
-                        valid_sizes = {"1K", "2K", "4K"}
-                        valid_thinking = {"MINIMAL", "LOW", "MEDIUM", "HIGH"}
-                        image_config = {}
-                        if aspect_ratio in valid_ratios:
-                            image_config["aspectRatio"] = aspect_ratio
-                        size_val = (image_size or GEMINI_IMAGE_SIZE or "").upper().strip()
-                        if size_val in valid_sizes:
-                            image_config["imageSize"] = size_val
-                        person_val = (person_generation or GEMINI_PERSON_GENERATION or "").strip()
-                        if person_val:
-                            # 中转接口若支持该字段会生效；不设置时不发送，保证兼容
-                            image_config["personGeneration"] = person_val
-                        thinking_val = (thinking_level or GEMINI_THINKING_LEVEL or "").upper().strip()
-                        thinking_config = {}
-                        if thinking_val in valid_thinking:
-                            thinking_config["thinkingLevel"] = thinking_val
-
-                        data = {
             "contents": [
                 {
-                                    "role": "user",
                     "parts": [
-                                        {
+                        {"inlineData": {"mimeType": self._guess_mime_type(input_path), "data": img64}},
-                                            "inlineData": {
+                        {"text": prompt},
-                                                "mimeType": "image/jpeg",
-                                                "data": img64
-                                            }
-                                        },
-                                        {
-                                            "text": prompt
-                                        }
                     ]
                 }
             ],
-                            "generationConfig": {
+            "generationConfig": self._build_generation_config(
-                                "responseModalities": ["IMAGE"],
+                aspect_ratio=aspect_ratio,
-                                **({"imageConfig": image_config} if image_config else {}),
+                image_size=image_size,
-                                **({"thinkingConfig": thinking_config} if thinking_config else {}),
+                person_generation=person_generation,
-                            }
+                thinking_level=thinking_level,
-                        }
+            ),
-                        logger.info(
-                            f"Gemini 生成配置: 比例={aspect_ratio} 尺寸={image_config.get('imageSize', '默认')} "
-                            f"person={image_config.get('personGeneration', '默认')} thinking={thinking_config.get('thinkingLevel', '默认')}"
-                        )
-                    else:
-                        # OpenAI兼容格式
-                        api_url = config['api_url']
-                        headers = {
-                            "Authorization": f"Bearer {config['api_key']}",
-                            "Content-Type": "application/json"
         }
 
-                        data = {
+        metrics_emit("gemini_request", model=GEMINI_IMAGE_MODEL, provider="laozhang_gemini_native")
-                            "model": config['api_model'],
-                            "stream": False,
-                            "messages": [
-                                {
-                                    "role": "user",
-                                    "content": [
-                                        {
-                                            "type": "text",
-                                            "text": prompt
-                                        },
-                                        {
-                                            "type": "image_url",
-                                            "image_url": {
-                                                "url": f"data:image/png;base64,{img64}"
-                                            }
-                                        }
-                                    ]
-                                }
-                            ]
-                        }
-                    
-                    logger.info(f"正在请求{config['name']}服务 (第{attempt + 1}次)...")
-                    
-                    # 发送异步请求
         timeout = aiohttp.ClientTimeout(total=300, connect=30)
-                    connector = aiohttp.TCPConnector(limit=10, limit_per_host=5)
 
