初始提交：识流 AI 助手项目
微信自动回复机器人，基于截图+OCR识别消息，支持关键词规则和 AI（OpenAI/DeepSeek/Dify）自动回复。 技术栈：PySide6 + Flask + Vue3 + RapidOCR + SQLite 注：OCR大模型文件（.onnx / .pdiparams）不纳入版本控制，需单独下载。 🤖 Generated with [Qoder][https://qoder.com]
This commit is contained in:
figmar
504
app/infrastructure/wechat_multi_chat_bot copy.py.bak
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app/infrastructure/wechat_multi_chat_bot copy.py.bak
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import ctypes # Windows窗口句柄相关API
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import hashlib # 文本去重哈希
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import logging # 日志模块
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import os # 文件目录操作
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import re # 正则过滤文本
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import time # 休眠/节流
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from datetime import datetime # 时间戳命名与日志辅助
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from io import BytesIO # 图片转字节流给OCR
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import cv2 # 图像处理
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import numpy as np # 数组/图像矩阵
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import pyautogui # 鼠标键盘自动化
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import pyperclip # 剪贴板粘贴发送文本
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import requests # 调用后端接口
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import uiautomation as auto # Windows UI 自动化
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from PIL import ImageGrab, Image # 截图与图像对象
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from app.infrastructure.service.wechat.config import BAIDU_API_KEY, BAIDU_SECRET_KEY, BACKEND_URL, LOOP_INTERVAL, NO_REPLY_KEYWORDS, BLOCKED_SESSION_KEYWORDS, UI_NOISE_KEYWORDS, BOT_DEBUG_LOG, WECHAT_WINDOW_TARGET_WIDTH, WECHAT_WINDOW_TARGET_HEIGHT, WECHAT_WINDOW_TARGET_LEFT, WECHAT_WINDOW_TARGET_TOP, OCR_SAVE_IMAGES, OCR_SAVE_DIR # 机器人配置
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from app.infrastructure.service.wechat.ocr import BaiduOCR # 百度OCR封装
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logging.basicConfig( # 配置全局日志
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level=logging.INFO, # 默认输出INFO及以上
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format="%(asctime)s - %(levelname)s - %(message)s", # 日志格式
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handlers=[
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logging.FileHandler("wechat_multi_chat_bot.log", encoding="utf-8"), # 写文件
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logging.StreamHandler() # 打终端
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],
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)
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logger = logging.getLogger(__name__) # 当前模块日志器
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def debug_log(message):
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if BOT_DEBUG_LOG: # 仅在调试开关开启时输出
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logger.info(f"[DEBUG] {message}")
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class WechatMultiChatBot:
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"""微信自动回复机器人（UI自动化 + OCR + 规则/AI回复）。"""
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def __init__(self):
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"""初始化OCR、微信窗口句柄和运行期状态。"""
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logger.info("[BOT] 初始化开始") # 生命周期日志
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self.ocr = BaiduOCR(BAIDU_API_KEY, BAIDU_SECRET_KEY) # OCR客户端
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self.wechat_window = None # 微信窗口对象
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self.find_wechat_window() # 启动即查找窗口
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self.seen_messages = {} # 去重缓存：contact_key -> hash set
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self.processed_contacts = set() # 预留：已处理联系人集合
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self.running = False # 主循环运行状态
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logger.info("[BOT] 初始化完成")
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def find_wechat_window(self):
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"""查找微信主窗口；成功后执行窗口标准化。"""
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logger.info("[WIN] 正在查找微信窗口")
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self.wechat_window = auto.WindowControl(searchDepth=1, Name='微信') # 按窗口名查找
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if self.wechat_window.Exists(0, 0): # 立即探测是否存在
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logger.info("[WIN] 找到微信窗口")
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self.normalize_wechat_window() # 找到后标准化尺寸
