# -*- coding: utf-8 -*- """ AI 模型调用层 统一管理所有 LLM / Vision / Image 模型的调用逻辑 支持 Qwen (DashScope) 和 Gemini (第三方代理) 两套路由 """ from typing import List import json, base64, re, logging from app.core.config import settings from app.api.v1.ai_tools import PS_TOOLS, TOOL_DISPLAY_NAMES log = logging.getLogger(__name__) # ==================== Prompts ==================== SYSTEM_PROMPT = """你是 DesignerCEP 的 AI 助手，运行在 Adobe Photoshop CEP 插件中。 你的能力： 1. 回答关于 Photoshop 操作和插件使用的问题 2. 通过工具直接操作 Photoshop（创建图层、对齐、查看文档信息等） 3. 帮用户排查操作中遇到的错误 4. **AI 智能套图**：两阶段流程 — 先生成预览确认，再提取套到裁片上 ## AI 智能套图流程（两阶段） 当用户上传成衣图片并要求套图时： ### 阶段 1 — 识别裁片 + 生成预览（需用户确认） 1. 调用 **identify_pieces** — 截取画布并识别每个图层是什么裁片部位（前片、后片、袖子等） 2. 告诉用户识别结果（如 "M-1=前片, M-2=后片, M-5=左袖..."） 3. 调用 **generate_garment_preview** — 会自动在裁片下方标注名称标签（如 "M-1 前片"），然后截取带标签的画布 + 成衣照片一起发给 AI 生成预览 4. 预览图显示在聊天中，每个裁片都有标签，**等用户确认 OK 后**进入阶段 2 ### 阶段 2 — 提取花样 + 正式套图（用户确认后） 5. 根据 identify_pieces 分析结果中每个裁片的 type 决定处理方式： - solid → 设 color 字段，PS 直接纯色填充 - fill_pattern → AI 提取花型铺满 - theme_pattern → 底层 PS 纯色填充（设 color）+ 上层 AI 提取主题图案（白底+正片叠底） - mixed_pattern → 底层 AI 提取花型 + 上层 AI 提取主题图案（白底+正片叠底） 6. 调用 **extract_and_apply_all_pieces** 执行套图 7. 可选：调用 **verify_pattern_result** 验证效果 重要： - 阶段 1 完成后必须**等用户说"可以"/"OK"**才执行阶段 2 - 用户可能会说"袖子纯色"、"后幅不要花样"等，要根据 identify_pieces 的结果对应到正确的图层名 重要规则： - 当用户要求执行 PS 操作时，使用工具完成 - 执行操作前可以先了解当前文档和图层状态 - 用简洁的中文回答，适合在小面板中阅读 - 如果工具执行失败，向用户解释原因并建议解决方案 """ VISION_PROMPT = """你是一位资深的服装设计分析师，同时也是 DesignerCEP 的 AI 助手。 当用户发送服装/成衣图片时，请从以下维度进行专业分析： 1. **服装类别** — 上衣/裤子/裙子/连衣裙/外套/配饰等，细分款式 2. **面料分析** — 根据视觉特征推测面料类型（棉、涤纶、丝绸、针织、牛仔、雪纺等），分析面料质感 3. **颜色与印花** — 主色调、配色方案、印花/图案类型及工艺（数码印花、丝网印刷、提花等） 4. **版型特点** — 修身/宽松/A字/H型等，分析领口、袖型、肩线、腰线、下摆处理 5. **工艺细节** — 缝线工艺、拉链/纽扣/暗扣、口袋设计、装饰细节、包边/锁边 6. **设计评价** — 设计亮点、风格定位（休闲/正装/运动/时尚等）、目标消费群体 7. **改进建议** — 如有可改进之处，给出专业建议 规则： - 如果图片不是服装相关，也请尽力分析图片内容并给出有价值的反馈 - 如果用户同时提了文字问题，请结合图片和问题一起回答 - 用清晰、结构化的中文回答，适合在设计工作中参考 - 回答要专业但不啰嗦，突出重点信息 """ # ==================== 路由判断 ==================== def is_gemini_model(model_name: str) -> bool: """判断是否是 Gemini 模型""" return bool(model_name) and "gemini" in model_name.lower() # ==================== Qwen / OpenAI 兼容调用 ==================== def call_llm_with_tools(messages_history: List[dict], model_override: str = None): """调用 LLM，支持 function calling""" from openai import OpenAI use_model = model_override or settings.AI_MODEL client = OpenAI( api_key=settings.AI_API_KEY, base_url=settings.AI_BASE_URL or "https://api.openai.com/v1", ) messages = [{"role": "system", "content": SYSTEM_PROMPT}] + messages_history log.info(f"{'='*60}") log.info(f"[LLM] 调用工具模型: {use_model}{' (override)' if model_override else ''}") log.info(f"[LLM] 消息数量: {len(messages)} (system + {len(messages_history)} history)") for i, m in enumerate(messages_history[-5:]): role = m['role'] content = m['content'][:120] if m.get('content') else '(empty)' log.info(f"[LLM] history[-{len(messages_history)-i}] {role}: {content}") log.info(f"[LLM] 工具数量: {len(PS_TOOLS)}") completion = client.chat.completions.create( model=use_model, messages=messages, tools=PS_TOOLS, tool_choice="auto", ) choice = completion.choices[0] message = choice.message log.info(f"[LLM] 响应 finish_reason={choice.finish_reason}") if message.content: log.info(f"[LLM] 回复文本: {message.content[:150]}...") if message.tool_calls: for tc in message.tool_calls: log.info(f"[LLM] 