+        for attempt in range(1, 3):
             try:
-                        async with aiohttp.ClientSession(timeout=timeout, connector=connector) as session:
+                logger.info(f"Gemini 出图开始 attempt={attempt}/2 model={GEMINI_IMAGE_MODEL} input={input_path}")
-                            async with session.post(api_url, headers=headers, json=data) as response:
+                async with aiohttp.ClientSession(timeout=timeout) as session:
+                    async with session.post(api_url, headers=headers, json=payload) as response:
                         if response.status != 200:
                             error_text = await response.text()
-                                    logger.error(f"{config['name']} API请求失败 (第{attempt + 1}次): {response.status} - {error_text}")
+                            logger.error(f"Gemini API请求失败 attempt={attempt}: {response.status} - {error_text}")
-                                    
+                            if attempt < 2:
-                                    # 如果是当前API配置的最后一次重试
+                                await asyncio.sleep(attempt)
-                                    if attempt == config['max_retries'] - 1:
-                                        logger.warning(f"{config['name']} 所有重试已用完，切换到下一个API配置")
-                                        break
-                                    
-                                    # 当前API配置内部重试
-                                    base_wait_time = 2
-                                    wait_time = base_wait_time * (attempt + 1)
-                                    logger.info(f"等待{wait_time}秒后重试{config['name']}...")
-                                    await asyncio.sleep(wait_time)
                                 continue
-                                
+                            return False, f"Gemini API请求失败: {response.status}", {}
                         result = await response.json()
-                                # Gemini 偶发只返回文本不返回图片：NO_IMAGE 时快速重试/降级
-                                if config.get('use_gemini_format', False):
-                                    finish_reason = ""
-                                    try:
-                                        finish_reason = (
-                                            (result.get("candidates") or [{}])[0].get("finishReason", "")
-                                        )
-                                    except Exception:
-                                        finish_reason = ""
-                                    if finish_reason == "NO_IMAGE":
-                                        logger.warning(
-                                            f"{config['name']} 返回 NO_IMAGE (模型={config.get('api_model')})，第{attempt + 1}次"
-                                        )
-                                        metrics_emit("gemini_no_image", model=config.get("api_model", ""), provider=config.get("name", ""))
-                                        if attempt == config['max_retries'] - 1:
-                                            logger.warning(f"{config['name']} NO_IMAGE 重试已用完，切换下一个配置")
-                                            break
-                                        await asyncio.sleep(1 + attempt)
-                                        continue
 
-                    except (aiohttp.ClientError, asyncio.TimeoutError, AssertionError) as e:
+                image_bytes = self._extract_image_bytes(result)
-                        logger.error(f"{config['name']} 网络连接错误 (第{attempt + 1}次): {str(e)}")
+                data = self._save_image(image_bytes, output_path)
-                        
+                metrics_emit("gemini_success", model=GEMINI_IMAGE_MODEL, provider="laozhang_gemini_native")
-                        # 如果是当前API配置的最后一次重试
-                        if attempt == config['max_retries'] - 1:
-                            logger.warning(f"{config['name']} 网络重试已用完，切换到下一个API配置")
-                            break
-                        
-                        # 当前API配置内部重试
-                        base_wait_time = 2
-                        wait_time = base_wait_time * (attempt + 1)
-                        logger.info(f"等待{wait_time}秒后重试{config['name']}...")
-                        await asyncio.sleep(wait_time)
-                        continue
-                    
-                    logger.info(f"{config['name']} 服务请求成功 (第{attempt + 1}次)，正在处理响应...")
-                    
-                    # 处理API响应并提取图片
-                    success, message, data = await self._process_api_response(result, output_path, config['name'], config)
-                    
-                    if success:
-                        logger.info(f"使用 {config['name']} 成功完成印花提取")
-                        metrics_emit("gemini_success", model=config.get("api_model", ""), provider=config.get("name", ""))
                 try:
                     from utils.api_cost_tracker import record
+
                     record("gemini_extract", count=1)
                 except Exception:
                     pass
-                        return True, f"Gemini V2印花提取完成 - 使用{config['name']}", data
+                return True, "Gemini 出图完成", data
-                    else:
+            except Exception as e:
-                        logger.warning(f"{config['name']} 响应处理失败: {message}")
+                logger.error(f"Gemini 出图异常 attempt={attempt}: {e}")
-                        
+                if attempt < 2:
-                        # 如果是当前API配置的最后一次重试
+                    await asyncio.sleep(attempt)
-                        if attempt == config['max_retries'] - 1:
-                            logger.warning(f"{config['name']} 所有重试已用完，切换到下一个API配置")
-                            break
-                        
-                        # 当前API配置内部重试
-                        base_wait_time = 2
-                        wait_time = base_wait_time * (attempt + 1)
-                        logger.info(f"等待{wait_time}秒后重试{config['name']}...")
-                        await asyncio.sleep(wait_time)
                     continue
+                return False, f"Gemini 出图失败: {e}", {}
 