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return
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logger.error("[WIN] 未找到微信窗口")
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raise Exception("未找到微信窗口")
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def normalize_wechat_window(self):
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"""将微信窗口调整到预设位置和尺寸，保证识别区域稳定。"""
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logger.info("[WIN] 开始标准化窗口大小和位置")
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try:
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self.wechat_window.SetActive() # 激活窗口
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time.sleep(0.25) # 等待激活稳定
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hwnd = getattr(self.wechat_window, "NativeWindowHandle", 0) # 尝试取窗口句柄
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if hwnd:
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user32 = ctypes.windll.user32 # Win32接口
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SWP_NOZORDER = 0x0004 # 不改变Z序
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SWP_NOACTIVATE = 0x0010 # 不抢前台焦点
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user32.SetWindowPos(
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int(hwnd), # 目标窗口句柄
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0, # hWndInsertAfter
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int(WECHAT_WINDOW_TARGET_LEFT), # 目标左上x
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int(WECHAT_WINDOW_TARGET_TOP), # 目标左上y
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int(WECHAT_WINDOW_TARGET_WIDTH), # 目标宽度
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int(WECHAT_WINDOW_TARGET_HEIGHT), # 目标高度
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SWP_NOZORDER | SWP_NOACTIVATE, # 标志位
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)
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logger.info(f"[WIN] 句柄模式窗口标准化成功 hwnd={hwnd}")
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else:
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if hasattr(self.wechat_window, "MoveTo") and hasattr(self.wechat_window, "Resize"): # 兼容旧接口
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self.wechat_window.MoveTo(WECHAT_WINDOW_TARGET_LEFT, WECHAT_WINDOW_TARGET_TOP) # 移动窗口
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self.wechat_window.Resize(WECHAT_WINDOW_TARGET_WIDTH, WECHAT_WINDOW_TARGET_HEIGHT) # 调整尺寸
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logger.info("[WIN] MoveTo/Resize 模式窗口标准化成功")
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else:
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raise Exception("未获取到窗口句柄且不支持 MoveTo/Resize")
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time.sleep(0.35) # 等待窗口重排完成
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logger.info(f"[WIN] 标准化参数 left={WECHAT_WINDOW_TARGET_LEFT} top={WECHAT_WINDOW_TARGET_TOP} width={WECHAT_WINDOW_TARGET_WIDTH} height={WECHAT_WINDOW_TARGET_HEIGHT}")
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debug_log(f"window_normalized=({WECHAT_WINDOW_TARGET_LEFT},{WECHAT_WINDOW_TARGET_TOP},{WECHAT_WINDOW_TARGET_WIDTH},{WECHAT_WINDOW_TARGET_HEIGHT})")
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except Exception as e:
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logger.warning(f"[WIN] 窗口标准化失败，继续按当前窗口运行: {e}")
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def get_window_rect(self):
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try:
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rect = self.wechat_window.BoundingRectangle # UIA矩形
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result = {'left': rect.left, 'top': rect.top, 'right': rect.right, 'bottom': rect.bottom, 'width': rect.right - rect.left, 'height': rect.bottom - rect.top} # 转字典
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debug_log(f"window_rect={result}")
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return result
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except Exception as e:
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logger.warning(f"[WIN] 获取窗口位置失败: {e}")
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return None
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def get_contact_list_rect(self, window_rect):
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rect = {'left': window_rect['left'] + 10, 'top': window_rect['top'] + 50, 'right': window_rect['left'] + int(window_rect['width'] * 0.25) - 10, 'bottom': window_rect['bottom'] - 50} # 左侧会话列表区域
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debug_log(f"contact_rect={rect}")
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return rect
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def detect_red_dots(self, window_rect):