工具调用: {tc.function.name}({tc.function.arguments[:200]})") else: log.info(f"[LLM] 无工具调用") log.info(f"{'='*60}") if message.tool_calls and len(message.tool_calls) > 0: tool_calls_data = [] for tc in message.tool_calls: args = {} if tc.function.arguments: try: args = json.loads(tc.function.arguments) except json.JSONDecodeError: args = {} tool_calls_data.append({ "id": tc.id, "name": tc.function.name, "display_name": TOOL_DISPLAY_NAMES.get(tc.function.name, tc.function.name), "args": args, "status": "pending" }) return message.content or "", tool_calls_data return message.content or "", None # ==================== Gemini 调用（OpenAI 兼容代理） ==================== def call_gemini(messages_history: List[dict], model: str, images_b64: List[str] = None) -> str: """调用 Gemini（通过第三方代理，OpenAI 兼容格式）""" from openai import OpenAI as _OpenAI if not settings.GEMINI_API_KEY or not settings.GEMINI_BASE_URL: raise ValueError("GEMINI_API_KEY 或 GEMINI_BASE_URL 未配置") client = _OpenAI( api_key=settings.GEMINI_API_KEY, base_url=f"{settings.GEMINI_BASE_URL}/v1", ) messages = [] for msg in messages_history: role = msg.get("role", "user") content = msg.get("content", "") if role not in ("system", "user", "assistant"): role = "user" messages.append({"role": role, "content": content}) if images_b64: last_user_idx = None for i in range(len(messages) - 1, -1, -1): if messages[i]["role"] == "user": last_user_idx = i break if last_user_idx is not None: text_content = messages[last_user_idx]["content"] multimodal_content = [] for img_b64 in images_b64: multimodal_content.append({ "type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"} }) multimodal_content.append({"type": "text", "text": text_content}) messages[last_user_idx]["content"] = multimodal_content log.info(f"{'='*60}") log.info(f"[Gemini] 调用模型: {model} (OpenAI 兼容)") log.info(f"[Gemini] 消息数: {len(messages)}, 图片数: {len(images_b64) if images_b64 else 0}") completion = client.chat.completions.create(model=model, messages=messages) result = completion.choices[0].message.content or "" log.info(f"[Gemini] 回复: {result[:200]}...") return result def call_gemini_with_tools(messages_history: List[dict], model: str) -> tuple: """用 Gemini 做对话（不支持 function calling）""" full_history = [{"role": "system", "content": SYSTEM_PROMPT}] + messages_history log.info(f"{'='*60}") log.info(f"[Gemini] 调用对话模型: {model}") for i, m in enumerate(messages_history[-3:]): log.info(f"[Gemini] history[-{len(messages_history)-i}] {m['role']}: {str(m.get('content',''))[:120]}") result = call_gemini(full_history, model) log.info(f"[Gemini] 回复文本: {result[:150]}...") return result, None # ==================== 视觉模型 ==================== def call_vision_llm(user_message: str, image_base64: str, history: List[dict], model_override: str = None) -> str: """调用视觉模型分析图片（自动路由 Qwen / Gemini）""" use_model = model_override or settings.AI_VISION_MODEL if is_gemini_model(use_model): log.info(f"[Vision] 使用 Gemini 视觉模型: {use_model}") msgs = [{"role": "system", "content": VISION_PROMPT}] for h in history[-10:]: role = h["role"] if h["role"] != "tool" else "user" msgs.append({"role": role, "content": h["content"]}) msgs.append({"role": "user", "content": user_message}) return call_gemini(msgs, use_model, images_b64=[image_base64]) from openai import OpenAI client = OpenAI(api_key=settings.AI_API_KEY, base_url=settings.AI_BASE_URL