-                except Exception as e:
+        return False, "Gemini 出图失败", {}
-                    logger.error(f"{config['name']} API调用异常 (第{attempt + 1}次): {str(e)}")
-                    
-                    # 如果是当前API配置的最后一次重试
-                    if attempt == config['max_retries'] - 1:
-                        logger.warning(f"{config['name']} 异常重试已用完，切换到下一个API配置")
-                        break
-                    
-                    # 当前API配置内部重试
-                    base_wait_time = 2
-                    wait_time = base_wait_time * (attempt + 1)
-                    logger.info(f"等待{wait_time}秒后重试{config['name']}...")
-                    await asyncio.sleep(wait_time)
-                    continue
-        
-        # 所有API配置都尝试过了，返回失败
-        return False, "所有API配置都已尝试失败", {}
-
-    async def _process_api_response(self, result: dict, output_path: str, api_name: str, config: dict) -> tuple[bool, str, dict]:
-        """处理API响应并提取图片"""
-        try:
-            # 根据API格式提取内容
-            if config.get('use_gemini_format', False):
-                # Gemini原生API格式: candidates[0].content.parts[0]
-                content_parts = result['candidates'][0]['content']['parts']
-                
-                # 查找包含图片数据的part
-                image_data = None
-                for part in content_parts:
-                    # 注意：响应中使用驼峰命名 inlineData
-                    if 'inlineData' in part:
-                        # 提取Base64图片数据
-                        base64_data = part['inlineData']['data']
-                        logger.info(f"{api_name} 找到Gemini格式的inlineData图片")
-                        try:
-                            image_data = base64.b64decode(base64_data)
-                            break
-                        except Exception as e:
-                            logger.error(f"{api_name} Base64解码失败: {e}")
-                            return False, f"Base64解码失败: {e}", {}
-                
-                if not image_data:
-                    logger.error(f"{api_name} 在Gemini响应中未找到图片数据")
-                    return False, "未找到图片数据", {}
-                
-                # 直接保存图片
-                return await self._save_image(image_data, output_path, api_name)
-                
-            else:
-                # OpenAI兼容格式: choices[0].message.content
-                content = result['choices'][0]['message']['content']
-                logger.info(f"{api_name} 收到内容: {content[:200]}...")
-                
-                # 使用原有的URL/Base64提取逻辑
-                return await self._extract_and_save_image(content, output_path, api_name)
-                
-        except KeyError as e:
-            logger.error(f"{api_name} 响应格式不正确，缺少字段: {e}")
-            logger.error(f"响应内容: {json.dumps(result, ensure_ascii=False)[:500]}")
-            return False, f"响应格式错误: {e}", {}
-        except Exception as e:
-            logger.error(f"{api_name} 处理响应时发生异常: {e}")
-            return False, f"处理异常: {e}", {}
-    
-    async def _save_image(self, image_data: bytes, output_path: str, api_name: str) -> tuple[bool, str, dict]:
-        """保存图片文件"""
-        try:
-            os.makedirs(os.path.dirname(output_path), exist_ok=True)
-            
-            with open(output_path, 'wb') as f:
-                f.write(image_data)
-            
-            logger.info(f"{api_name} 图片已保存到: {output_path}")
-            
-            # 验证保存的图片
-            if os.path.exists(output_path) and os.path.getsize(output_path) > 0:
-                file_size = os.path.getsize(output_path)
-                logger.info(f"{api_name} 图片保存成功，文件大小: {file_size} bytes")
-                
-                return True, f"{api_name} 印花提取完成", {
-                    'output_path': output_path,
-                    'file_size': file_size,
-                    'api_used': api_name
-                }
-            else:
-                logger.error(f"{api_name} 保存的图片文件无效")
-                return False, "保存的图片文件无效", {}
-                
-        except Exception as e:
-            logger.error(f"{api_name} 保存图片时发生错误: {e}")
-            return False, f"保存图片失败: {e}", {}
-    
-    async def _extract_and_save_image(self, content: str, output_path: str, api_name: str) -> tuple[bool, str, dict]:
-        """从响应内容中提取并保存图片（URL或Base64格式）"""
-        # 查找和处理图片数据
-        image_data = None
-        
-        # 方法1: 查找URL链接 (优先检查URL格式)
-        url_match = re.search(r'https?://[^\s\)]+\.(?:png|jpg|jpeg|gif|webp)', content)
-        if url_match:
-            image_url = url_match.group(0)
-            logger.info(f"{api_name} 找到图片URL: {image_url}")
-            
-            # 图片下载重试机制
-            download_retries = 3
-            for download_attempt in range(download_retries):
-                try:
-                    logger.info(f"{api_name} 开始下载图片 (第{download_attempt + 1}/{download_retries}次尝试): {image_url}")
-                    
-                    # 异步下载图片，增加超时时间
-                    timeout = aiohttp.ClientTimeout(total=300, connect=60)
-                    connector = aiohttp.TCPConnector(limit=5, limit_per_host=2)
-                    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}
-                    
-                    async with aiohttp.ClientSession(
-                        timeout=timeout,
-                        connector=connector,
-                        headers=headers
-                    ) as download_session:
-                        logger.info(f"{api_name} 正在发送HTTP请求...")
-                        async with download_session.get(image_url) as img_response:
-                            logger.info(f"{api_name} 收到HTTP响应: {img_response.status}")
-                            if img_response.status == 200:
-                                image_data = await img_response.read()
-                                logger.info(f"{api_name} 图片下载成功，大小: {len(image_data)} bytes")
-                                break  # 成功则跳出重试循环
-                            else:
-                                logger.error(f"{api_name} 图片下载失败，HTTP状态码: {img_response.status}")
-                                if download_attempt == download_retries - 1:
-                                    return False, "图片下载失败", {}
-                                else:
-                                    await asyncio.sleep(2)
-                                    continue
-                
-                except Exception as e:
-                    logger.error(f"{api_name} 下载图片时发生异常 (第{download_attempt + 1}次): {type(e).__name__}: {str(e)}")
-                    if download_attempt == download_retries - 1:
-                        return False, f"图片下载异常: {str(e)}", {}
-                    else:
-                        await asyncio.sleep(2)
-                        continue
-        
-        else:
-            # 方法2: 查找标准格式 data:image/type;base64,data
-            base64_match = re.search(r'data:image/[^;]+;base64,([A-Za-z0-9+/=]+)', content)
-            
-            if base64_match:
-                base64_data = base64_match.group(1)
-                logger.info(f"{api_name} 找到标准格式的Base64数据")
-                try:
-                    image_data = base64.b64decode(base64_data)
-                except Exception as e:
-                    logger.error(f"{api_name} Base64解码失败: {e}")
-                    return False, f"Base64解码失败: {e}", {}
-            else:
-                # 方法3: 查找纯Base64数据（长字符串）
-                base64_match = re.search(r'([A-Za-z0-9+/=]{100,})', content)
-                if base64_match:
-                    base64_data = base64_match.group(1)
-                    logger.info(f"{api_name} 找到纯Base64数据")
-                    try:
-                        image_data = base64.b64decode(base64_data)
-                    except Exception as e:
-                        logger.error(f"{api_name} Base64解码失败: {e}")
-                        return False, f"Base64解码失败: {e}", {}
-                else:
-                    logger.error(f"{api_name} 在响应中未找到图片数据")
-                    return False, "未找到图片数据", {}
-        
-        # 检查图片数据
-        if not image_data:
-            logger.error(f"{api_name} 图片数据为空")
-            return False, "图片数据为空", {}
-        
-        # 保存图片
-        return await self._save_image(image_data, output_path, api_name)
 