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logger.info("[DETECT] 开始检测红点")
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contact_rect = self.get_contact_list_rect(window_rect) # 获取列表区域
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try:
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screenshot = ImageGrab.grab(bbox=(contact_rect['left'], contact_rect['top'], contact_rect['right'], contact_rect['bottom'])) # 列表截图
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img_np = np.array(screenshot) # PIL -> numpy
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img_bgr = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) # RGB -> BGR
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hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV) # BGR -> HSV
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mask = cv2.inRange(hsv, np.array([0, 80, 80]), np.array([12, 255, 255])) + cv2.inRange(hsv, np.array([168, 80, 80]), np.array([180, 255, 255])) # 红色双区间
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 查找连通域
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contact_width = contact_rect['right'] - contact_rect['left'] # 列表宽度
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red_dots_raw = [] # 原始红点
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for contour in contours:
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area = cv2.contourArea(contour) # 面积过滤
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if 12 < area < 220:
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perimeter = cv2.arcLength(contour, True) # 周长
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if perimeter > 0:
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circularity = 4 * np.pi * area / (perimeter * perimeter) # 圆度
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if circularity > 0.45:
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M = cv2.moments(contour) # 矩中心
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if M["m00"] != 0:
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cx = int(M["m10"] / M["m00"]) # 中心x
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cy = int(M["m01"] / M["m00"]) # 中心y
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if cx > contact_width * 0.1: # 过滤左边缘噪声
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red_dots_raw.append({'x': contact_rect['left'] + cx, 'y': contact_rect['top'] + cy, 'rel_y': cy}) # 记录绝对坐标
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red_dots_grouped = [] # 按行合并后的红点
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debug_log(f"red_dots_raw={len(red_dots_raw)}")
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used = set() # 已分组索引
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for i, dot in enumerate(red_dots_raw):
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if i in used:
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continue
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group = [dot] # 当前分组
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for j, other in enumerate(red_dots_raw):
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if j != i and j not in used and abs(dot['rel_y'] - other['rel_y']) < 50: # 同一行
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group.append(other)
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used.add(j)
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red_dots_grouped.append({'x': sum(d['x'] for d in group) // len(group), 'y': sum(d['y'] for d in group) // len(group)}) # 行中心
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used.add(i)
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logger.info(f"[DETECT] 红点检测完成 grouped={len(red_dots_grouped)}")
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debug_log(f"red_dots_grouped={len(red_dots_grouped)}")
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return red_dots_grouped
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except Exception as e:
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logger.error(f"[DETECT] 检测红点失败: {e}")
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return []
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def click_contact_by_red_dot(self, red_dot, window_rect):
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contact_rect = self.get_contact_list_rect(window_rect) # 计算左侧联系人列表区域
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click_x = (contact_rect['left'] + contact_rect['right']) // 2 # 点击联系人列表中线，避免点到红点本身
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click_y = red_dot['y'] # 使用红点纵坐标对应会话行
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logger.info(f"[ACTION] 点击联系人 x={click_x} y={click_y}") # 记录点击行为
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pyautogui.click(click_x, click_y) # 执行点击
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time.sleep(1.2) # 等待会话内容加载
