zhoujie
d782bb6781
- 新增自动点赞功能【一键点赞】：支持关键词搜索笔记并随机批量点赞，提升账号活跃度 - 新增自动回复功能【一键回复】：自动扫描用户笔记的粉丝评论，使用AI生成并发送回复 - 扩展自动化调度器【定时调度】：支持点赞和回复任务的随机定时执行，模拟真人操作间隔 - 新增项目文档【文档】：添加README、CHANGELOG、CONTRIBUTING、LICENSE等核心文档文件 - 优化.gitignore文件【配置】：完善Python项目、IDE、敏感文件、日志等忽略规则 - 新增配置文件模板【配置】：提供config.example.json作为配置参考 - 优化MCP客户端【工具】：新增评论解析方法，支持从笔记详情中提取结构化评论数据
2256 lines
87 KiB
Python
2256 lines
87 KiB
Python
"""
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小红书 AI 爆文生产工坊 V2.0
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全自动工作台：灵感 -> 文案 -> 绘图 -> 发布 -> 运营
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"""
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import gradio as gr
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import os
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import re
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import json
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import time
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import logging
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import platform
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import subprocess
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import threading
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import random
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from datetime import datetime
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from PIL import Image
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import matplotlib
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import matplotlib.pyplot as plt
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from config_manager import ConfigManager, OUTPUT_DIR
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from llm_service import LLMService
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from sd_service import SDService, DEFAULT_NEGATIVE
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from mcp_client import MCPClient, get_mcp_client
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# ================= matplotlib 中文字体配置 =================
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_font_candidates = ["Microsoft YaHei", "SimHei", "PingFang SC", "WenQuanYi Micro Hei"]
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for _fn in _font_candidates:
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try:
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matplotlib.font_manager.findfont(_fn, fallback_to_default=False)
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plt.rcParams["font.sans-serif"] = [_fn]
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break
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except Exception:
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continue
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plt.rcParams["axes.unicode_minus"] = False
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# ================= 日志配置 =================
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
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handlers=[
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logging.StreamHandler(),
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logging.FileHandler("autobot.log", encoding="utf-8"),
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],
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)
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logger = logging.getLogger("autobot")
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# 强制不走代理连接本地服务
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os.environ["NO_PROXY"] = "127.0.0.1,localhost"
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# ================= 全局服务初始化 =================
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cfg = ConfigManager()
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cfg.ensure_workspace()
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mcp = get_mcp_client(cfg.get("mcp_url", "http://localhost:18060/mcp"))
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# ==================================================
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# LLM 多提供商管理
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# ==================================================
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def _get_llm_config() -> tuple[str, str, str]:
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"""获取当前激活 LLM 的 (api_key, base_url, model)"""
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p = cfg.get_active_llm()
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if p:
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return p["api_key"], p["base_url"], cfg.get("model", "")
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return "", "", ""
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||
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def connect_llm(provider_name):
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"""连接选中的 LLM 提供商并获取模型列表"""
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if not provider_name:
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return gr.update(choices=[], value=None), "⚠️ 请先选择或添加 LLM 提供商"
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cfg.set_active_llm(provider_name)
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p = cfg.get_active_llm()
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if not p:
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return gr.update(choices=[], value=None), "❌ 未找到该提供商配置"
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try:
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svc = LLMService(p["api_key"], p["base_url"])
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models = svc.get_models()
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if models:
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return (
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gr.update(choices=models, value=models[0]),
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f"✅ 已连接「{provider_name}」，加载 {len(models)} 个模型",
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)
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else:
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# API 无法获取模型列表，保留手动输入
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current_model = cfg.get("model", "")
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return (
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gr.update(choices=[current_model] if current_model else [], value=current_model or None),
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f"⚠️ 已连接「{provider_name}」，但未获取到模型列表，请手动输入模型名",
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)
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except Exception as e:
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logger.error("LLM 连接失败: %s", e)
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current_model = cfg.get("model", "")
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return (
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gr.update(choices=[current_model] if current_model else [], value=current_model or None),
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f"❌ 连接「{provider_name}」失败: {e}",
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)
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def add_llm_provider(name, api_key, base_url):
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"""添加新的 LLM 提供商"""
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msg = cfg.add_llm_provider(name, api_key, base_url)
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names = cfg.get_llm_provider_names()
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active = cfg.get("active_llm", "")
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return (
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gr.update(choices=names, value=active),
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msg,
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)
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def remove_llm_provider(provider_name):
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"""删除 LLM 提供商"""
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if not provider_name:
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return gr.update(choices=cfg.get_llm_provider_names(), value=cfg.get("active_llm", "")), "⚠️ 请先选择要删除的提供商"
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msg = cfg.remove_llm_provider(provider_name)
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names = cfg.get_llm_provider_names()
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active = cfg.get("active_llm", "")
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return (
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gr.update(choices=names, value=active),
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msg,
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)
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def on_provider_selected(provider_name):
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"""切换 LLM 提供商时更新显示信息"""
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if not provider_name:
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return "未选择提供商"
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for p in cfg.get_llm_providers():
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if p["name"] == provider_name:
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cfg.set_active_llm(provider_name)
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masked_key = p["api_key"][:8] + "***" if len(p["api_key"]) > 8 else "***"
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return f"**{provider_name}** \nAPI Key: `{masked_key}` \nBase URL: `{p['base_url']}`"
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return "未找到该提供商"
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# ==================================================
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# Tab 1: 内容创作
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# ==================================================
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def connect_sd(sd_url):
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"""连接 SD 并获取模型列表"""
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try:
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svc = SDService(sd_url)
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ok, msg = svc.check_connection()
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if ok:
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models = svc.get_models()
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cfg.set("sd_url", sd_url)
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return gr.update(choices=models, value=models[0] if models else None), f"✅ {msg}"
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return gr.update(choices=[]), f"❌ {msg}"
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except Exception as e:
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logger.error("SD 连接失败: %s", e)
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return gr.update(choices=[]), f"❌ SD 连接失败: {e}"
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def check_mcp_status(mcp_url):
