feat:初版
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350
plugins/Kiira2AI/main.py
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350
plugins/Kiira2AI/main.py
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"""
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Kiira2 AI绘图插件
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支持命令触发和LLM工具调用
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"""
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import asyncio
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import tomllib
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import httpx
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import uuid
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from pathlib import Path
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from datetime import datetime
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from typing import List, Optional
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from loguru import logger
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from utils.plugin_base import PluginBase
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from utils.decorators import on_text_message
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from WechatHook import WechatHookClient
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class TokenState:
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"""Token轮询状态管理"""
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def __init__(self):
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self.token_index = 0
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self._lock = asyncio.Lock()
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async def get_next_token(self, tokens: List[str]) -> str:
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"""获取下一个可用的token"""
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async with self._lock:
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if not tokens:
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raise ValueError("Token列表为空")
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return tokens[self.token_index % len(tokens)]
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async def rotate(self, tokens: List[str]):
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"""轮换到下一个token"""
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async with self._lock:
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if tokens:
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self.token_index = (self.token_index + 1) % len(tokens)
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class Kiira2AI(PluginBase):
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"""Kiira2 AI绘图插件"""
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description = "Kiira2 AI绘图插件 - 支持AI绘图和LLM工具调用"
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author = "ShiHao"
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version = "1.0.0"
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def __init__(self):
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super().__init__()
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self.config = None
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self.token_state = TokenState()
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self.images_dir = None
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async def async_init(self):
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"""异步初始化"""
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config_path = Path(__file__).parent / "config.toml"
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with open(config_path, "rb") as f:
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self.config = tomllib.load(f)
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# 创建图片目录
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self.images_dir = Path(__file__).parent / "images"
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self.images_dir.mkdir(exist_ok=True)
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logger.success(f"Kiira2 AI插件初始化完成，配置了 {len(self.config['api']['tokens'])} 个token")
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async def generate_image(self, prompt: str, **kwargs) -> List[str]:
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"""
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生成图像
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Args:
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prompt: 提示词
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**kwargs: 其他参数（model）
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Returns:
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图片本地路径列表
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"""
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api_config = self.config["api"]
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gen_config = self.config["generation"]
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model = kwargs.get("model", gen_config["default_model"])
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tokens = api_config["tokens"]
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max_retry = gen_config["max_retry_attempts"]
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# 尝试每个token
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for token_attempt in range(len(tokens)):
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current_token = await self.token_state.get_next_token(tokens)
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for attempt in range(max_retry):
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if attempt > 0:
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await asyncio.sleep(min(2 ** attempt, 10))
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try:
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url = f"{api_config['base_url'].rstrip('/')}/v1/chat/completions"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {current_token}"
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}
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payload = {
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"model": model,
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"messages": [{"role": "user", "content": prompt}],
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"stream": False
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}
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logger.info(f"Kiira2 AI请求: {model}, 提示词: {prompt[:50]}...")
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timeout = httpx.Timeout(connect=10.0, read=api_config["timeout"], write=10.0, pool=10.0)
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# 配置代理
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proxy = None
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proxy_config = self.config.get("proxy", {})
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if proxy_config.get("enabled", False):
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proxy_type = proxy_config.get("type", "socks5")
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proxy_host = proxy_config.get("host", "127.0.0.1")
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proxy_port = proxy_config.get("port", 7890)
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proxy = f"{proxy_type}://{proxy_host}:{proxy_port}"
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logger.info(f"使用代理: {proxy}")
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async with httpx.AsyncClient(timeout=timeout, proxy=proxy) as client:
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response = await client.post(url, json=payload, headers=headers)
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if response.status_code == 200:
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data = response.json()
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logger.debug(f"API返回数据: {data}")
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if "error" in data:
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logger.error(f"API错误: {data['error']}")
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continue
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# 检查是否返回空content（图片还在生成中）
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if "choices" in data and data["choices"]:
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message = data["choices"][0].get("message", {})
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content = message.get("content", "")
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video_url = message.get("video_url")
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# 如果content为空且没有video_url，说明还在生成，等待后重试
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if not content and not video_url:
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wait_time = min(10 + attempt * 5, 30)
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logger.info(f"图片生成中，等待 {wait_time} 秒后重试...")