     async def correct_perspective(
         self,
@@ -497,29 +195,16 @@ class GeminiExtractV2Service(BaseService):
         output_path: str,
         level: str = "mild",
     ) -> tuple[bool, str, dict]:
-        """
-        透视矫正：先把有透视畸变的图还原为正面平铺视图，再做后续处理。
-
-        Args:
-            input_path:  本地图片路径
-            output_path: 矫正后输出路径
-            level:       "mild" 或 "strong"
-        """
         if level == "strong":
             prompt = (
-                "这张图存在明显透视畸变（俯拍/斜拍/贴墙）。"
+                "这张图存在明显透视畸变。请把主体矫正为正面平铺视图，"
-                "请对图片进行透视矫正：将主体变换为正面平铺视图，"
+                "所有边缘尽量水平或垂直，保持图案颜色和细节不变，只做几何矫正。"
-                "使所有边缘变成水平或垂直，去除梯形形变，"
-                "保持图案颜色和细节完全不变，只矫正几何形状，输出矫正后的完整图片。"
             )
         else:
             prompt = (
-                "这张图存在轻微透视畸变（衣物悬挂/桌面斜拍）。"
+                "这张图存在轻微透视畸变。请做轻度透视矫正，"
-                "请做轻度透视矫正：将主体调整为尽量正视角，"
+                "消除斜拍拉伸感，保持图案颜色和细节不变。"
-                "消除轻微的梯形拉伸感，保持图案颜色和细节不变，输出矫正后的图片。"
             )
-
-        # 透视矫正使用 1:1 比例避免比例失真
         return await self.extract_pattern(
             input_path=input_path,
             output_path=output_path,
@@ -528,40 +213,17 @@ class GeminiExtractV2Service(BaseService):
         )
 