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"""检查 MCP 连接状态"""
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try:
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client = get_mcp_client(mcp_url)
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ok, msg = client.check_connection()
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if ok:
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cfg.set("mcp_url", mcp_url)
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return f"✅ MCP 服务正常 - {msg}"
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return f"❌ {msg}"
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except Exception as e:
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return f"❌ MCP 连接失败: {e}"
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# ==================================================
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# 小红书账号登录
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# ==================================================
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def get_login_qrcode(mcp_url):
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"""获取小红书登录二维码"""
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try:
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client = get_mcp_client(mcp_url)
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result = client.get_login_qrcode()
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if "error" in result:
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return None, f"❌ 获取二维码失败: {result['error']}"
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qr_image = result.get("qr_image")
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msg = result.get("text", "")
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if qr_image:
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return qr_image, f"✅ 二维码已生成，请用小红书 App 扫码\n{msg}"
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return None, f"⚠️ 未获取到二维码图片，MCP 返回:\n{msg}"
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except Exception as e:
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logger.error("获取登录二维码失败: %s", e)
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return None, f"❌ 获取二维码失败: {e}"
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def logout_xhs(mcp_url):
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"""退出登录：清除 cookies 并重置本地 token"""
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try:
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client = get_mcp_client(mcp_url)
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result = client.delete_cookies()
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if "error" in result:
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return f"❌ 退出失败: {result['error']}"
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cfg.set("xsec_token", "")
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client._reset()
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return "✅ 已退出登录，可以重新扫码登录"
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except Exception as e:
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logger.error("退出登录失败: %s", e)
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return f"❌ 退出失败: {e}"
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def _auto_fetch_xsec_token(mcp_url) -> str:
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"""从推荐列表自动获取一个有效的 xsec_token"""
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try:
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client = get_mcp_client(mcp_url)
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entries = client.list_feeds_parsed()
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for e in entries:
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token = e.get("xsec_token", "")
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if token:
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return token
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except Exception as e:
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logger.warning("自动获取 xsec_token 失败: %s", e)
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return ""
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||
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def check_login(mcp_url):
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"""检查登录状态，登录成功后自动获取 xsec_token 并保存"""
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try:
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client = get_mcp_client(mcp_url)
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result = client.check_login_status()
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if "error" in result:
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return f"❌ {result['error']}", gr.update(), gr.update()
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text = result.get("text", "")
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if "未登录" in text:
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return f"🔴 {text}", gr.update(), gr.update()
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# 登录成功 → 自动获取 xsec_token
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token = _auto_fetch_xsec_token(mcp_url)
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if token:
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cfg.set("xsec_token", token)
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logger.info("自动获取 xsec_token 成功")
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return (
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f"🟢 {text}\n\n✅ xsec_token 已自动获取并保存",
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gr.update(value=cfg.get("my_user_id", "")),
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gr.update(value=token),
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)
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return f"🟢 {text}\n\n⚠️ 自动获取 xsec_token 失败，请手动刷新", gr.update(), gr.update()
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except Exception as e:
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return f"❌ 检查登录状态失败: {e}", gr.update(), gr.update()
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def save_my_user_id(user_id_input):
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"""保存用户 ID (验证 24 位十六进制格式)"""
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uid = (user_id_input or "").strip()
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if not uid:
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cfg.set("my_user_id", "")
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return "⚠️ 已清除用户 ID"
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if not re.match(r'^[0-9a-fA-F]{24}$', uid):
|
||
return (
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"❌ 格式错误！用户 ID 应为 24 位十六进制字符串\n"
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f"你输入的: `{uid}` ({len(uid)} 位)\n\n"
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"💡 如果你输入的是小红书号 (纯数字如 18688457507)，那不是 userId。"
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)
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cfg.set("my_user_id", uid)
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return f"✅ 用户 ID 已保存: `{uid}`"
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||
|
||
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||
def generate_copy(model, topic, style):
|
||
"""生成文案"""
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api_key, base_url, _ = _get_llm_config()
|
||
if not api_key:
|
||
return "", "", "", "", "❌ 请先配置并连接 LLM 提供商"
|
||
try:
|
||
svc = LLMService(api_key, base_url, model)
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||
data = svc.generate_copy(topic, style)
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||
cfg.set("model", model)
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||
tags = data.get("tags", [])
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||
return (
|
||
data.get("title", ""),
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||
data.get("content", ""),
|
||
data.get("sd_prompt", ""),
|
||
", ".join(tags) if tags else "",
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||
"✅ 文案生成完毕",
|
||
)
|
||
except Exception as e:
|
||
logger.error("文案生成失败: %s", e)
|
||
return "", "", "", "", f"❌ 生成失败: {e}"
|
||
|
||
|
||
def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale):
|
||
"""生成图片"""
|
||
if not model:
|
||
return None, [], "❌ 未选择 SD 模型"
|
||
try:
|
||
svc = SDService(sd_url)
|
||
images = svc.txt2img(
|
||
prompt=prompt,
|
||
negative_prompt=neg_prompt,
|
||
model=model,
|
||
steps=int(steps),
|
||
cfg_scale=float(cfg_scale),
|
||
)
|
||
return images, images, f"✅ 生成 {len(images)} 张图片"
|
||
except Exception as e:
|
||
logger.error("图片生成失败: %s", e)
|
||
return None, [], f"❌ 绘图失败: {e}"
|
||
|
||
|
||
def one_click_export(title, content, images):
|
||
"""导出文案和图片到本地"""
|
||
if not title:
|
||
return "❌ 无法导出：没有标题"
|
||
|
||
safe_title = re.sub(r'[\\/*?:"<>|]', "", title)[:20]
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||
folder_name = f"{int(time.time())}_{safe_title}"
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||
folder_path = os.path.join(OUTPUT_DIR, folder_name)
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||
os.makedirs(folder_path, exist_ok=True)
|
||
|
||
with open(os.path.join(folder_path, "文案.txt"), "w", encoding="utf-8") as f:
|
||
f.write(f"{title}\n\n{content}")
|
||
|
||
saved_paths = []
|
||
if images:
|
||
for idx, img in enumerate(images):
|
||
path = os.path.join(folder_path, f"图{idx+1}.png")
|
||
if isinstance(img, Image.Image):
|
||
img.save(path)
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||
saved_paths.append(os.path.abspath(path))
|
||
|
||
# 尝试打开文件夹
|
||
try:
|
||
abs_path = os.path.abspath(folder_path)
|
||
if platform.system() == "Windows":
|
||
os.startfile(abs_path)
|
||
elif platform.system() == "Darwin":
|
||
subprocess.call(["open", abs_path])
|
||
else:
|
||
subprocess.call(["xdg-open", abs_path])
|
||
except Exception:
|
||
pass
|
||
|
||
return f"✅ 已导出至: {folder_path} ({len(saved_paths)} 张图片)"
|
||
|
||
|
||
def publish_to_xhs(title, content, tags_str, images, local_images, mcp_url, schedule_time):
|
||
"""通过 MCP 发布到小红书"""
|
||
if not title:
|
||
return "❌ 缺少标题"
|
||
|
||
client = get_mcp_client(mcp_url)
|
||
|
||
# 收集图片路径
|
||
image_paths = []
|
||
|
||
# 先保存 AI 生成的图片到临时目录
|
||
if images:
|
||
temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish")
|
||
os.makedirs(temp_dir, exist_ok=True)
|
||
for idx, img in enumerate(images):
|
||
if isinstance(img, Image.Image):
|
||
path = os.path.abspath(os.path.join(temp_dir, f"ai_{idx}.png"))
|
||
img.save(path)
|
||
image_paths.append(path)
|
||
|
||
# 添加本地上传的图片
|
||
if local_images:
|
||
for img_file in local_images:
|
||
# Gradio File 组件返回的是 NamedString 或 tempfile path
|
||
img_path = img_file.name if hasattr(img_file, 'name') else str(img_file)
|
||
if os.path.exists(img_path):
|
||
image_paths.append(os.path.abspath(img_path))
|
||
|
||
if not image_paths:
|
||
return "❌ 至少需要 1 张图片才能发布"
|
||
|
||
# 解析标签
|
||
tags = [t.strip().lstrip("#") for t in tags_str.split(",") if t.strip()] if tags_str else None
|
||
|
||
# 定时发布
|
||
schedule = schedule_time if schedule_time and schedule_time.strip() else None