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session_title = self.get_current_session_title() # 读取当前会话标题
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logger.info(f"[ACTION] 当前会话标题 title={session_title or '未知'}") # 输出标题用于排查
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time.sleep(1.0) # 再等待短时间，保证后续OCR稳定
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def get_session_title_by_ocr(self):
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try:
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window_rect = self.get_window_rect() # 获取微信窗口坐标
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if not window_rect:
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return "" # 窗口坐标不可用
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title_areas = [ # 多个标题区域兜底，适配不同微信版本/缩放
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(
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window_rect['left'] + int(window_rect['width'] * 0.32),
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window_rect['top'] + 6,
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window_rect['right'] - int(window_rect['width'] * 0.30),
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window_rect['top'] + max(40, int(window_rect['height'] * 0.085)),
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),
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(
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window_rect['left'] + int(window_rect['width'] * 0.28),
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window_rect['top'] + 4,
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window_rect['right'] - int(window_rect['width'] * 0.24),
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window_rect['top'] + max(46, int(window_rect['height'] * 0.095)),
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),
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]
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for idx, (left, top, right, bottom) in enumerate(title_areas, 1): # 逐区域尝试
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screenshot = ImageGrab.grab(bbox=(left, top, right, bottom)) # 截标题图
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self.save_ocr_debug_image(screenshot, f"title_ocr_{idx}_{datetime.now().strftime('%Y%m%d_%H%M%S_%f')}.png") # 落盘调试
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img_bytes = BytesIO() # 内存字节流
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screenshot.save(img_bytes, format='PNG') # 转png字节
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lines = self.ocr.recognize(img_bytes.getvalue()) # OCR识别
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valid = [x.strip() for x in lines if x and x.strip() and len(x.strip()) >= 2] # 清洗文本
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valid = [x for x in valid if x not in UI_NOISE_KEYWORDS] # 去噪
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if valid:
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title = valid[0] # 取首个有效标题
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debug_log(f"session_title_ocr={title} area={idx} lines={valid[:5]}")
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return title
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return "" # 全部区域失败
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except Exception as e:
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debug_log(f"session_title_ocr_error={e}")
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return "" # OCR异常返回空
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def get_current_session_title(self):
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try:
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title_ctrl = self.wechat_window.TextControl(foundIndex=1) # 优先UIA读标题
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if hasattr(title_ctrl, "Exists") and not title_ctrl.Exists(0.3, 0.05): # 快速存在性检查
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debug_log("session_title_not_found_fast")
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title = self.get_session_title_by_ocr() # UIA失败转OCR
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return title
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title = (getattr(title_ctrl, "Name", "") or "").strip() # 读取控件Name
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if not title or title in UI_NOISE_KEYWORDS: # 噪声/空值
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title = self.get_session_title_by_ocr() # OCR兜底
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debug_log(f"session_title={title}")
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return title
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except Exception as e:
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debug_log(f"session_title_error={e}")