or "https://api.openai.com/v1") user_content = [ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}, {"type": "text", "text": user_message} ] messages = [{"role": "system", "content": VISION_PROMPT}] for h in history[-10:]: role = h["role"] if h["role"] != "tool" else "user" messages.append({"role": role, "content": h["content"]}) messages.append({"role": "user", "content": user_content}) completion = client.chat.completions.create(model=use_model, messages=messages) return completion.choices[0].message.content or "" # ==================== 图片编辑/生成模型 ==================== def call_image_model(images_b64: List[str], prompt: str, model_override: str = None) -> tuple: """调用图片编辑/生成模型（自动路由 DashScope / Gemini），返回 (url_or_datauri, description)""" import requests as http_requests use_model = model_override or settings.AI_IMAGE_EDIT_MODEL # ---------- Gemini（OpenAI 兼容代理） ---------- if is_gemini_model(use_model): log.info(f"[ImageModel] 使用 Gemini 图片模型: {use_model}") if not settings.GEMINI_API_KEY or not settings.GEMINI_BASE_URL: raise ValueError("GEMINI_API_KEY 或 GEMINI_BASE_URL 未配置") from openai import OpenAI as _OpenAI client = _OpenAI(api_key=settings.GEMINI_API_KEY, base_url=f"{settings.GEMINI_BASE_URL}/v1") content_parts = [{"type": "text", "text": prompt}] for img_b64 in images_b64: content_parts.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"}}) log.info(f"[ImageModel] Gemini OpenAI 兼容, 模型: {use_model}") log.info(f"[ImageModel] 图片数: {len(images_b64)}, 各图: {[len(b)//1024 for b in images_b64]}KB") log.info(f"[ImageModel] 提示词: {prompt[:200]}") completion = client.chat.completions.create(model=use_model, messages=[{"role": "user", "content": content_parts}]) result_content = completion.choices[0].message.content or "" log.info(f"[ImageModel] Gemini 回复长度: {len(result_content)} chars") # 提取 base64 图片 match = re.search(r'!\[.*?\]\((data:image/(\w+);base64,([^)]+))\)', result_content) if not match: match = re.search(r'(data:image/(\w+);base64,([A-Za-z0-9+/=]+))', result_content) if not match: log.warning(f"[ImageModel] Gemini 响应中无图片，前500字: {result_content[:500]}") raise ValueError("Gemini 未返回图片，请检查模型是否支持图片生成") img_format = match.group(2) image_b64 = match.group(3) padding = 4 - len(image_b64) % 4 if padding != 4: image_b64 += '=' * padding log.info(f"[ImageModel] Gemini 返回图片: image/{img_format}, {len(image_b64)//1024}KB") _save_debug_image(base64.b64decode(image_b64), f'gemini_output.{img_format}') description = re.sub(r'!\[.*?\]\(data:image/[^)]+\)', '', result_content).strip() return f"data:image/{img_format};base64,{image_b64}", description # ---------- DashScope 原生接口 ---------- api_url = "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" content_parts = [] for img_b64 in images_b64: content_parts.append({"image": f"data:image/jpeg;base64,{img_b64}"}) content_parts.append({"text": prompt}) payload = { "model": use_model, "input": {"messages": [{"role": "user", "content": content_parts}]}, "parameters": {"n": 1, "watermark": False, "prompt_extend": True} } headers = {"Content-Type": "application/json", "Authorization": f"Bearer {settings.AI_API_KEY}"} log.info(f"{'='*60}") log.info(f"[ImageModel] DashScope 原生 API, 模型: {use_model}") log.info(f"[ImageModel] 图片数: {len(images_b64)}, 各图: {[len(b)//1024 for b in images_b64]}KB") log.info(f"[ImageModel] 