|
||
|
||
try:
|
||
result = client.publish_content(
|
||
title=title,
|
||
content=content,
|
||
images=image_paths,
|
||
tags=tags,
|
||
schedule_at=schedule,
|
||
)
|
||
if "error" in result:
|
||
return f"❌ 发布失败: {result['error']}"
|
||
return f"✅ 发布成功！\n{result.get('text', '')}"
|
||
except Exception as e:
|
||
logger.error("发布失败: %s", e)
|
||
return f"❌ 发布异常: {e}"
|
||
|
||
|
||
# ==================================================
|
||
# Tab 2: 热点探测
|
||
# ==================================================
|
||
|
||
|
||
def search_hotspots(keyword, sort_by, mcp_url):
|
||
"""搜索小红书热门内容"""
|
||
if not keyword:
|
||
return "❌ 请输入搜索关键词", ""
|
||
try:
|
||
client = get_mcp_client(mcp_url)
|
||
result = client.search_feeds(keyword, sort_by=sort_by)
|
||
if "error" in result:
|
||
return f"❌ 搜索失败: {result['error']}", ""
|
||
text = result.get("text", "无结果")
|
||
return "✅ 搜索完成", text
|
||
except Exception as e:
|
||
logger.error("热点搜索失败: %s", e)
|
||
return f"❌ 搜索失败: {e}", ""
|
||
|
||
|
||
def analyze_and_suggest(model, keyword, search_result):
|
||
"""AI 分析热点并给出建议"""
|
||
if not search_result:
|
||
return "❌ 请先搜索", "", ""
|
||
api_key, base_url, _ = _get_llm_config()
|
||
if not api_key:
|
||
return "❌ 请先配置 LLM 提供商", "", ""
|
||
try:
|
||
svc = LLMService(api_key, base_url, model)
|
||
analysis = svc.analyze_hotspots(search_result)
|
||
|
||
topics = "\n".join(f"• {t}" for t in analysis.get("hot_topics", []))
|
||
patterns = "\n".join(f"• {p}" for p in analysis.get("title_patterns", []))
|
||
suggestions = "\n".join(
|
||
f"**{s['topic']}** - {s['reason']}"
|
||
for s in analysis.get("suggestions", [])
|
||
)
|
||
structure = analysis.get("content_structure", "")
|
||
|
||
summary = (
|
||
f"## 🔥 热门选题\n{topics}\n\n"
|
||
f"## 📝 标题套路\n{patterns}\n\n"
|
||
f"## 📐 内容结构\n{structure}\n\n"
|
||
f"## 💡 推荐选题\n{suggestions}"
|
||
)
|
||
return "✅ 分析完成", summary, keyword
|
||
except Exception as e:
|
||
logger.error("热点分析失败: %s", e)
|
||
return f"❌ 分析失败: {e}", "", ""
|
||
|
||
|
||
def generate_from_hotspot(model, topic_from_hotspot, style, search_result):
|
||
"""基于热点分析生成文案"""
|
||
if not topic_from_hotspot:
|
||
return "", "", "", "", "❌ 请先选择或输入选题"
|
||
api_key, base_url, _ = _get_llm_config()
|
||
if not api_key:
|
||
return "", "", "", "", "❌ 请先配置 LLM 提供商"
|
||
try:
|
||
svc = LLMService(api_key, base_url, model)
|
||
data = svc.generate_copy_with_reference(
|
||
topic=topic_from_hotspot,
|
||
style=style,
|
||
reference_notes=search_result[:2000], # 截断防止超长
|
||
)
|
||
tags = data.get("tags", [])
|
||
return (
|
||
data.get("title", ""),
|
||
data.get("content", ""),
|
||
data.get("sd_prompt", ""),
|
||
", ".join(tags),
|
||
"✅ 基于热点的文案已生成",
|
||
)
|
||
except Exception as e:
|
||
return "", "", "", "", f"❌ 生成失败: {e}"
|
||
|
||
|
||
# ==================================================
|
||
# Tab 3: 评论管家
|
||
# ==================================================
|
||
|
||
# ---- 共用: 笔记列表缓存 ----
|
||
|
||
# 主动评论缓存
|
||
_cached_proactive_entries: list[dict] = []
|
||
# 我的笔记评论缓存
|
||
_cached_my_note_entries: list[dict] = []
|
||
|
||
|
||
def _fetch_and_cache(keyword, mcp_url, cache_name="proactive"):
|
||
"""通用: 获取笔记列表并缓存"""
|
||
global _cached_proactive_entries, _cached_my_note_entries
|
||
try:
|
||
client = get_mcp_client(mcp_url)
|
||
if keyword and keyword.strip():
|
||
entries = client.search_feeds_parsed(keyword.strip())
|
||
src = f"搜索「{keyword.strip()}」"
|
||
else:
|
||
entries = client.list_feeds_parsed()
|
||
src = "首页推荐"
|
||
|
||
if cache_name == "proactive":
|
||
_cached_proactive_entries = entries
|
||
else:
|
||
_cached_my_note_entries = entries
|
||
|
||
if not entries:
|
||
return gr.update(choices=[], value=None), f"⚠️ 从{src}未找到笔记"
|
||
|
||
choices = []
|
||
for i, e in enumerate(entries):
|
||
title_short = (e["title"] or "无标题")[:28]
|
||
label = f"[{i+1}] {title_short} | @{e['author'] or '未知'} | ❤ {e['likes']}"
|
||
choices.append(label)
|
||
|
||
return (
|
||
gr.update(choices=choices, value=choices[0]),
|
||
f"✅ 从{src}获取 {len(entries)} 条笔记",
|
||
)
|
||
except Exception as e:
|
||
if cache_name == "proactive":
|
||
_cached_proactive_entries = []
|
||
else:
|
||
_cached_my_note_entries = []
|
||
return gr.update(choices=[], value=None), f"❌ {e}"
|
||
|
||
|
||
def _pick_from_cache(selected, cache_name="proactive"):
|
||
"""通用: 从缓存中提取选中条目的 feed_id / xsec_token / title"""
|
||
cache = _cached_proactive_entries if cache_name == "proactive" else _cached_my_note_entries
|
||
if not selected or not cache:
|
||
return "", "", ""
|
||
try:
|
||
# 尝试从 [N] 前缀提取序号
|
||
idx = int(selected.split("]")[0].replace("[", "")) - 1
|
||
if 0 <= idx < len(cache):
|
||
e = cache[idx]
|
||
return e["feed_id"], e["xsec_token"], e.get("title", "")
|
||
except (ValueError, IndexError):
|
||
pass
|
||
# 回退: 模糊匹配标题
|
||
for e in cache:
|
||
if e.get("title", "")[:15] in selected:
|
||
return e["feed_id"], e["xsec_token"], e.get("title", "")
|
||
return "", "", ""
|
||
|
||
|
||
# ---- 模块 A: 主动评论他人 ----
|
||
|
||
def fetch_proactive_notes(keyword, mcp_url):
|
||
return _fetch_and_cache(keyword, mcp_url, "proactive")
|
||
|
||
|
||
def on_proactive_note_selected(selected):
|
||
return _pick_from_cache(selected, "proactive")
|
||
|
||
|
||
def load_note_for_comment(feed_id, xsec_token, mcp_url):
|
||
"""加载目标笔记详情 (标题+正文+已有评论), 用于 AI 分析"""
|
||
if not feed_id or not xsec_token:
|
||
return "❌ 请先选择笔记", "", "", ""
|
||
try:
|
||
client = get_mcp_client(mcp_url)
|
||
result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True)
|
||
if "error" in result:
|
||
return f"❌ {result['error']}", "", "", ""
|
||
full_text = result.get("text", "")
|
||
# 尝试分离正文和评论
|
||
if "评论" in full_text:
|
||
parts = full_text.split("评论", 1)
|
||
content_part = parts[0].strip()
|
||
comments_part = "评论" + parts[1] if len(parts) > 1 else ""
|
||
else:
|
||
content_part = full_text[:500]
|
||
comments_part = ""
|
||
return "✅ 笔记内容已加载", content_part[:800], comments_part[:1500], full_text
|
||
except Exception as e:
|
||
return f"❌ {e}", "", "", ""
|
||
|
||
|
||
def ai_generate_comment(model, persona,
|
||
post_title, post_content, existing_comments):
|
||
"""AI 生成主动评论"""
|
||
api_key, base_url, _ = _get_llm_config()
|
||
if not api_key:
|
||
return "⚠️ 请先配置 LLM 提供商", "❌ LLM 未配置"
|
||
if not model:
|
||
return "⚠️ 请先连接 LLM", "❌ 未选模型"
|
||
if not post_title and not post_content:
|
||
return "⚠️ 请先加载笔记内容", "❌ 无笔记内容"
|
||
try:
|
||
svc = LLMService(api_key, base_url, model)
|
||
comment = svc.generate_proactive_comment(
|
||
persona, post_title, post_content[:600], existing_comments[:800]
|
||
)
|
||
return comment, "✅ 评论已生成"
|
||
except Exception as e:
|
||
logger.error(f"AI 评论生成失败: {e}")
|
||
return f"生成失败: {e}", f"❌ {e}"
|
||
|
||
|
||
def send_comment(feed_id, xsec_token, comment_content, mcp_url):
|
||
"""发送评论到别人的笔记"""
|
||
if not all([feed_id, xsec_token, comment_content]):
|
||
return "❌ 缺少必要参数 (笔记ID / token / 评论内容)"
|
||
try:
|
||
client = get_mcp_client(mcp_url)
|
||
result = client.post_comment(feed_id, xsec_token, comment_content)
|
||
if "error" in result:
|
||
return f"❌ {result['error']}"
|
||
return "✅ 评论已发送！"
|
||
except Exception as e:
|
||
return f"❌ {e}"
|
||
|
||
|
||
# ---- 模块 B: 回复我的笔记评论 ----
|
||
|
||
def fetch_my_notes(mcp_url):
|
||
"""通过已保存的 userId 获取我的笔记列表"""
|
||
global _cached_my_note_entries
|
||
my_uid = cfg.get("my_user_id", "")
|
||
xsec = cfg.get("xsec_token", "")
|
||
if not my_uid:
|
||
return (
|
||
gr.update(choices=[], value=None),
|
||
"❌ 未配置用户 ID，请先到「账号登录」页填写并保存",
|
||
)
|
||
if not xsec:
|
||
return (
|
||
gr.update(choices=[], value=None),
|
||
"❌ 未获取 xsec_token，请先登录",
|
||
)
|
||
try:
|
||
client = get_mcp_client(mcp_url)
|
||
result = client.get_user_profile(my_uid, xsec)
|
||
if "error" in result:
|
||
return gr.update(choices=[], value=None), f"❌ {result['error']}"
|
||
|
||
# 从 raw 中解析 feeds
|
||
raw = result.get("raw", {})
|
||
text = result.get("text", "")
|
||
data = None
|
||
if raw and isinstance(raw, dict):
|
||
for item in raw.get("content", []):
|
||
if item.get("type") == "text":
|
||
try:
|
||
data = json.loads(item["text"])
|
||
except (json.JSONDecodeError, KeyError):
|
||
pass
|
||
if not data:
|
||
try:
|
||
data = json.loads(text)
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
|
||
feeds = (data or {}).get("feeds") or []
|
||
if not feeds:
|
||
return (
|
||
gr.update(choices=[], value=None),
|
||
"⚠️ 未找到你的笔记，可能账号还没有发布内容",
|
||
)
|
||
|
||
entries = []
|
||
for f in feeds:
|
||
nc = f.get("noteCard") or {}
|
||
user = nc.get("user") or {}
|
||
interact = nc.get("interactInfo") or {}
|
||
entries.append({
|
||
"feed_id": f.get("id", ""),
|
||
"xsec_token": f.get("xsecToken", ""),
|
||
"title": nc.get("displayTitle", "未知标题"),
|
||
"author": user.get("nickname", user.get("nickName", "")),
|
||
"user_id": user.get("userId", ""),
|
||
"likes": interact.get("likedCount", "0"),
|
||
"type": nc.get("type", ""),
|
||
})
|
||
|
||
_cached_my_note_entries = entries
|
||
choices = [
|
||
f"[{i+1}] {e['title'][:20]} | {e['type']} | ❤{e['likes']}"
|
||
for i, e in enumerate(entries)
|
||
]
|
||
return (
|
||
gr.update(choices=choices, value=choices[0] if choices else None),
|
||
f"✅ 找到 {len(entries)} 篇笔记",
|
||
)
|
||
except Exception as e:
|
||
return gr.update(choices=[], value=None), f"❌ {e}"
|
||
|
||
|
||
def on_my_note_selected(selected):
|
||
return _pick_from_cache(selected, "my_notes")
|
||
|
||
|
||
def fetch_my_note_comments(feed_id, xsec_token, mcp_url):
|
||
"""获取我的笔记的评论列表"""
|
||
if not feed_id or not xsec_token:
|
||
return "❌ 请先选择笔记", ""
|
||
try:
|
||
client = get_mcp_client(mcp_url)
|
||
result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True)
|
||
if "error" in result:
|
||
return f"❌ {result['error']}", ""
|
||
return "✅ 评论加载完成", result.get("text", "暂无评论")
|
||
except Exception as e:
|
||
return f"❌ {e}", ""
|
||
|
||
|
||
def ai_reply_comment(model, persona, post_title, comment_text):
|
||
"""AI 生成评论回复"""
|
||
api_key, base_url, _ = _get_llm_config()
|
||
if not api_key:
|
||
return "⚠️ 请先配置 LLM 提供商", "❌ LLM 未配置"
|
||
if not model:
|
||
return "⚠️ 请先连接 LLM 并选择模型", "❌ 未选择模型"
|
||
if not comment_text:
|
||
return "请输入需要回复的评论内容", "⚠️ 请输入评论"
|
||
try:
|
||
svc = LLMService(api_key, base_url, model)
|
||
reply = svc.generate_reply(persona, post_title, comment_text)
|
||
return reply, "✅ 回复已生成"
|
||
except Exception as e:
|
||
logger.error(f"AI 回复生成失败: {e}")
|
||
return f"生成失败: {e}", f"❌ {e}"
|
||
|
||
|
||
def send_reply(feed_id, xsec_token, reply_content, mcp_url):
|
||
"""发送评论回复"""
|
||
if not all([feed_id, xsec_token, reply_content]):
|
||
return "❌ 缺少必要参数"
|
||
try:
|
||
client = get_mcp_client(mcp_url)
|
||
result = client.post_comment(feed_id, xsec_token, reply_content)
|
||
if "error" in result:
|
||
return f"❌ 回复失败: {result['error']}"
|
||
return "✅ 回复已发送"
|
||
except Exception as e:
|
||
return f"❌ 发送失败: {e}"
|
||
|
||
|
||
# ==================================================
|
||
# Tab 4: 数据看板 (我的账号)
|
||
# ==================================================
|
||
|
||
|
||
def _parse_profile_json(text: str):
|
||
"""尝试从文本中解析用户 profile JSON"""
|
||
if not text:
|
||
return None
|
||
# 直接 JSON
|
||
try:
|
||
return json.loads(text)
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
# 可能包含 Markdown 代码块
|
||
m = re.search(r'```(?:json)?\s*\n([\s\S]+?)\n```', text)
|
||
if m:
|
||
try:
|
||
return json.loads(m.group(1))
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
return None
|
||
|
||
|
||
def _parse_count(val) -> float:
|
||
"""解析数字字符串, 支持 '1.2万' 格式"""
|
||
if isinstance(val, (int, float)):
|
||
return float(val)
|
||
s = str(val).strip()
|
||
if "万" in s:
|
||
try:
|
||
return float(s.replace("万", "")) * 10000
|
||
except ValueError:
|
||
pass
|
||
try:
|
||
return float(s)
|
||
except ValueError:
|
||
return 0.0
|
||
|
||
|
||
def fetch_my_profile(user_id, xsec_token, mcp_url):
|
||
"""获取我的账号数据, 返回结构化信息 + 可视化图表"""
|
||
if not user_id or not xsec_token:
|
||
return "❌ 请填写你的用户 ID 和 xsec_token", "", None, None, None
|
||
try:
|
||
client = get_mcp_client(mcp_url)
|
||
result = client.get_user_profile(user_id, xsec_token)
|
||
if "error" in result:
|
||
return f"❌ {result['error']}", "", None, None, None
|
||
|
||
raw = result.get("raw", {})
|
||
text = result.get("text", "")
|
||
|
||
# 尝试从 raw 或 text 解析 JSON
|
||
data = None
|
||
if raw and isinstance(raw, dict):
|
||
content_list = raw.get("content", [])
|
||
for item in content_list:
|
||
if item.get("type") == "text":
|
||
data = _parse_profile_json(item.get("text", ""))
|
||
if data:
|
||
break
|
||
if not data:
|
||
data = _parse_profile_json(text)
|
||
if not data:
|
||
return "✅ 数据加载完成 (纯文本)", text, None, None, None
|
||
|
||
# ---- 提取基本信息 (注意 MCP 对新号可能返回 null) ----
|
||
basic = data.get("userBasicInfo") or {}
|
||
interactions = data.get("interactions") or []
|
||
feeds = data.get("feeds") or []
|
||
|
||
gender_map = {0: "未知", 1: "男", 2: "女"}
|
||
info_lines = [
|
||
f"## 👤 {basic.get('nickname', '未知')}",
|
||
f"- **小红书号**: {basic.get('redId', '-')}",
|
||
f"- **性别**: {gender_map.get(basic.get('gender', 0), '未知')}",
|
||
f"- **IP 属地**: {basic.get('ipLocation', '-')}",
|
||
f"- **简介**: {basic.get('desc', '-')}",
|
||
"",
|
||
"### 📊 核心数据",
|
||
]
|
||
for inter in interactions:
|
||
info_lines.append(f"- **{inter.get('name', '')}**: {inter.get('count', '0')}")
|
||
|
||
info_lines.append(f"\n### 📝 展示笔记: {len(feeds)} 篇")
|
||
profile_md = "\n".join(info_lines)
|
||
|
||
# ---- 互动数据柱状图 ----
|
||
fig_interact = None
|
||
if interactions:
|
||
inter_data = {i["name"]: _parse_count(i["count"]) for i in interactions}
|
||
fig_interact, ax = plt.subplots(figsize=(4, 3), dpi=100)
|
||
labels = list(inter_data.keys())
|
||
values = list(inter_data.values())
|
||
colors = ["#FF6B6B", "#4ECDC4", "#45B7D1"][:len(labels)]
|
||
ax.bar(labels, values, color=colors, edgecolor="white", linewidth=0.5)
|
||
ax.set_title("账号核心指标", fontsize=12, fontweight="bold")
|
||
for i, v in enumerate(values):
|
||
display = f"{v/10000:.1f}万" if v >= 10000 else str(int(v))
|
||
ax.text(i, v + max(values) * 0.02, display, ha="center", fontsize=9)
|
||
ax.set_ylabel("")
|
||
ax.spines["top"].set_visible(False)
|
||
ax.spines["right"].set_visible(False)
|
||
fig_interact.tight_layout()
|
||
|
||
# ---- 笔记点赞分布图 ----
|
||
fig_notes = None
|
||
if feeds:
|
||
titles, likes = [], []
|
||
for f in feeds[:15]:
|
||
nc = f.get("noteCard") or {}
|
||
t = (nc.get("displayTitle", "") or "无标题")[:12]
|
||
lk = _parse_count((nc.get("interactInfo") or {}).get("likedCount", "0"))
|
||
titles.append(t)
|
||
likes.append(lk)
|
||
|
||
fig_notes, ax2 = plt.subplots(figsize=(7, 3.5), dpi=100)
|
||
ax2.barh(range(len(titles)), likes, color="#FF6B6B", edgecolor="white")
|
||
ax2.set_yticks(range(len(titles)))
|
||
ax2.set_yticklabels(titles, fontsize=8)
|
||
ax2.set_title(f"笔记点赞排行 (Top {len(titles)})", fontsize=12, fontweight="bold")
|
||
ax2.invert_yaxis()
|
||
for i, v in enumerate(likes):
|
||
display = f"{v/10000:.1f}万" if v >= 10000 else str(int(v))
|
||
ax2.text(v + max(likes) * 0.01 if max(likes) > 0 else 0, i, display, va="center", fontsize=8)
|
||
ax2.spines["top"].set_visible(False)
|
||
ax2.spines["right"].set_visible(False)
|
||
fig_notes.tight_layout()
|
||
|
||
# ---- 笔记详情表格 (Markdown) ----
|
||
table_lines = [
|
||
"### 📋 笔记数据明细",
|
||
"| # | 标题 | 类型 | ❤ 点赞 |",
|
||
"|---|------|------|--------|",
|
||
]
|
||
for i, f in enumerate(feeds):
|
||
nc = f.get("noteCard") or {}
|
||
t = (nc.get("displayTitle", "") or "无标题")[:25]
|
||
tp = "📹 视频" if nc.get("type") == "video" else "📷 图文"
|
||
lk = (nc.get("interactInfo") or {}).get("likedCount", "0")
|
||
table_lines.append(f"| {i+1} | {t} | {tp} | {lk} |")
|
||
notes_table = "\n".join(table_lines)
|
||
|
||
return "✅ 数据加载完成", profile_md, fig_interact, fig_notes, notes_table
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取我的数据失败: {e}")
|
||
return f"❌ {e}", "", None, None, None
|
||
|
||
|
||
# ==================================================
|
||
# 自动化运营模块
|
||
# ==================================================
|
||
|
||
# 自动化状态
|
||
_auto_running = threading.Event()
|
||
_auto_thread: threading.Thread | None = None
|
||
_auto_log: list[str] = []
|
||
|
||
DEFAULT_TOPICS = [
|
||
"春季穿搭", "通勤穿搭", "约会穿搭", "显瘦穿搭", "平价好物",
|
||
"护肤心得", "妆容教程", "好物分享", "生活好物", "减脂餐分享",
|
||
"居家好物", "收纳技巧", "咖啡探店", "书单推荐", "旅行攻略",
|
||
]
|
||
|
||
DEFAULT_STYLES = ["好物种草", "干货教程", "情绪共鸣", "生活Vlog", "测评避雷"]
|
||
|
||
DEFAULT_COMMENT_KEYWORDS = [
|
||
"穿搭", "美食", "护肤", "好物推荐", "旅行", "生活日常", "减脂",
|
||
]
|
||
|
||
|
||
def _auto_log_append(msg: str):
|
||
"""记录自动化日志"""
|
||
ts = datetime.now().strftime("%H:%M:%S")
|
||
entry = f"[{ts}] {msg}"
|
||
_auto_log.append(entry)
|
||
if len(_auto_log) > 500:
|
||
_auto_log[:] = _auto_log[-300:]
|
||
logger.info("[自动化] %s", msg)
|
||
|
||
|
||
def _auto_comment_with_log(keywords_str, mcp_url, model, persona_text):
|
||
"""一键评论 + 同步刷新日志"""
|
||
msg = auto_comment_once(keywords_str, mcp_url, model, persona_text)
|
||
return msg, get_auto_log()
|
||
|
||
|
||
def auto_comment_once(keywords_str, mcp_url, model, persona_text):
|
||
"""一键评论：自动搜索高赞笔记 → AI生成评论 → 发送"""
|
||
try:
|
||
keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else DEFAULT_COMMENT_KEYWORDS
|
||
keyword = random.choice(keywords)
|
||
_auto_log_append(f"🔍 搜索关键词: {keyword}")
|
||
|
||
client = get_mcp_client(mcp_url)
|
||
|
||
# 搜索高赞笔记
|
||
entries = client.search_feeds_parsed(keyword, sort_by="最多点赞")
|
||
if not entries:
|
||
_auto_log_append("⚠️ 搜索无结果，尝试推荐列表")
|
||
entries = client.list_feeds_parsed()
|
||
if not entries:
|
||
return "❌ 未找到任何笔记"
|
||
|
||
# 过滤掉自己的笔记
|
||
my_uid = cfg.get("my_user_id", "")
|
||
if my_uid:
|
||
filtered = [e for e in entries if e.get("user_id") != my_uid]
|
||
if filtered:
|
||
entries = filtered
|
||
|
||
# 从前10个中随机选择
|
||
target = random.choice(entries[:min(10, len(entries))])
|
||
feed_id = target["feed_id"]
|
||
xsec_token = target["xsec_token"]
|
||
title = target.get("title", "未知")
|
||
_auto_log_append(f"🎯 选中: {title[:30]} (@{target.get('author', '未知')})")
|
||
|
||
if not feed_id or not xsec_token:
|
||
return "❌ 笔记缺少必要参数 (feed_id/xsec_token)"
|
||
|
||
# 模拟浏览延迟
|
||
time.sleep(random.uniform(2, 5))
|
||
|
||
# 加载笔记详情
|
||
result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True)
|
||
if "error" in result:
|
||
return f"❌ 加载笔记失败: {result['error']}"
|
||
|
||
full_text = result.get("text", "")
|
||
if "评论" in full_text:
|
||
parts = full_text.split("评论", 1)
|
||
content_part = parts[0].strip()[:600]
|
||
comments_part = ("评论" + parts[1])[:800] if len(parts) > 1 else ""
|
||
else:
|
||
content_part = full_text[:500]
|
||
comments_part = ""
|
||
|
||
# AI 生成评论
|
||
api_key, base_url, _ = _get_llm_config()
|
||
if not api_key:
|
||
return "❌ LLM 未配置，请先在全局设置中配置提供商"
|
||
|
||
svc = LLMService(api_key, base_url, model)
|
||
comment = svc.generate_proactive_comment(
|
||
persona_text, title, content_part, comments_part
|
||
)
|
||
_auto_log_append(f"💬 生成评论: {comment[:60]}...")
|
||
|
||
# 随机等待后发送
|
||
time.sleep(random.uniform(3, 8))
|
||
result = client.post_comment(feed_id, xsec_token, comment)
|
||
resp_text = result.get("text", "")
|
||
_auto_log_append(f"📡 MCP 响应: {resp_text[:200]}")
|
||
|
||
if "error" in result:
|
||
_auto_log_append(f"❌ 评论发送失败: {result['error']}")
|
||
return f"❌ 评论发送失败: {result['error']}"
|
||
|
||
# 检查是否真正成功
|
||
if "成功" not in resp_text and "success" not in resp_text.lower() and not resp_text:
|
||
_auto_log_append(f"⚠️ 评论可能未成功，MCP 原始响应: {result}")
|
||
return f"⚠️ 评论状态不确定，请手动检查\nMCP 响应: {resp_text[:300]}\n📝 评论: {comment}"
|
||
|
||
_auto_log_append(f"✅ 评论已发送到「{title[:20]}」")
|
||
return f"✅ 已评论「{title[:25]}」\n📝 评论: {comment}\n\n💡 小红书可能有内容审核延迟，请稍等 1-2 分钟后查看"
|
||
|
||
except Exception as e:
|
||
_auto_log_append(f"❌ 一键评论异常: {e}")
|
||
return f"❌ 评论失败: {e}"
|
||
|
||
|
||
def _auto_like_with_log(keywords_str, like_count, mcp_url):
|
||
"""一键点赞 + 同步刷新日志"""
|
||
msg = auto_like_once(keywords_str, like_count, mcp_url)
|
||
return msg, get_auto_log()
|
||
|
||
|
||
def auto_like_once(keywords_str, like_count, mcp_url):
|
||
"""一键点赞：搜索/推荐笔记 → 随机选择 → 批量点赞"""
|
||
try:
|
||
keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else DEFAULT_COMMENT_KEYWORDS
|
||
keyword = random.choice(keywords)
|
||
like_count = int(like_count) if like_count else 5
|
||
_auto_log_append(f"👍 点赞关键词: {keyword} | 目标: {like_count} 个")
|
||
|
||
client = get_mcp_client(mcp_url)
|
||
|
||
# 搜索笔记
|
||
entries = client.search_feeds_parsed(keyword, sort_by="综合")
|
||
if not entries:
|
||
_auto_log_append("⚠️ 搜索无结果，尝试推荐列表")
|
||
entries = client.list_feeds_parsed()
|
||
if not entries:
|
||
return "❌ 未找到任何笔记"
|
||
|
||
# 过滤自己的笔记
|
||
my_uid = cfg.get("my_user_id", "")
|
||
if my_uid:
|
||
filtered = [e for e in entries if e.get("user_id") != my_uid]
|
||
if filtered:
|
||
entries = filtered
|
||
|
||
# 随机打乱，取前 N 个
|
||
random.shuffle(entries)
|
||
targets = entries[:min(like_count, len(entries))]
|
||
|
||
liked = 0
|
||
for target in targets:
|
||
feed_id = target.get("feed_id", "")
|
||
xsec_token = target.get("xsec_token", "")
|
||
title = target.get("title", "未知")[:25]
|
||
|
||
if not feed_id or not xsec_token:
|
||
continue
|
||
|
||
# 模拟浏览延迟
|
||
time.sleep(random.uniform(2, 6))
|
||
|
||
result = client.like_feed(feed_id, xsec_token)
|
||
if "error" in result:
|
||
_auto_log_append(f" ❌ 点赞失败「{title}」: {result['error']}")
|
||
else:
|
||
liked += 1
|
||
_auto_log_append(f" ❤️ 已点赞「{title}」@{target.get('author', '未知')}")
|
||
|
||
_auto_log_append(f"👍 点赞完成: 成功 {liked}/{len(targets)}")
|
||
return f"✅ 点赞完成！成功 {liked}/{len(targets)} 个"
|
||
|
||
except Exception as e:
|
||
_auto_log_append(f"❌ 一键点赞异常: {e}")
|
||
return f"❌ 点赞失败: {e}"
|
||
|
||
|
||
def _auto_publish_with_log(topics_str, mcp_url, sd_url_val, sd_model_name, model):
|
||
"""一键发布 + 同步刷新日志"""
|
||
msg = auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model)
|
||
return msg, get_auto_log()
|
||
|
||
|
||
def _auto_reply_with_log(max_replies, mcp_url, model, persona_text):
|
||
"""一键回复 + 同步刷新日志"""
|
||
msg = auto_reply_once(max_replies, mcp_url, model, persona_text)
|
||
return msg, get_auto_log()
|
||
|
||
|
||
def auto_reply_once(max_replies, mcp_url, model, persona_text):
|
||
"""一键回复：获取我的笔记 → 加载评论 → AI 生成回复 → 发送"""
|
||
try:
|
||
my_uid = cfg.get("my_user_id", "")
|
||
xsec = cfg.get("xsec_token", "")
|
||
if not my_uid:
|
||
return "❌ 未配置用户 ID，请到「账号登录」页填写"
|
||
if not xsec:
|
||
return "❌ 未获取 xsec_token，请先登录"
|
||
|
||
api_key, base_url, _ = _get_llm_config()
|
||
if not api_key:
|
||
return "❌ LLM 未配置"
|
||
|
||
max_replies = int(max_replies) if max_replies else 3
|
||
client = get_mcp_client(mcp_url)
|
||
_auto_log_append("💌 开始自动回复评论...")