     async def cleanup(self):
-        """清理资源"""
+        return None
-        if self.session and not self.session.closed:
+
-            await self.session.close()
 
-# 便捷函数
 async def extract_pattern_v2(
     input_path: str,
     output_path: str,
     custom_prompt: str = None,
     aspect_ratio: str = "1:1",
 ) -> tuple[bool, str, dict]:
-    """Gemini V2印花提取便捷函数"""
     service = GeminiExtractV2Service()
     try:
         return await service.extract_pattern(input_path, output_path, custom_prompt, aspect_ratio)
     finally:
         await service.cleanup()
-
-if __name__ == "__main__":
-    # 测试代码
-    import asyncio
-    
-    async def test():
-        service = GeminiExtractV2Service()
-        
-        input_path = "F:/api/134.png"
-        output_path = "test_output_v2.png"
-        
-        success, message, data = await service.extract_pattern(input_path, output_path)
-        
-        print(f"结果: {success}")
-        print(f"消息: {message}")
-        print(f"数据: {data}")
-        
-        await service.cleanup()
-    
-    asyncio.run(test())

services/service_image_analyzer.py

@@ -20,6 +20,13 @@ logger = logging.getLogger("cs_agent")
 
 
 ANALYSIS_PROMPT = """你是一个电商图片处理评估专家。
+客户需求如下：
+{customer_requirement}
+
+请结合客户需求和图片内容一起判断，不要只看图片本身。
+如果客户明确说了“找原图/找图/素材/大图”，类型优先判断为“找原图/素材提取”类；
+如果客户明确说了“修复/高清/清晰/放大”，类型优先判断为“高清修复”类。
+
 请仔细分析这张图片，输出以下字段，每行一个，不要多余内容：
 