提示词: {prompt[:200]}") for idx, img_b64 in enumerate(images_b64): _save_debug_image(base64.b64decode(img_b64), f'input_{idx}.jpg') resp = http_requests.post(api_url, json=payload, headers=headers, timeout=120) if resp.status_code != 200: error_data = resp.json() if resp.headers.get("content-type", "").startswith("application/json") else {} err_msg = error_data.get("message", resp.text[:300]) log.error(f"[ImageModel] API 错误: {resp.status_code} {err_msg}") if "data_inspection_failed" in str(error_data): raise ValueError("图片内容未通过安全审核，请更换图片") raise ValueError(f"图片模型调用失败({resp.status_code}): {err_msg}") data = resp.json() output = data.get("output", {}) choices = output.get("choices", []) if not choices: raise ValueError("模型未返回结果") content_list = choices[0].get("message", {}).get("content", []) image_url = None description = "" for item in content_list: if isinstance(item, dict): if "image" in item: image_url = item["image"] elif "text" in item: description += item["text"] if not image_url: raise ValueError("模型未返回图片") log.info(f"[ImageModel] 输出图片 URL: {image_url[:120]}...") try: out_resp = http_requests.get(image_url, timeout=60) if out_resp.status_code == 200: _save_debug_image(out_resp.content, 'output.png') except Exception: pass return image_url, description # ==================== 验证套图效果 ==================== def verify_pattern_result(garment_b64: str, canvas_b64: str, extra_prompt: str = None, vision_model: str = None) -> str: """用视觉模型对比原始成衣和套图结果""" use_model = vision_model or settings.AI_VISION_MODEL log.info(f"[Verify] 模型: {use_model}, 成衣: {len(garment_b64)//1024}KB, 画布: {len(canvas_b64)//1024}KB") verify_prompt = ( "请对比这两张图片：第一张是原始成衣照片，第二张是套图结果。\n" "验证：1. 花样还原度 2. 裁片覆盖完整度 3. 对齐质量 4. 整体效果。给出评分(1-10)和改进建议。" ) if extra_prompt: verify_prompt += f"\n用户补充：{extra_prompt}" if is_gemini_model(use_model): return call_gemini( [{"role": "user", "content": "你是服装套图质量检验专家。"}, {"role": "user", "content": verify_prompt}], use_model, images_b64=[garment_b64, canvas_b64] ) from openai import OpenAI client = OpenAI(api_key=settings.AI_API_KEY, base_url=settings.AI_BASE_URL or "https://api.openai.com/v1") completion = client.chat.completions.create( model=use_model, messages=[ {"role": "system", "content": "你是服装套图质量检验专家。"}, {"role": "user", "content": [ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{garment_b64}"}}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{canvas_b64}"}}, {"type": "text", "text": verify_prompt}, ]}, ], ) return completion.choices[0].message.content or "" # ==================== Mock ==================== def mock_reply(message: str, has_image: bool = False) -> str: """未配置 API Key 时的模拟回复""" if has_image: return "【模拟分析】收到图片。AI 分析功能需要配置 AI_API_KEY。" if "套图" in message: return "套图功能在「参数预设」页面。先选择花样组和裁片组，再添加规则，最后点击生成。" return "你好！我是 DesignerCEP AI 助手。你可以问我关于套图、裁片、对齐等功能的问题。" # ==================== 工具函数 ==================== def _save_debug_image(data: bytes, filename: str): """保存调试图片到 debug_images 目录""" import os, time debug_dir = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'debug_images') os.makedirs(debug_dir, exist_ok=True) ts = int(time.time()) path = os.path.join(debug_dir, f'{ts}_{filename}') try: with open(path, 'wb') as f: f.write(data) log.info(f"[Debug] 图片已保存: {path}") except Exception as e: log.warning(f"[Debug] 保存失败: {e}")