|
||
|
||
# Step 1: 获取我的笔记列表
|
||
result = client.get_user_profile(my_uid, xsec)
|
||
if "error" in result:
|
||
_auto_log_append(f"❌ 获取我的笔记失败: {result['error']}")
|
||
return f"❌ 获取我的笔记失败: {result['error']}"
|
||
|
||
# 解析笔记列表
|
||
raw = result.get("raw", {})
|
||
text = result.get("text", "")
|
||
data = None
|
||
if raw and isinstance(raw, dict):
|
||
for item in raw.get("content", []):
|
||
if item.get("type") == "text":
|
||
try:
|
||
data = json.loads(item["text"])
|
||
except (json.JSONDecodeError, KeyError):
|
||
pass
|
||
if not data:
|
||
try:
|
||
data = json.loads(text)
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
|
||
feeds = (data or {}).get("feeds") or []
|
||
if not feeds:
|
||
_auto_log_append("⚠️ 未找到任何笔记")
|
||
return "⚠️ 未找到你的笔记"
|
||
|
||
# 构建笔记条目
|
||
my_entries = []
|
||
for f in feeds:
|
||
nc = f.get("noteCard") or {}
|
||
my_entries.append({
|
||
"feed_id": f.get("id", ""),
|
||
"xsec_token": f.get("xsecToken", ""),
|
||
"title": nc.get("displayTitle", "未知标题"),
|
||
})
|
||
|
||
_auto_log_append(f"📝 找到 {len(my_entries)} 篇笔记，开始扫描评论...")
|
||
|
||
# Step 2: 遍历笔记，找到未回复的评论
|
||
total_replied = 0
|
||
svc = LLMService(api_key, base_url, model)
|
||
|
||
for entry in my_entries:
|
||
if total_replied >= max_replies:
|
||
break
|
||
|
||
feed_id = entry["feed_id"]
|
||
xsec_token = entry["xsec_token"]
|
||
title = entry["title"]
|
||
|
||
if not feed_id or not xsec_token:
|
||
continue
|
||
|
||
time.sleep(random.uniform(1, 3))
|
||
|
||
# 加载笔记详情（含评论）
|
||
detail = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True)
|
||
if "error" in detail:
|
||
_auto_log_append(f"⚠️ 加载「{title[:15]}」评论失败，跳过")
|
||
continue
|
||
|
||
full_text = detail.get("text", "")
|
||
|
||
# 解析评论
|
||
comments = client._parse_comments(full_text)
|
||
if not comments:
|
||
continue
|
||
|
||
# 过滤掉自己的评论，只回复他人
|
||
other_comments = [
|
||
c for c in comments
|
||
if c.get("user_id") and c["user_id"] != my_uid and c.get("content")
|
||
]
|
||
|
||
if not other_comments:
|
||
continue
|
||
|
||
_auto_log_append(f"📖「{title[:20]}」有 {len(other_comments)} 条他人评论")
|
||
|
||
for comment in other_comments:
|
||
if total_replied >= max_replies:
|
||
break
|
||
|
||
comment_id = comment.get("comment_id", "")
|
||
comment_uid = comment.get("user_id", "")
|
||
comment_text = comment.get("content", "")
|
||
nickname = comment.get("nickname", "网友")
|
||
|
||
if not comment_text.strip():
|
||
continue
|
||
|
||
_auto_log_append(f" 💬 @{nickname}: {comment_text[:40]}...")
|
||
|
||
# AI 生成回复
|
||
try:
|
||
reply = svc.generate_reply(persona_text, title, comment_text)
|
||
except Exception as e:
|
||
_auto_log_append(f" ❌ AI 回复生成失败: {e}")
|
||
continue
|
||
|
||
_auto_log_append(f" 🤖 回复: {reply[:50]}...")
|
||
|
||
# 发送回复
|
||
time.sleep(random.uniform(2, 6))
|
||
|
||
if comment_id and comment_uid:
|
||
# 使用 reply_comment 精确回复
|
||
resp = client.reply_comment(
|
||
feed_id, xsec_token, comment_id, comment_uid, reply
|
||
)
|
||
else:
|
||
# 没有 comment_id 就用 post_comment 发到笔记下
|
||
resp = client.post_comment(feed_id, xsec_token, f"@{nickname} {reply}")
|
||
|
||
resp_text = resp.get("text", "")
|
||
if "error" in resp:
|
||
_auto_log_append(f" ❌ 回复发送失败: {resp['error']}")
|
||
else:
|
||
_auto_log_append(f" ✅ 已回复 @{nickname}")
|
||
total_replied += 1
|
||
|
||
if total_replied == 0:
|
||
_auto_log_append("ℹ️ 没有找到需要回复的新评论")
|
||
return "ℹ️ 没有找到需要回复的新评论\n\n💡 可能所有评论都已回复过"
|
||
else:
|
||
_auto_log_append(f"✅ 自动回复完成，共回复 {total_replied} 条评论")
|
||
return f"✅ 自动回复完成！共回复 {total_replied} 条评论\n\n💡 小红书审核可能有延迟，请稍后查看"
|
||
|
||
except Exception as e:
|
||
_auto_log_append(f"❌ 自动回复异常: {e}")
|
||
return f"❌ 自动回复失败: {e}"
|
||
|
||
|
||
def auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model):
|
||
"""一键发布：自动生成文案 → 生成图片 → 发布到小红书"""
|
||
try:
|
||
topics = [t.strip() for t in topics_str.split(",") if t.strip()] if topics_str else DEFAULT_TOPICS
|
||
topic = random.choice(topics)
|
||
style = random.choice(DEFAULT_STYLES)
|
||
_auto_log_append(f"📝 主题: {topic} | 风格: {style}")
|
||
|
||
# 生成文案
|
||
api_key, base_url, _ = _get_llm_config()
|
||
if not api_key:
|
||
return "❌ LLM 未配置，请先在全局设置中配置提供商"
|
||
|
||
svc = LLMService(api_key, base_url, model)
|
||
data = svc.generate_copy(topic, style)
|
||
title = (data.get("title", "") or "")[:20]
|
||
content = data.get("content", "")
|
||
sd_prompt = data.get("sd_prompt", "")
|
||
tags = data.get("tags", [])
|
||
|
||
if not title:
|
||
return "❌ 文案生成失败：无标题"
|
||
_auto_log_append(f"📄 文案: {title}")
|
||
|
||
# 生成图片
|
||
if not sd_url_val or not sd_model_name:
|
||
return "❌ SD WebUI 未连接或未选择模型，请先在全局设置中连接"
|
||
|
||
sd_svc = SDService(sd_url_val)
|
||
images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name)
|
||
if not images:
|
||
return "❌ 图片生成失败：没有返回图片"
|
||
_auto_log_append(f"🎨 已生成 {len(images)} 张图片")
|
||
|
||
# 保存图片到临时目录
|
||
temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish")
|
||
os.makedirs(temp_dir, exist_ok=True)
|
||
image_paths = []
|
||
ts = int(time.time())
|
||
for idx, img in enumerate(images):
|
||
if isinstance(img, Image.Image):
|
||
path = os.path.abspath(os.path.join(temp_dir, f"auto_{ts}_{idx}.png"))
|
||
img.save(path)
|
||
image_paths.append(path)
|
||
|
||
if not image_paths:
|
||
return "❌ 图片保存失败"
|
||
|
||
# 发布到小红书
|
||
client = get_mcp_client(mcp_url)
|
||
result = client.publish_content(
|
||
title=title, content=content, images=image_paths, tags=tags
|
||
)
|
||
if "error" in result:
|
||
_auto_log_append(f"❌ 发布失败: {result['error']}")
|
||
return f"❌ 发布失败: {result['error']}"
|
||
|
||
_auto_log_append(f"🚀 发布成功: {title}")
|
||
return f"✅ 发布成功！\n📌 标题: {title}\n{result.get('text', '')}"
|
||
|
||
except Exception as e:
|
||
_auto_log_append(f"❌ 一键发布异常: {e}")
|
||
return f"❌ 发布失败: {e}"
|
||
|
||
|
||
def _scheduler_loop(comment_enabled, publish_enabled, reply_enabled, like_enabled,
|
||
comment_min, comment_max, publish_min, publish_max,
|
||
reply_min, reply_max, max_replies_per_run,
|
||
like_min, like_max, like_count_per_run,
|
||
keywords, topics, mcp_url, sd_url_val, sd_model_name,
|
||
model, persona_text):
|
||
"""后台定时调度循环"""
|
||
_auto_log_append("🤖 自动化调度器已启动")
|
||
|
||
# 首次执行的随机延迟
|
||
next_comment = time.time() + random.randint(10, 60)
|
||
next_publish = time.time() + random.randint(30, 120)
|
||
next_reply = time.time() + random.randint(15, 90)
|
||
next_like = time.time() + random.randint(5, 40)
|
||
|
||
while _auto_running.is_set():
|
||
now = time.time()
|
||
|
||
# 自动评论
|
||
if comment_enabled and now >= next_comment:
|
||
try:
|
||
_auto_log_append("--- 🔄 执行自动评论 ---")