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return self.get_session_title_by_ocr() # 异常也兜底
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def is_blocked_session(self):
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title = self.get_current_session_title() # 取会话标题
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if not title:
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return False # 标题未知默认不拦截
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for keyword in BLOCKED_SESSION_KEYWORDS: # 命中过滤词则跳过
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if keyword in title:
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logger.info(f"[SESSION] 命中过滤会话 title={title} keyword={keyword}")
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return True
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return False
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def get_latest_message_areas(self, window_rect):
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"""计算多段消息识别区域（靠近底部，适配不同窗口尺寸）。"""
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chat_left = window_rect['left'] + int(window_rect['width'] * 0.30) # 聊天区左边界
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chat_right = window_rect['right'] - 20 # 聊天区右边界
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chat_top = window_rect['top'] + int(window_rect['height'] * 0.16) # 聊天区上边界
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chat_bottom = window_rect['bottom'] - int(window_rect['height'] * 0.20) # 聊天区下边界（排除输入区）
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chat_height = max(420, int((chat_bottom - chat_top) * 0.90)) # 有效聊天高度
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area1_h = max(220, int(chat_height * 0.46)) # 近底部区域
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area2_h = max(320, int(chat_height * 0.72)) # 中大区域
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area3_h = max(420, int(chat_height * 0.98)) # 大区域兜底
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areas = [
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{'left': chat_left, 'top': max(chat_top, chat_bottom - area1_h), 'right': chat_right, 'bottom': chat_bottom}, # 区域1
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{'left': chat_left, 'top': max(chat_top, chat_bottom - area2_h), 'right': chat_right, 'bottom': chat_bottom - max(35, int(area1_h * 0.12))}, # 区域2
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{'left': chat_left, 'top': max(chat_top, chat_bottom - area3_h), 'right': chat_right, 'bottom': chat_bottom - max(80, int(area2_h * 0.20))}, # 区域3
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]
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debug_log(f"message_areas={areas}")
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return areas
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def should_reply(self, text):
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"""基础过滤：过短、无意义、无需回复文本直接跳过。"""
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if not text or len(text) < 2: # 文本太短不回复
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return False
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text_lower = text.lower() # 统一小写比较
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for keyword in NO_REPLY_KEYWORDS: # 命中免回复词
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if keyword.lower() in text_lower:
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return False
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return not text.isdigit() # 纯数字通常无语义
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def is_new_message(self, text, contact_key):
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"""消息去重：同一会话内已处理过的文本不重复回复。"""
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if contact_key not in self.seen_messages:
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self.seen_messages[contact_key] = set() # 首次初始化会话集合
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h = hashlib.md5(text.encode("utf-8")).hexdigest() # 文本哈希
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if h in self.seen_messages[contact_key]:
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logger.info(f"[FILTER] 重复消息 contact_key={contact_key}")
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return False
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self.seen_messages[contact_key].add(h) # 记录已处理
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if len(self.seen_messages[contact_key]) > 50: # 限制缓存规模
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self.seen_messages[contact_key] = set(list(self.seen_messages[contact_key])[-50:])
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return True
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def extract_text_by_uia(self):
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logger.info("[UIA] 尝试控件树文本提取")