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await asyncio.sleep(wait_time)
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continue
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# 提取图片URL
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image_paths = await self._extract_images(data)
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if image_paths:
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logger.success(f"成功生成 {len(image_paths)} 张图像")
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return image_paths
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else:
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logger.warning(f"未找到图像数据，API返回: {str(data)[:500]}")
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continue
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elif response.status_code == 401:
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logger.warning("Token认证失败，尝试下一个token")
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break
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elif response.status_code == 429:
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logger.warning("请求频率限制，等待后重试")
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await asyncio.sleep(5)
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continue
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else:
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error_text = response.text
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logger.error(f"API请求失败: {response.status_code}, {error_text[:200]}")
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continue
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except asyncio.TimeoutError:
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logger.warning(f"请求超时，重试中... ({attempt + 1}/{max_retry})")
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continue
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except Exception as e:
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logger.error(f"请求异常: {e}")
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continue
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# 当前token失败，轮换
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await self.token_state.rotate(tokens)
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logger.error("所有token都失败了")
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return []
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async def _extract_images(self, data: dict) -> List[str]:
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"""从API响应中提取图片（只提取图片，忽略文字）"""
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import re
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image_paths = []
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# OpenAI格式的choices
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if "choices" in data and data["choices"]:
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for choice in data["choices"]:
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message = choice.get("message", {})
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# 检查video_url字段（实际包含图片URL）
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if "video_url" in message:
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video_url = message["video_url"]
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if isinstance(video_url, list) and video_url:
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url = video_url[0]
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if isinstance(url, str) and url.startswith("http"):
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path = await self._download_image(url)
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if path:
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image_paths.append(path)
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# 检查content字段
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if "content" in message and not image_paths:
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content = message["content"]
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if content and "http" in content:
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urls = re.findall(r'https?://[^\s\)\]"]+', content)
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for url in urls:
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path = await self._download_image(url)
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if path:
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image_paths.append(path)
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return image_paths
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async def _download_image(self, url: str) -> Optional[str]:
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"""下载图片到本地"""
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try:
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timeout = httpx.Timeout(connect=10.0, read=30.0, write=10.0, pool=10.0)
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# 配置代理
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proxy = None
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proxy_config = self.config.get("proxy", {})
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if proxy_config.get("enabled", False):
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proxy_type = proxy_config.get("type", "socks5")
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proxy_host = proxy_config.get("host", "127.0.0.1")
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proxy_port = proxy_config.get("port", 7890)
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proxy = f"{proxy_type}://{proxy_host}:{proxy_port}"
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async with httpx.AsyncClient(timeout=timeout, proxy=proxy) as client:
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response = await client.get(url)
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response.raise_for_status()
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# 生成文件名
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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uid = uuid.uuid4().hex[:8]
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file_path = self.images_dir / f"kiira2_{ts}_{uid}.jpg"
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# 保存文件
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with open(file_path, "wb") as f:
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f.write(response.content)
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logger.info(f"图片下载成功: {file_path}")
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return str(file_path)
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except Exception as e:
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logger.error(f"下载图片失败: {e}")
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return None
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@on_text_message(priority=70)
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async def handle_message(self, bot: WechatHookClient, message: dict):
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"""处理文本消息"""
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if not self.config["behavior"]["enable_command"]:
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return True
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content = message.get("Content", "").strip()
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from_wxid = message.get("FromWxid", "")
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is_group = message.get("IsGroup", False)
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# 检查群聊/私聊开关
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if is_group and not self.config["behavior"]["enable_group"]:
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return True
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if not is_group and not self.config["behavior"]["enable_private"]:
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return True
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# 检查是否是绘图命令
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keywords = self.config["behavior"]["command_keywords"]
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matched_keyword = None
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for keyword in keywords:
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if content.startswith(keyword + " "):
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matched_keyword = keyword
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break
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if not matched_keyword:
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return True
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# 提取提示词
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prompt = content[len(matched_keyword):].strip()
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if not prompt:
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await bot.send_text(from_wxid, "❌ 请提供绘图提示词\n用法: /画画 <提示词>")
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return False
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logger.info(f"收到绘图请求: {prompt[:50]}...")
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# 发送处理中提示
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await bot.send_text(from_wxid, "🎨 正在为您生成图像，请稍候...")
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try:
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# 生成图像
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image_paths = await self.generate_image(prompt)
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if image_paths:
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# 直接发送图片
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await bot.send_image(from_wxid, image_paths[0])
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logger.success(f"绘图成功，已发送图片")
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else:
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await bot.send_text(from_wxid, "❌ 图像生成失败，请稍后重试")
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except Exception as e:
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logger.error(f"绘图处理失败: {e}")
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await bot.send_text(from_wxid, f"❌ 处理失败: {str(e)}")
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return False
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def get_llm_tools(self) -> List[dict]:
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"""返回LLM工具定义"""
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if not self.config["llm_tool"]["enabled"]:
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return []
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return [{
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"type": "function",
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"function": {
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"name": self.config["llm_tool"]["tool_name"],
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"description": self.config["llm_tool"]["tool_description"],
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"parameters": {
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"type": "object",
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"properties": {
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"prompt": {
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"type": "string",
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"description": "图像生成提示词，描述想要生成的图像内容"
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}
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},
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"required": ["prompt"]
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}
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}
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}]
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async def execute_llm_tool(self, tool_name: str, arguments: dict, bot: WechatHookClient, from_wxid: str) -> dict:
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"""执行LLM工具调用"""
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expected_tool_name = self.config["llm_tool"]["tool_name"]
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if tool_name != expected_tool_name:
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return None
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try:
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prompt = arguments.get("prompt")
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if not prompt:
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return {"success": False, "message": "缺少提示词参数"}
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logger.info(f"LLM工具调用绘图: {prompt[:50]}...")
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# 生成图像
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image_paths = await self.generate_image(prompt=prompt)
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if image_paths:
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# 直接发送图片
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await bot.send_image(from_wxid, image_paths[0])
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return {
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"success": True,
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"message": "已生成并发送图像",
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"images": [image_paths[0]]
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}
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else:
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return {"success": False, "message": "图像生成失败"}
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except Exception as e:
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logger.error(f"LLM工具执行失败: {e}")
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return {"success": False, "message": f"执行失败: {str(e)}"}
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