|
||
msg = auto_comment_once(keywords, mcp_url, model, persona_text)
|
||
_auto_log_append(msg)
|
||
except Exception as e:
|
||
_auto_log_append(f"❌ 自动评论异常: {e}")
|
||
interval = random.randint(int(comment_min) * 60, int(comment_max) * 60)
|
||
next_comment = time.time() + interval
|
||
_auto_log_append(f"⏰ 下次评论: {interval // 60} 分钟后")
|
||
|
||
# 自动点赞
|
||
if like_enabled and now >= next_like:
|
||
try:
|
||
_auto_log_append("--- 🔄 执行自动点赞 ---")
|
||
msg = auto_like_once(keywords, like_count_per_run, mcp_url)
|
||
_auto_log_append(msg)
|
||
except Exception as e:
|
||
_auto_log_append(f"❌ 自动点赞异常: {e}")
|
||
interval = random.randint(int(like_min) * 60, int(like_max) * 60)
|
||
next_like = time.time() + interval
|
||
_auto_log_append(f"⏰ 下次点赞: {interval // 60} 分钟后")
|
||
|
||
# 自动发布
|
||
if publish_enabled and now >= next_publish:
|
||
try:
|
||
_auto_log_append("--- 🔄 执行自动发布 ---")
|
||
msg = auto_publish_once(topics, mcp_url, sd_url_val, sd_model_name, model)
|
||
_auto_log_append(msg)
|
||
except Exception as e:
|
||
_auto_log_append(f"❌ 自动发布异常: {e}")
|
||
interval = random.randint(int(publish_min) * 60, int(publish_max) * 60)
|
||
next_publish = time.time() + interval
|
||
_auto_log_append(f"⏰ 下次发布: {interval // 60} 分钟后")
|
||
|
||
# 自动回复评论
|
||
if reply_enabled and now >= next_reply:
|
||
try:
|
||
_auto_log_append("--- 🔄 执行自动回复评论 ---")
|
||
msg = auto_reply_once(max_replies_per_run, mcp_url, model, persona_text)
|
||
_auto_log_append(msg)
|
||
except Exception as e:
|
||
_auto_log_append(f"❌ 自动回复异常: {e}")
|
||
interval = random.randint(int(reply_min) * 60, int(reply_max) * 60)
|
||
next_reply = time.time() + interval
|
||
_auto_log_append(f"⏰ 下次回复: {interval // 60} 分钟后")
|
||
|
||
# 每5秒检查一次停止信号
|
||
for _ in range(5):
|
||
if not _auto_running.is_set():
|
||
break
|
||
time.sleep(1)
|
||
|
||
_auto_log_append("🛑 自动化调度器已停止")
|
||
|
||
|
||
def start_scheduler(comment_on, publish_on, reply_on, like_on,
|
||
c_min, c_max, p_min, p_max, r_min, r_max,
|
||
max_replies_per_run,
|
||
l_min, l_max, like_count_per_run,
|
||
keywords, topics, mcp_url, sd_url_val, sd_model_name,
|
||
model, persona_text):
|
||
"""启动定时自动化"""
|
||
global _auto_thread
|
||
if _auto_running.is_set():
|
||
return "⚠️ 调度器已在运行中，请先停止"
|
||
|
||
if not comment_on and not publish_on and not reply_on and not like_on:
|
||
return "❌ 请至少启用一项自动化功能"
|
||
|
||
# 评论/回复需要 LLM，点赞不需要
|
||
if (comment_on or reply_on):
|
||
api_key, _, _ = _get_llm_config()
|
||
if not api_key:
|
||
return "❌ LLM 未配置，请先在全局设置中配置提供商"
|
||
|
||
_auto_running.set()
|
||
_auto_thread = threading.Thread(
|
||
target=_scheduler_loop,
|
||
args=(comment_on, publish_on, reply_on, like_on,
|
||
c_min, c_max, p_min, p_max, r_min, r_max,
|
||
max_replies_per_run,
|
||
l_min, l_max, like_count_per_run,
|
||
keywords, topics, mcp_url, sd_url_val, sd_model_name,
|
||
model, persona_text),
|
||
daemon=True,
|
||
)
|
||
_auto_thread.start()
|
||
|
||
parts = []
|
||
if comment_on:
|
||
parts.append(f"评论 (每 {int(c_min)}-{int(c_max)} 分钟)")
|
||
if like_on:
|
||
parts.append(f"点赞 (每 {int(l_min)}-{int(l_max)} 分钟, {int(like_count_per_run)}个/轮)")
|
||
if publish_on:
|
||
parts.append(f"发布 (每 {int(p_min)}-{int(p_max)} 分钟)")
|
||
if reply_on:
|
||
parts.append(f"回复 (每 {int(r_min)}-{int(r_max)} 分钟, 每轮≤{int(max_replies_per_run)}条)")
|
||
|
||
_auto_log_append(f"调度器已启动: {' + '.join(parts)}")
|
||
return f"✅ 自动化已启动 🟢\n任务: {' | '.join(parts)}\n\n💡 点击「刷新日志」查看实时进度"
|
||
|
||
|
||
def stop_scheduler():
|
||
"""停止定时自动化"""
|
||
if not _auto_running.is_set():
|
||
return "⚠️ 调度器未在运行"
|
||
_auto_running.clear()
|
||
_auto_log_append("⏹️ 收到停止信号，等待当前任务完成...")
|
||
return "🛑 调度器停止中...当前任务完成后将完全停止"
|
||
|
||
|
||
def get_auto_log():
|
||
"""获取自动化运行日志"""
|
||
if not _auto_log:
|
||
return "📋 暂无日志\n\n💡 点击「一键评论」「一键发布」或启动定时后日志将在此显示"
|
||
return "\n".join(_auto_log[-80:])
|
||
|
||
|
||
def get_scheduler_status():
|
||
"""获取调度器运行状态"""
|
||
if _auto_running.is_set():
|
||
return "🟢 **调度器运行中**"
|
||
return "⚪ **调度器未运行**"
|
||
|
||
|
||
# ==================================================
|
||
# UI 构建
|
||
# ==================================================
|
||
|
||
config = cfg.all
|
||
|
||
with gr.Blocks(
|
||
title="小红书 AI 爆文工坊 V2.0",
|
||
theme=gr.themes.Soft(),
|
||
css="""
|
||
.status-ok { color: #16a34a; font-weight: bold; }
|
||
.status-err { color: #dc2626; font-weight: bold; }
|
||
footer { display: none !important; }
|
||
""",
|
||
) as app:
|
||
gr.Markdown(
|
||
"# 🍒 小红书 AI 爆文生产工坊 V2.0\n"
|
||
"> 灵感 → 文案 → 绘图 → 发布 → 运营，一站式全闭环"
|
||
)
|
||
|
||
# 全局状态
|
||
state_images = gr.State([])
|
||
state_search_result = gr.State("")
|
||
|
||
# ============ 全局设置栏 ============
|
||
with gr.Accordion("⚙️ 全局设置 (自动保存)", open=False):
|
||
gr.Markdown("#### 🤖 LLM 提供商 (支持所有 OpenAI 兼容接口)")
|
||
with gr.Row():
|
||
llm_provider = gr.Dropdown(
|
||
label="选择 LLM 提供商",
|
||
choices=cfg.get_llm_provider_names(),
|
||
value=cfg.get("active_llm", ""),
|
||
interactive=True, scale=2,
|
||
)
|
||
btn_connect_llm = gr.Button("🔗 连接 LLM", size="sm", scale=1)
|
||
with gr.Row():
|
||
llm_model = gr.Dropdown(
|
||
label="LLM 模型", value=config["model"],
|
||
allow_custom_value=True, interactive=True, scale=2,
|
||
)
|
||
llm_provider_info = gr.Markdown(
|
||
value="*选择提供商后显示详情*",
|
||
)
|
||
with gr.Accordion("➕ 添加 / 管理 LLM 提供商", open=False):
|
||
with gr.Row():
|
||
new_provider_name = gr.Textbox(
|
||
label="名称", placeholder="如: DeepSeek / GPT-4o / 通义千问",
|
||
scale=1,
|
||
)
|
||
new_provider_key = gr.Textbox(
|
||
label="API Key", type="password", scale=2,
|
||
)
|
||
new_provider_url = gr.Textbox(
|
||
label="Base URL", placeholder="https://api.openai.com/v1",
|
||
value="https://api.openai.com/v1", scale=2,
|
||
)
|
||
with gr.Row():
|
||
btn_add_provider = gr.Button("✅ 添加提供商", variant="primary", size="sm")
|
||
btn_del_provider = gr.Button("🗑️ 删除当前提供商", variant="stop", size="sm")
|
||
provider_mgmt_status = gr.Markdown("")
|
||
|
||
gr.Markdown("---")
|
||
with gr.Row():
|
||
mcp_url = gr.Textbox(
|
||
label="MCP Server URL", value=config["mcp_url"], scale=2,
|
||
)
|
||
sd_url = gr.Textbox(
|
||
label="SD WebUI URL", value=config["sd_url"], scale=2,
|
||
)
|
||
persona = gr.Textbox(
|
||
label="博主人设（评论回复用）",
|
||
value=config["persona"], scale=3,
|
||
)
|
||
with gr.Row():
|
||
btn_connect_sd = gr.Button("🎨 连接 SD", size="sm")
|
||
btn_check_mcp = gr.Button("📡 检查 MCP", size="sm")
|
||
with gr.Row():
|
||
sd_model = gr.Dropdown(
|
||
label="SD 模型", allow_custom_value=True,
|
||
interactive=True, scale=2,
|
||
)
|
||
status_bar = gr.Markdown("🔄 等待连接...")