 敏感内容: <yes|no>
@@ -101,7 +108,7 @@ class ImageAnalyzerService:
             logger.debug(f"[ImageAnalyzer] 获取尺寸失败: {e}")
             return (0, 0)
 
-    async def analyze(self, image_url: str) -> Dict[str, Any]:
+    async def analyze(self, image_url: str, customer_requirement: str = "") -> Dict[str, Any]:
         """
         异步分析图片，返回结构化结果
         
@@ -133,7 +140,8 @@ class ImageAnalyzerService:
             return self._fallback(image_url, "未配置 API Key")
 
         # 缓存检查
-        cache_key = image_url
+        customer_requirement = str(customer_requirement or "").strip()
+        cache_key = f"{image_url}|{customer_requirement}"
         now = time.monotonic()
         cached = self._analysis_cache.get(cache_key)
         if cached:
@@ -149,6 +157,9 @@ class ImageAnalyzerService:
         try:
             client = AsyncOpenAI(base_url=self.base_url, api_key=self.api_key)
             
+            prompt_text = ANALYSIS_PROMPT.format(
+                customer_requirement=customer_requirement or "未提供明确补充需求"
+            )
             response = await asyncio.wait_for(
                 client.chat.completions.create(
                     model=self.vision_model,
@@ -156,7 +167,7 @@ class ImageAnalyzerService:
                         "role": "user",
                         "content": [
                             {"type": "image_url", "image_url": {"url": image_url}},
-                            {"type": "text", "text": ANALYSIS_PROMPT}
+                            {"type": "text", "text": prompt_text}
                         ]
                     }],
                     max_tokens=500
@@ -170,6 +181,7 @@ class ImageAnalyzerService:
             elapsed = time.monotonic() - start
             
             result = self._parse_result(image_url, content)
+            result["customer_requirement"] = customer_requirement
             result["elapsed"] = round(elapsed, 2)
             
             # 获取尺寸
@@ -241,6 +253,7 @@ class ImageAnalyzerService:
             
         return {
             "url": url,
+            "customer_requirement": "",
             "complexity": complexity,
             "reason": extract("原因"),
             "subject": extract("主体"),
@@ -280,6 +293,7 @@ class ImageAnalyzerService:
         from datetime import datetime
         return {
             "url": url,
+            "customer_requirement": "",
             "complexity": "normal",
             "reason": reason,
             "subject": "",

services/service_tuhui_upload.py

@@ -7,8 +7,9 @@ import os
 import httpx
 import logging
 import mimetypes
+from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional, Tuple
+from typing import Iterator, Optional
 from dotenv import load_dotenv
 
 logger = logging.getLogger(__name__)
@@ -17,11 +18,41 @@ load_dotenv()
 # 图绘平台配置
 TUHUI_BASE_URL = os.getenv("TUHUI_BASE_URL", "https://tuhui.cloud")
 TUHUI_FALLBACK_BASE_URL = "https://tuhui.cloud"
+TUHUI_WEB_BASE_URL = os.getenv("TUHUI_WEB_BASE_URL", "https://tuhui.cloud").rstrip("/")
 TUHUI_PHONE = os.getenv("TUHUI_PHONE", "17520145271")  # 图绘账号手机号
 TUHUI_PASSWORD = os.getenv("TUHUI_PASSWORD", "zuowei1216")  # 图绘账号密码
 TUHUI_DEFAULT_PRICE = int(os.getenv("TUHUI_DEFAULT_PRICE", "20"))  # 默认定价（元）
 TUHUI_DEFAULT_CATEGORY = os.getenv("TUHUI_DEFAULT_CATEGORY", "设计素材")
 