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lines = [] # 候选文本
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try:
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all_text_controls = self.wechat_window.GetChildren() # 取一级子控件
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for ctrl in all_text_controls:
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name = (getattr(ctrl, 'Name', '') or '').strip() # 控件文本
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if not name:
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continue
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if len(name) < 2:
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continue
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if re.fullmatch(r"[0-9:\-\s]+", name): # 过滤时间/数字
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continue
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if name in UI_NOISE_KEYWORDS: # 过滤UI噪声
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continue
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lines.append(name) # 保留候选
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except Exception as e:
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logger.warning(f"[UIA] 控件树提取失败: {e}")
|
||||
|
||||
dedup = [] # 去重结果
|
||||
seen = set() # 去重集合
|
||||
for x in lines:
|
||||
if x not in seen:
|
||||
dedup.append(x)
|
||||
seen.add(x)
|
||||
|
||||
logger.info(f"[UIA] 控件树提取文本数={len(dedup)}")
|
||||
debug_log(f"uia_lines={dedup[-8:] if dedup else []}")
|
||||
return dedup
|
||||
|
||||
def save_ocr_debug_image(self, image_obj, filename):
|
||||
"""保存OCR调试截图，便于核对截取区域和图像质量。"""
|
||||
if not OCR_SAVE_IMAGES: # 开关关闭则不落盘
|
||||
return
|
||||
try:
|
||||
os.makedirs(OCR_SAVE_DIR, exist_ok=True) # 创建目录
|
||||
file_path = os.path.join(OCR_SAVE_DIR, filename) # 文件路径
|
||||
image_obj.save(file_path) # 保存图片
|
||||
debug_log(f"saved_ocr_image={file_path}")
|
||||
except Exception as e:
|
||||
logger.warning(f"[OCR] 保存调试截图失败: {e}")
|
||||
|
||||
def get_ai_reply(self, message):
|
||||
"""调用后端接口获取最终回复（规则优先，AI兜底）。"""
|
||||
logger.info(f"[API] 请求后端生成回复 content={message}")
|
||||
try:
|
||||
resp = requests.post(BACKEND_URL, json={"content": message, "wx_user_id": "multi_chat_bot", "wx_nickname": "用户"}, timeout=10) # 请求后端
|
||||
logger.info(f"[API] 后端响应 status={resp.status_code}")
|
||||
if resp.status_code == 200:
|
||||
data = resp.json() # 解析json
|
||||
if data.get("success") and data.get("should_reply"):
|
||||
reply = (data.get("reply_text") or "").strip() # 取回复文本
|
||||
logger.info(f"[API] 生成回复成功 reply={reply}")
|
||||
return reply
|
||||
logger.info("[API] 后端未返回可发送回复")
|
||||
except Exception as e:
|
||||
logger.error(f"[API] 调用接口失败: {e}")
|
||||
return None
|
||||
|
||||
def send_message(self, text, window_rect):
|
||||
logger.info(f"[SEND] 开始发送 reply={text}")
|
||||
try:
|
||||
self.wechat_window.SetActive() # 激活微信窗口
|
||||
time.sleep(0.3)
|
||||
pyautogui.click(window_rect['left'] + window_rect['width'] // 2, window_rect['bottom'] - 100) # 点击输入框
|
||||
time.sleep(0.3)
|
||||
pyautogui.hotkey('ctrl', 'a') # 全选旧文本
|
||||
pyautogui.press('delete') # 清空输入框
|
||||
pyperclip.copy(text) # 文本放入剪贴板
|
||||
pyautogui.hotkey('ctrl', 'v') # 粘贴内容
|
||||
pyautogui.press('enter') # 回车发送
|
||||
logger.info("[SEND] 发送成功")
|
||||
except Exception as e:
|
||||
logger.error(f"[SEND] 发送失败: {e}")
|
||||
|
||||
def process_current_chat(self, window_rect, contact_key):
|
||||
logger.info(f"[CHAT] 开始处理会话 contact_key={contact_key}")
|
||||
if self.is_blocked_session(): # 系统会话直接跳过
|
||||
logger.info("[CHAT] 当前会话在过滤名单，跳过")
|
||||
return False
|
||||
try:
|
||||
all_lines = [] # OCR/UIA汇总文本
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f") # 调试图时间戳
|
||||
for idx, msg_rect in enumerate(self.get_latest_message_areas(window_rect), 1): # 分区识别
|
||||
screenshot = ImageGrab.grab(bbox=(msg_rect['left'], msg_rect['top'], msg_rect['right'], msg_rect['bottom'])) # 区域截图
|
||||
img_np = np.array(screenshot) # 转numpy
|
||||
|
||||
raw_pil = Image.fromarray(img_np) # 原图
|
||||
self.save_ocr_debug_image(raw_pil, f"{timestamp}_{contact_key}_area{idx}_raw.png") # 保存原图
|
||||
raw_bytes = BytesIO() # 原图字节流
|
||||
raw_pil.save(raw_bytes, format='PNG')
|
||||
lines_raw = self.ocr.recognize(raw_bytes.getvalue()) # 原图OCR
|
||||
|
||||
gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) # 灰度化
|
||||
enhanced = cv2.convertScaleAbs(gray, alpha=1.35, beta=8) # 提升对比度
|
||||
_, binary = cv2.threshold(enhanced, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # 二值化
|
||||
pil_bin = Image.fromarray(binary) # 二值图
|
||||
self.save_ocr_debug_image(pil_bin, f"{timestamp}_{contact_key}_area{idx}_bin.png") # 保存二值图
|
||||
bin_bytes = BytesIO() # 二值图字节流
|
||||
pil_bin.save(bin_bytes, format='PNG')
|
||||
lines_bin = self.ocr.recognize(bin_bytes.getvalue()) # 二值图OCR
|
||||
|
||||