|
||
|
||
# ============ Tab 页面 ============
|
||
with gr.Tabs():
|
||
# -------- Tab 1: 内容创作 --------
|
||
with gr.Tab("✨ 内容创作"):
|
||
with gr.Row():
|
||
# 左栏：输入
|
||
with gr.Column(scale=1):
|
||
gr.Markdown("### 💡 构思")
|
||
topic = gr.Textbox(label="笔记主题", placeholder="例如：优衣库早春穿搭")
|
||
style = gr.Dropdown(
|
||
["好物种草", "干货教程", "情绪共鸣", "生活Vlog", "测评避雷", "知识科普"],
|
||
label="风格", value="好物种草",
|
||
)
|
||
btn_gen_copy = gr.Button("✨ 第一步：生成文案", variant="primary")
|
||
|
||
gr.Markdown("---")
|
||
gr.Markdown("### 🎨 绘图参数")
|
||
with gr.Accordion("高级设置", open=False):
|
||
neg_prompt = gr.Textbox(
|
||
label="反向提示词", value=DEFAULT_NEGATIVE, lines=2,
|
||
)
|
||
steps = gr.Slider(15, 50, value=25, step=1, label="步数")
|
||
cfg_scale = gr.Slider(1, 15, value=7, step=0.5, label="CFG Scale")
|
||
btn_gen_img = gr.Button("🎨 第二步：生成图片", variant="primary")
|
||
|
||
# 中栏：文案编辑
|
||
with gr.Column(scale=1):
|
||
gr.Markdown("### 📝 文案编辑")
|
||
res_title = gr.Textbox(label="标题 (≤20字)", interactive=True)
|
||
res_content = gr.TextArea(
|
||
label="正文 (可手动修改)", lines=12, interactive=True,
|
||
)
|
||
res_prompt = gr.TextArea(
|
||
label="绘图提示词", lines=3, interactive=True,
|
||
)
|
||
res_tags = gr.Textbox(
|
||
label="话题标签 (逗号分隔)", interactive=True,
|
||
placeholder="穿搭, 春季, 好物种草",
|
||
)
|
||
|
||
# 右栏：预览 & 发布
|
||
with gr.Column(scale=1):
|
||
gr.Markdown("### 🖼️ 视觉预览")
|
||
gallery = gr.Gallery(label="AI 生成图片", columns=2, height=300)
|
||
local_images = gr.File(
|
||
label="📁 上传本地图片（可混排）",
|
||
file_count="multiple",
|
||
file_types=["image"],
|
||
)
|
||
|
||
gr.Markdown("### 🚀 发布")
|
||
schedule_time = gr.Textbox(
|
||
label="定时发布 (可选, ISO8601格式)",
|
||
placeholder="如 2026-02-08T18:00:00+08:00，留空=立即发布",
|
||
)
|
||
with gr.Row():
|
||
btn_export = gr.Button("📂 导出本地", variant="secondary")
|
||
btn_publish = gr.Button("🚀 发布到小红书", variant="primary")
|
||
publish_msg = gr.Markdown("")
|
||
|
||
# -------- Tab 2: 热点探测 --------
|
||
with gr.Tab("🔥 热点探测"):
|
||
gr.Markdown("### 搜索热门内容 → AI 分析趋势 → 一键借鉴创作")
|
||
with gr.Row():
|
||
with gr.Column(scale=1):
|
||
hot_keyword = gr.Textbox(
|
||
label="搜索关键词", placeholder="如：春季穿搭",
|
||
)
|
||
hot_sort = gr.Dropdown(
|
||
["综合", "最新", "最多点赞", "最多评论", "最多收藏"],
|
||
label="排序", value="综合",
|
||
)
|
||
btn_search = gr.Button("🔍 搜索", variant="primary")
|
||
search_status = gr.Markdown("")
|
||
|
||
with gr.Column(scale=2):
|
||
search_output = gr.TextArea(
|
||
label="搜索结果", lines=12, interactive=False,
|
||
)
|
||
|
||
with gr.Row():
|
||
btn_analyze = gr.Button("🧠 AI 分析热点趋势", variant="primary")
|
||
analysis_status = gr.Markdown("")
|
||
analysis_output = gr.Markdown(label="分析报告")
|
||
topic_from_hot = gr.Textbox(
|
||
label="选择/输入创作选题", placeholder="基于分析选一个方向",
|
||
)
|
||
|
||
with gr.Row():
|
||
hot_style = gr.Dropdown(
|
||
["好物种草", "干货教程", "情绪共鸣", "生活Vlog", "测评避雷"],
|
||
label="风格", value="好物种草",
|
||
)
|
||
btn_gen_from_hot = gr.Button("✨ 基于热点生成文案", variant="primary")
|
||
|
||
with gr.Row():
|
||
hot_title = gr.Textbox(label="生成的标题", interactive=True)
|
||
hot_content = gr.TextArea(label="生成的正文", lines=8, interactive=True)
|
||
with gr.Row():
|
||
hot_prompt = gr.TextArea(label="绘图提示词", lines=3, interactive=True)
|
||
hot_tags = gr.Textbox(label="标签", interactive=True)
|
||
hot_gen_status = gr.Markdown("")
|
||
btn_sync_to_create = gr.Button(
|
||
"📋 同步到「内容创作」Tab → 绘图 & 发布",
|
||
variant="primary",
|
||
)
|
||
|
||
# -------- Tab 3: 评论管家 --------
|
||
with gr.Tab("💬 评论管家"):
|
||
gr.Markdown("### 智能评论管理：主动评论引流 & 自动回复粉丝")
|
||
|
||
with gr.Tabs():
|
||
# ======== 子 Tab A: 主动评论他人 ========
|
||
with gr.Tab("✍️ 主动评论引流"):
|
||
gr.Markdown(
|
||
"> **流程**：搜索/浏览笔记 → 选择目标 → 加载内容 → "
|
||
"AI 分析笔记+已有评论自动生成高质量评论 → 一键发送"
|
||
)
|
||
|
||
# 笔记选择器
|
||
with gr.Row():
|
||
pro_keyword = gr.Textbox(
|
||
label="🔍 搜索关键词 (留空则获取推荐)",
|
||
placeholder="穿搭、美食、旅行…",
|
||
)
|
||
btn_pro_fetch = gr.Button("🔍 获取笔记", variant="primary")
|
||
with gr.Row():
|
||
pro_selector = gr.Dropdown(
|
||
label="📋 选择目标笔记",
|
||
choices=[], interactive=True,
|
||
)
|
||
pro_fetch_status = gr.Markdown("")
|
||
|
||
# 隐藏字段
|
||
with gr.Row():
|
||
pro_feed_id = gr.Textbox(label="笔记 ID", interactive=False)
|
||
pro_xsec_token = gr.Textbox(label="xsec_token", interactive=False)
|
||
pro_title = gr.Textbox(label="标题", interactive=False)
|
||
|
||
# 加载内容 & AI 分析
|
||
btn_pro_load = gr.Button("📖 加载笔记内容", variant="secondary")
|
||
pro_load_status = gr.Markdown("")
|
||
|
||
with gr.Row():
|
||
with gr.Column(scale=1):
|
||
pro_content = gr.TextArea(
|
||
label="📄 笔记正文摘要", lines=8, interactive=False,
|
||
)
|
||
with gr.Column(scale=1):
|
||
pro_comments = gr.TextArea(
|
||
label="💬 已有评论", lines=8, interactive=False,
|
||
)
|
||
# 隐藏: 完整文本
|
||
pro_full_text = gr.Textbox(visible=False)
|
||
|
||
gr.Markdown("---")
|
||
with gr.Row():
|
||
with gr.Column(scale=1):
|
||
btn_pro_ai = gr.Button(
|
||
"🤖 AI 智能生成评论", variant="primary", size="lg",
|
||
)
|
||
pro_ai_status = gr.Markdown("")
|
||
with gr.Column(scale=2):
|
||
pro_comment_text = gr.TextArea(
|
||
label="✏️ 评论内容 (可手动修改)", lines=3,
|
||
interactive=True,
|
||
placeholder="点击左侧按钮自动生成，也可手动编写",
|
||
)
|
||
with gr.Row():
|
||
btn_pro_send = gr.Button("📩 发送评论", variant="primary")
|
||
pro_send_status = gr.Markdown("")
|
||
|
||
# ======== 子 Tab B: 回复我的评论 ========
|
||
with gr.Tab("💌 回复粉丝评论"):
|
||
gr.Markdown(
|
||
"> **流程**：选择我的笔记 → 加载评论 → "
|
||
"粘贴要回复的评论 → AI 生成回复 → 一键发送"
|
||
)
|
||
|
||
# 笔记选择器 (自动用已保存的 userId 获取)
|
||
with gr.Row():
|
||
btn_my_fetch = gr.Button("🔍 获取我的笔记", variant="primary")
|
||
with gr.Row():
|
||
my_selector = gr.Dropdown(
|
||
label="📋 选择我的笔记",
|
||
choices=[], interactive=True,
|
||
)
|
||
my_fetch_status = gr.Markdown("")
|
||
|
||
with gr.Row():
|
||
my_feed_id = gr.Textbox(label="笔记 ID", interactive=False)
|
||
my_xsec_token = gr.Textbox(label="xsec_token", interactive=False)
|
||
my_title = gr.Textbox(label="笔记标题", interactive=False)
|
||
|
||
btn_my_load_comments = gr.Button("📥 加载评论", variant="primary")
|
||
my_comment_status = gr.Markdown("")
|
||
|
||
my_comments_display = gr.TextArea(
|
||
label="📋 粉丝评论列表", lines=12, interactive=False,
|
||
)
|
||
|
||
gr.Markdown("---")
|
||
gr.Markdown("#### 📝 回复评论")
|
||
with gr.Row():
|
||
with gr.Column(scale=1):
|
||
my_target_comment = gr.TextArea(
|
||
label="要回复的评论内容", lines=3,
|
||
placeholder="从上方评论列表中复制粘贴要回复的评论",
|
||
)
|
||
btn_my_ai_reply = gr.Button(
|
||
"🤖 AI 生成回复", variant="secondary",
|
||
)
|
||
my_reply_gen_status = gr.Markdown("")
|
||
with gr.Column(scale=1):
|
||
my_reply_content = gr.TextArea(
|
||
label="回复内容 (可修改)", lines=3,
|
||
interactive=True,
|
||
)
|
||
btn_my_send_reply = gr.Button(
|
||
"📩 发送回复", variant="primary",
|
||
)
|
||
my_reply_status = gr.Markdown("")
|
||
|
||
# -------- Tab 4: 账号登录 --------
|
||
with gr.Tab("🔐 账号登录"):
|
||
gr.Markdown(
|
||
"### 小红书账号登录\n"
|
||
"> 扫码登录后自动获取 xsec_token，配合用户 ID 即可使用所有功能"
|
||
)
|
||
with gr.Row():
|
||
with gr.Column(scale=1):
|
||
gr.Markdown(
|
||
"**操作步骤：**\n"
|
||
"1. 确保 MCP 服务已启动\n"
|
||
"2. 点击「获取登录二维码」→ 用小红书 App 扫码\n"
|
||
"3. 点击「检查登录状态」→ 自动获取并保存 xsec_token\n"
|
||
"4. 首次使用请填写你的用户 ID 并点击保存\n\n"
|
||
"⚠️ 登录后不要在其他网页端登录同一账号，否则会被踢出"
|
||
)
|
||
btn_get_qrcode = gr.Button(
|
||
"📱 获取登录二维码", variant="primary", size="lg",
|
||
)
|
||
btn_check_login = gr.Button(
|
||
"🔍 检查登录状态 (自动获取 Token)",
|
||
variant="secondary", size="lg",
|
||
)
|
||
btn_logout = gr.Button(
|
||
"🚪 退出登录 (重新扫码)",
|
||
variant="stop", size="lg",
|
||
)
|
||
login_status = gr.Markdown("🔄 等待操作...")