+
+@dataclass
+class TuhuiUploadResult:
+    """图绘上传结果。主返回 URL 为站内作品页，保留三元组解包兼容。"""
+    success: bool
+    download_url: str
+    work_id: int
+    image_url: str = ""
+    thumbnail_url: str = ""
+    watermarked_url: str = ""
+    message: str = ""
+
+    def __iter__(self) -> Iterator[object]:
+        # 兼容历史调用：ok, download_url, work_id = result
+        yield self.success
+        yield self.download_url
+        yield self.work_id
+
+    def as_dict(self) -> dict:
+        return {
+            "success": self.success,
+            "download_url": self.download_url,
+            "work_id": self.work_id,
+            "image_url": self.image_url,
+            "thumbnail_url": self.thumbnail_url,
+            "watermarked_url": self.watermarked_url,
+            "message": self.message,
+        }
+
 class TuhuiUploadService:
     """图绘平台上传服务"""
     
@@ -49,6 +80,10 @@ class TuhuiUploadService:
     def _api_url(self, path: str) -> str:
         return self._build_api_url(self.base_url, path)
 
+    @staticmethod
+    def _build_work_url(work_id: int) -> str:
+        return f"{TUHUI_WEB_BASE_URL}/detail/{int(work_id)}"
+
     @staticmethod
     def _guess_file_meta(image_path: str) -> tuple[str, str]:
         path = Path(image_path)
@@ -97,7 +132,8 @@ class TuhuiUploadService:
         price: Optional[int] = None,
         category: str = TUHUI_DEFAULT_CATEGORY,
         tags: str = "",
-    ) -> Tuple[bool, str, int]:
+        designer_name: str = "",
+    ) -> TuhuiUploadResult:
         """
         上传图片到图绘平台
         
@@ -109,16 +145,19 @@ class TuhuiUploadService:
             category: 分类
             
         Returns:
-            (success, image_url, work_id)
+            TuhuiUploadResult
             - success: 是否上传成功
-            - image_url: 图片 URL
+            - download_url: 站内作品页地址
+            - image_url: 原图 URL（保留，便于需要时取用）
+            - thumbnail_url: 缩略图 URL
+            - watermarked_url: 水印图 URL
             - work_id: 作品 ID
         """
         try:
             # 如果 token 过期，重新登录
             if not self.access_token:
                 if not await self.login():
-                    return False, "登录失败", 0
+                    return TuhuiUploadResult(False, "", 0, message="登录失败")
             
             # 准备上传数据
             price = price or self.default_price
@@ -126,7 +165,7 @@ class TuhuiUploadService:
             # 读取图片文件
             if not os.path.exists(image_path):
                 logger.error(f"图片文件不存在：{image_path}")
-                return False, "文件不存在", 0
+                return TuhuiUploadResult(False, "", 0, message="文件不存在")
             
             filename, mime_type = self._guess_file_meta(image_path)
             with open(image_path, "rb") as f:
@@ -142,6 +181,8 @@ class TuhuiUploadService:
                 }
                 if tags:
                     data["tags"] = tags
+                if designer_name:
+                    data["designer_name"] = str(designer_name).strip()
                 
                 headers = {
                     "Authorization": f"Bearer {self.access_token}"
@@ -160,12 +201,34 @@ class TuhuiUploadService:
                         payload = response.json()
                         if not payload.get("success", False):
                             logger.error(f"图绘平台上传返回失败：{payload}")
-                            return False, payload.get("message", "上传失败"), 0
+                            return TuhuiUploadResult(
+                                False,
+                                "",
+                                0,
+                                message=str(payload.get("message", "上传失败")),
+                            )
 