lines = lines_raw + lines_bin # 合并两路识别
|
||||
logger.info(f"[OCR] area={idx} raw={len(lines_raw)} bin={len(lines_bin)} total={len(lines)}")
|
||||
debug_log(f"ocr_lines_count={len(lines)} contact_key={contact_key} area={idx}")
|
||||
all_lines.extend(lines) # 汇总
|
||||
|
||||
if not all_lines: # 分区都失败时走整块兜底
|
||||
logger.info("[CHAT] 分区OCR无结果，尝试整块聊天区兜底")
|
||||
chat_left = window_rect['left'] + int(window_rect['width'] * 0.30)
|
||||
chat_right = window_rect['right'] - 20
|
||||
chat_top = window_rect['top'] + int(window_rect['height'] * 0.16)
|
||||
chat_bottom = window_rect['bottom'] - int(window_rect['height'] * 0.20)
|
||||
full_shot = ImageGrab.grab(bbox=(chat_left, chat_top, chat_right, chat_bottom)) # 整块截图
|
||||
full_np = np.array(full_shot)
|
||||
full_gray = cv2.cvtColor(full_np, cv2.COLOR_RGB2GRAY)
|
||||
full_enh = cv2.convertScaleAbs(full_gray, alpha=1.25, beta=6)
|
||||
_, full_bin = cv2.threshold(full_enh, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
full_pil = Image.fromarray(full_bin)
|
||||
self.save_ocr_debug_image(full_pil, f"{timestamp}_{contact_key}_fallback_full_bin.png") # 保存兜底图
|
||||
full_bytes = BytesIO()
|
||||
full_pil.save(full_bytes, format='PNG')
|
||||
full_lines = self.ocr.recognize(full_bytes.getvalue()) # 兜底OCR
|
||||
logger.info(f"[OCR] fallback_full_chat lines={len(full_lines)}")
|
||||
all_lines.extend(full_lines)
|
||||
|
||||
if not all_lines: # OCR全失败时走UIA
|
||||
logger.info("[CHAT] OCR 无结果，尝试 UIA 文本兜底")
|
||||
all_lines = self.extract_text_by_uia()
|
||||
|
||||
if not all_lines: # UIA也失败
|
||||
logger.info("[CHAT] OCR+UIA 均无结果，结束")
|
||||
return False
|
||||
|
||||
valid_lines = [line.strip() for line in all_lines if len(line.strip()) >= 2] # 过滤无效行
|
||||
if not valid_lines:
|
||||
logger.info("[CHAT] 过滤后无有效文本，结束")
|
||||
return False
|
||||
|
||||
latest = None # 最终待处理消息
|
||||
prioritized = [line for line in reversed(valid_lines) if ("报名" in line or "课程" in line or "价格" in line)] # 规则关键词优先
|
||||
if prioritized:
|
||||
latest = prioritized[0]
|
||||
logger.info(f"[CHAT] 规则关键词优先命中 latest={latest}")
|
||||
else:
|
||||
for line in reversed(valid_lines): # 从最新往前找可回复文本
|
||||
if self.should_reply(line):
|
||||
latest = line
|
||||
break
|
||||
|
||||
if not latest:
|
||||
logger.info("[CHAT] 没有命中可回复文本，结束")
|
||||
debug_log("skip_all_should_reply=false")
|
||||
return False
|
||||
|
||||
logger.info(f"[CHAT] 识别目标消息 latest={latest}")
|
||||
debug_log(f"latest_text={latest}")
|
||||
if not self.is_new_message(latest, contact_key): # 重复消息不再回
|
||||
debug_log("skip_duplicate_message")
|
||||
return False
|
||||
|
||||
reply = self.get_ai_reply(latest) # 请求后端生成回复
|
||||
if reply:
|
||||
debug_log(f"ai_reply={reply}")
|
||||
self.send_message(reply, window_rect) # 发送回复
|
||||
logger.info("[CHAT] 本次会话处理完成（已发送）")
|
||||
return True
|
||||
|
||||
debug_log("no_ai_reply")
|
||||
logger.info("[CHAT] 本次会话处理完成（无可发送回复）")
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.error(f"[CHAT] 处理聊天失败: {e}")
|
||||
return False
|
||||
|
||||
def run_forever(self):
|
||||
"""主循环：红点检测 -> 点击会话 -> 处理消息 -> 间隔轮询。"""
|
||||
logger.info("[LOOP] 监听循环启动")
|
||||
self.running = True # 标记运行中
|
||||
round_count = 0 # 轮次计数
|
||||
while self.running:
|
||||
try:
|
||||
round_count += 1 # 新一轮
|
||||
logger.info(f"[LOOP] 开始第 {round_count} 轮")
|
||||
debug_log(f"round={round_count}")
|
||||
window_rect = self.get_window_rect() # 获取窗口坐标
|
||||
if not window_rect:
|
||||
logger.info("[LOOP] 未取到窗口位置，等待下一轮")
|
||||
time.sleep(LOOP_INTERVAL)
|
||||
continue
|
||||
red_dots = self.detect_red_dots(window_rect) # 红点检测
|
||||
logger.info(f"[LOOP] 本轮红点数={len(red_dots)}")
|
||||
debug_log(f"red_dots_detected={len(red_dots)}")
|
||||
if not red_dots:
|
||||
logger.info("[LOOP] 未检测到红点，走当前会话兜底")
|
||||
debug_log("fallback_current_chat")
|
||||
self.process_current_chat(window_rect, "current_chat_fallback") # 无红点也扫当前会话
|
||||
time.sleep(LOOP_INTERVAL)
|
||||
continue
|
||||
for idx, red_dot in enumerate(red_dots, 1): # 逐个红点处理
|
||||
logger.info(f"[LOOP] 处理红点 {idx}/{len(red_dots)}")
|
||||
self.click_contact_by_red_dot(red_dot, window_rect) # 点击会话
|
||||
contact_key = f"{red_dot['x']}_{red_dot['y']}" # 联系人key
|
||||
self.process_current_chat(window_rect, contact_key) # 处理会话
|
||||
time.sleep(1) # 短暂节流
|
||||
logger.info(f"[LOOP] 第 {round_count} 轮结束，休眠 {LOOP_INTERVAL} 秒")
|
||||
time.sleep(LOOP_INTERVAL)
|
||||
except Exception as e:
|
||||
logger.error(f"[LOOP] 循环出错: {e}")
|
||||
time.sleep(3) # 异常后稍等再继续
|
||||
|
||||
def stop(self):
|
||||
logger.info("[LOOP] 收到停止信号")
|
||||
self.running = False # 置位退出
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
bot = WechatMultiChatBot() # 构造机器人
|
||||
bot.run_forever() # 进入主循环
|
||||
except KeyboardInterrupt:
|
||||
bot.stop() # Ctrl+C优雅停止
|
||||
except Exception as e:
|
||||
print(f"错误: {e}") # 启动异常输出
|
||||
Reference in New Issue
Block a user