                         work_id = int(payload.get("work_id") or payload.get("work", {}).get("id") or 0)
                         image_url = str(payload.get("image_url") or payload.get("work", {}).get("original_image") or "")
-                        logger.info(f"图绘平台上传成功，作品 ID: {work_id}, URL: {image_url}")
+                        thumbnail_url = str(
-                        return True, image_url, work_id
+                            payload.get("thumbnail_url") or payload.get("work", {}).get("thumbnail_image") or ""
+                        )
+                        watermarked_url = str(
+                            payload.get("watermarked_url") or payload.get("work", {}).get("watermarked_image") or ""
+                        )
+                        download_url = self._build_work_url(work_id) if work_id else ""
+                        logger.info(
+                            f"图绘平台上传成功，作品 ID: {work_id}, 站内地址: {download_url}, 原图: {image_url}"
+                        )
+                        return TuhuiUploadResult(
+                            True,
+                            download_url,
+                            work_id,
+                            image_url=image_url,
+                            thumbnail_url=thumbnail_url,
+                            watermarked_url=watermarked_url,
+                            message=str(payload.get("message", "上传成功")),
+                        )
                     else:
                         logger.error(f"图绘平台上传失败：{response.status_code} {response.text}")
                         
@@ -176,13 +239,13 @@ class TuhuiUploadService:
                             if await self.login():
                                 # 重新上传
                                 return await self.upload_image(
-                                    image_path, title, description, price, category
+                                    image_path, title, description, price, category, tags, designer_name
                                 )
                         
-                        return False, f"上传失败：{response.text}", 0
+                        return TuhuiUploadResult(False, "", 0, message=f"上传失败：{response.text}")
         except Exception as e:
             logger.error(f"图绘平台上传异常：{e}")
-            return False, f"上传异常：{e}", 0
+            return TuhuiUploadResult(False, "", 0, message=f"上传异常：{e}")
 
 
 # 单例
@@ -204,12 +267,13 @@ async def upload_to_tuhui(
     price: int = 20,
     category: str = TUHUI_DEFAULT_CATEGORY,
     tags: str = "",
-) -> Tuple[bool, str, int]:
+    designer_name: str = "",
+) -> TuhuiUploadResult:
     """
     便捷函数：上传图片到图绘平台
     
     Returns:
-        (success, image_url, work_id)
+        TuhuiUploadResult
     """
     service = get_tuhui_service()
-    return await service.upload_image(image_path, title, description, price, category, tags)
+    return await service.upload_image(image_path, title, description, price, category, tags, designer_name)

services/service_wecom_bot.py

@@ -0,0 +1,51 @@
+import logging
+import os
+
+import httpx
+from dotenv import load_dotenv
+
+load_dotenv()
+logger = logging.getLogger("cs_agent")
+
+WECOM_BOT_WEBHOOK = os.getenv(
+    "WECOM_BOT_WEBHOOK",
+    "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=cc88bdef-a13f-4d7e-bdb6-ee51b68b8205",
+).strip()
+
+
+class WecomBotService:
+    def __init__(self, webhook_url: str = WECOM_BOT_WEBHOOK):
+        self.webhook_url = str(webhook_url or "").strip()
+
+    async def send_text(self, content: str) -> bool:
+        text = str(content or "").strip()
+        if not text:
+            return False
+        if not self.webhook_url:
+            logger.warning("[WeComBot] 未配置 webhook，跳过发送")
+            return False
+
+        payload = {
+            "msgtype": "text",
+            "text": {
+                "content": text[:3500],
+            },
+        }
+
+        try:
+            async with httpx.AsyncClient(timeout=10.0) as client:
+                response = await client.post(self.webhook_url, json=payload)
+            if response.status_code != 200:
+                logger.warning(f"[WeComBot] 发送失败 HTTP {response.status_code}: {response.text}")
+                return False
+            data = response.json()
+            ok = int(data.get("errcode", -1)) == 0
+            if not ok:
+                logger.warning(f"[WeComBot] 发送失败: {data}")
+            return ok
+        except Exception as e:
+            logger.warning(f"[WeComBot] 发送异常: {e}")
+            return False
+
+
+wecom_bot_service = WecomBotService()