|
||
|
||
gr.Markdown("---")
|
||
gr.Markdown(
|
||
"#### 📌 我的账号信息\n"
|
||
"> **注意**: 小红书号 ≠ 用户 ID\n"
|
||
"> - **小红书号 (redId)**: 如 `18688457507`，是你在 App 个人页看到的\n"
|
||
"> - **用户 ID (userId)**: 如 `5a695db6e8ac2b72e8af2a53`，24位十六进制字符串\n\n"
|
||
"💡 **如何获取 userId?**\n"
|
||
"1. 用浏览器打开你的小红书主页\n"
|
||
"2. 网址格式为: `xiaohongshu.com/user/profile/xxxxxxxx`\n"
|
||
"3. `profile/` 后面的就是你的 userId"
|
||
)
|
||
login_user_id = gr.Textbox(
|
||
label="我的用户 ID (24位 userId, 非小红书号)",
|
||
value=config.get("my_user_id", ""),
|
||
placeholder="如: 5a695db6e8ac2b72e8af2a53",
|
||
)
|
||
login_xsec_token = gr.Textbox(
|
||
label="xsec_token (登录后自动获取)",
|
||
value=config.get("xsec_token", ""),
|
||
interactive=False,
|
||
)
|
||
btn_save_uid = gr.Button(
|
||
"💾 保存用户 ID", variant="secondary",
|
||
)
|
||
save_uid_status = gr.Markdown("")
|
||
|
||
with gr.Column(scale=1):
|
||
qr_image = gr.Image(
|
||
label="扫码登录", height=350, width=350,
|
||
)
|
||
|
||
# -------- Tab 5: 数据看板 --------
|
||
with gr.Tab("📊 数据看板"):
|
||
gr.Markdown(
|
||
"### 我的账号数据看板\n"
|
||
"> 用户 ID 和 xsec_token 从「账号登录」自动获取，直接点击加载即可"
|
||
)
|
||
|
||
with gr.Row():
|
||
with gr.Column(scale=1):
|
||
data_user_id = gr.Textbox(
|
||
label="我的用户 ID (自动填充)",
|
||
value=config.get("my_user_id", ""),
|
||
interactive=True,
|
||
)
|
||
data_xsec_token = gr.Textbox(
|
||
label="xsec_token (自动填充)",
|
||
value=config.get("xsec_token", ""),
|
||
interactive=True,
|
||
)
|
||
btn_refresh_token = gr.Button(
|
||
"🔄 刷新 Token", variant="secondary",
|
||
)
|
||
btn_load_my_data = gr.Button(
|
||
"📊 加载我的数据", variant="primary", size="lg",
|
||
)
|
||
data_status = gr.Markdown("")
|
||
|
||
with gr.Column(scale=2):
|
||
profile_card = gr.Markdown(
|
||
value="*等待加载...*",
|
||
label="账号概览",
|
||
)
|
||
|
||
gr.Markdown("---")
|
||
gr.Markdown("### 📈 数据可视化")
|
||
with gr.Row():
|
||
with gr.Column(scale=1):
|
||
chart_interact = gr.Plot(label="📊 核心指标")
|
||
with gr.Column(scale=2):
|
||
chart_notes = gr.Plot(label="❤ 笔记点赞排行")
|
||
|
||
gr.Markdown("---")
|
||
notes_detail = gr.Markdown(
|
||
value="*加载数据后显示笔记明细表格*",
|
||
label="笔记数据明细",
|
||
)
|
||
|
||
# -------- Tab 6: 自动运营 --------
|
||
with gr.Tab("🤖 自动运营"):
|
||
gr.Markdown(
|
||
"### 🤖 无人值守自动化运营\n"
|
||
"> 一键评论引流 + 一键回复粉丝 + 一键内容发布 + 随机定时全自动\n\n"
|
||
"⚠️ **注意**: 请确保已连接 LLM、SD WebUI 和 MCP 服务"
|
||
)
|
||
|
||
with gr.Row():
|
||
# 左栏: 一键操作
|
||
with gr.Column(scale=1):
|
||
gr.Markdown("#### 💬 一键智能评论")
|
||
gr.Markdown(
|
||
"> 自动搜索高赞笔记 → AI 分析内容 → 生成评论 → 发送\n"
|
||
"每次随机选关键词搜索，从结果中随机选笔记"
|
||
)
|
||
auto_comment_keywords = gr.Textbox(
|
||
label="评论关键词池 (逗号分隔)",
|
||
value="穿搭, 美食, 护肤, 好物推荐, 旅行, 生活日常",
|
||
placeholder="关键词1, 关键词2, ...",
|
||
)
|
||
btn_auto_comment = gr.Button(
|
||
"💬 一键评论 (单次)", variant="primary", size="lg",
|
||
)
|
||
auto_comment_result = gr.Markdown("")
|
||
|
||
gr.Markdown("---")
|
||
gr.Markdown("#### <20> 一键自动点赞")
|
||
gr.Markdown(
|
||
"> 搜索笔记 → 随机选择多篇 → 依次点赞\n"
|
||
"提升账号活跃度，无需 LLM"
|
||
)
|
||
auto_like_count = gr.Number(
|
||
label="单次点赞数量", value=5, minimum=1, maximum=20,
|
||
)
|
||
btn_auto_like = gr.Button(
|
||
"👍 一键点赞 (单次)", variant="primary", size="lg",
|
||
)
|
||
auto_like_result = gr.Markdown("")
|
||
|
||
gr.Markdown("---")
|
||
gr.Markdown("#### <20>💌 一键自动回复")
|
||
gr.Markdown(
|
||
"> 扫描我的所有笔记 → 找到粉丝评论 → AI 生成回复 → 逐条发送\n"
|
||
"自动跳过自己的评论，模拟真人间隔回复"
|
||
)
|
||
auto_reply_max = gr.Number(
|
||
label="单次最多回复条数", value=5, minimum=1, maximum=20,
|
||
)
|
||
btn_auto_reply = gr.Button(
|
||
"💌 一键回复 (单次)", variant="primary", size="lg",
|
||
)
|
||
auto_reply_result = gr.Markdown("")
|
||
|
||
gr.Markdown("---")
|
||
gr.Markdown("#### 🚀 一键智能发布")
|
||
gr.Markdown(
|
||
"> 随机选主题+风格 → AI 生成文案 → SD 生成图片 → 自动发布"
|
||
)
|
||
auto_publish_topics = gr.Textbox(
|
||
label="主题池 (逗号分隔)",
|
||
value="春季穿搭, 通勤穿搭, 显瘦穿搭, 平价好物, 护肤心得, 好物分享",
|
||
placeholder="主题1, 主题2, ...",
|
||
)
|
||
btn_auto_publish = gr.Button(
|
||
"🚀 一键发布 (单次)", variant="primary", size="lg",
|
||
)
|
||
auto_publish_result = gr.Markdown("")
|
||
|
||
# 右栏: 定时自动化
|
||
with gr.Column(scale=1):
|
||
gr.Markdown("#### ⏰ 随机定时自动化")
|
||
gr.Markdown(
|
||
"> 设置时间间隔后启动，系统将在随机时间自动执行\n"
|
||
"> 模拟真人操作节奏，降低被检测风险"
|
||
)
|
||
sched_status = gr.Markdown("⚪ **调度器未运行**")
|
||
|
||
with gr.Group():
|
||
sched_comment_on = gr.Checkbox(
|
||
label="✅ 启用自动评论", value=True,
|
||
)
|
||
with gr.Row():
|
||
sched_c_min = gr.Number(
|
||
label="评论最小间隔(分钟)", value=15, minimum=5,
|
||
)
|
||
sched_c_max = gr.Number(
|
||
label="评论最大间隔(分钟)", value=45, minimum=10,
|
||
)
|
||
|
||
with gr.Group():
|
||
sched_like_on = gr.Checkbox(
|
||
label="✅ 启用自动点赞", value=True,
|
||
)
|
||
with gr.Row():
|
||
sched_l_min = gr.Number(
|
||
label="点赞最小间隔(分钟)", value=10, minimum=3,
|
||
)
|
||
sched_l_max = gr.Number(
|
||
label="点赞最大间隔(分钟)", value=30, minimum=5,
|
||
)
|
||
sched_like_count = gr.Number(
|
||
label="每轮点赞数量", value=5, minimum=1, maximum=15,
|
||
)
|
||
|
||
with gr.Group():
|
||
sched_reply_on = gr.Checkbox(
|
||
label="✅ 启用自动回复评论", value=True,
|
||
)
|
||
with gr.Row():
|
||
sched_r_min = gr.Number(
|
||
label="回复最小间隔(分钟)", value=20, minimum=5,
|
||
)
|
||
sched_r_max = gr.Number(
|
||
label="回复最大间隔(分钟)", value=60, minimum=10,
|
||
)
|
||
sched_reply_max = gr.Number(
|
||
label="每轮最多回复条数", value=3, minimum=1, maximum=10,
|
||
)
|
||
|
||
with gr.Group():
|
||
sched_publish_on = gr.Checkbox(
|
||
label="✅ 启用自动发布", value=True,
|
||
)
|
||
with gr.Row():
|
||
sched_p_min = gr.Number(
|
||
label="发布最小间隔(分钟)", value=60, minimum=30,
|
||
)
|
||
sched_p_max = gr.Number(
|
||
label="发布最大间隔(分钟)", value=180, minimum=60,
|
||
)
|
||
|
||
with gr.Row():
|
||
btn_start_sched = gr.Button(
|
||
"▶️ 启动定时", variant="primary", size="lg",
|
||
)
|
||
btn_stop_sched = gr.Button(
|
||
"⏹️ 停止定时", variant="stop", size="lg",
|
||
)
|
||
sched_result = gr.Markdown("")
|
||
|
||
gr.Markdown("---")
|
||
gr.Markdown("#### 📋 运行日志")
|
||
with gr.Row():
|
||
btn_refresh_log = gr.Button("🔄 刷新日志", size="sm")
|
||
btn_clear_log = gr.Button("🗑️ 清空日志", size="sm")
|
||
auto_log_display = gr.TextArea(
|
||
label="自动化运行日志",
|
||
value="📋 暂无日志\n\n💡 执行操作后日志将在此显示",
|
||
lines=15,
|
||
interactive=False,
|
||
)
|
||
|
||
# ==================================================
|
||
# 事件绑定
|
||
# ==================================================
|
||
|
||
# ---- 全局设置: LLM 提供商管理 ----
|
||
btn_connect_llm.click(
|
||
fn=connect_llm, inputs=[llm_provider],
|
||
outputs=[llm_model, status_bar],
|
||
)
|
||
llm_provider.change(
|
||
fn=on_provider_selected,
|
||
inputs=[llm_provider],
|
||
outputs=[llm_provider_info],
|
||
)
|
||
btn_add_provider.click(
|
||
fn=add_llm_provider,
|
||
inputs=[new_provider_name, new_provider_key, new_provider_url],
|
||
outputs=[llm_provider, provider_mgmt_status],
|
||
)
|
||
btn_del_provider.click(
|
||
fn=remove_llm_provider,
|
||
inputs=[llm_provider],
|
||
outputs=[llm_provider, provider_mgmt_status],
|
||
)
|
||
btn_connect_sd.click(
|
||
fn=connect_sd, inputs=[sd_url],
|
||
outputs=[sd_model, status_bar],
|
||
)
|
||
btn_check_mcp.click(
|
||
fn=check_mcp_status, inputs=[mcp_url],
|
||
outputs=[status_bar],
|
||
)
|
||
|
||
# ---- Tab 1: 内容创作 ----
|
||
btn_gen_copy.click(
|
||
fn=generate_copy,
|
||
inputs=[llm_model, topic, style],
|
||
outputs=[res_title, res_content, res_prompt, res_tags, status_bar],
|
||
)
|
||
|
||
btn_gen_img.click(
|
||
fn=generate_images,
|
||
inputs=[sd_url, res_prompt, neg_prompt, sd_model, steps, cfg_scale],
|
||
outputs=[gallery, state_images, status_bar],
|
||
)
|
||
|
||
btn_export.click(
|
||
fn=one_click_export,
|
||
inputs=[res_title, res_content, state_images],
|
||
outputs=[publish_msg],
|
||
)
|
||
|
||
btn_publish.click(
|
||
fn=publish_to_xhs,
|
||
inputs=[res_title, res_content, res_tags, state_images,
|
||
local_images, mcp_url, schedule_time],
|
||
outputs=[publish_msg],
|
||
)
|
||
|
||
# ---- Tab 2: 热点探测 ----
|
||
btn_search.click(
|
||
fn=search_hotspots,
|
||
inputs=[hot_keyword, hot_sort, mcp_url],
|
||
outputs=[search_status, search_output],
|
||
)
|
||
# 搜索结果同步到 state
|
||
search_output.change(
|
||
fn=lambda x: x, inputs=[search_output], outputs=[state_search_result],
|
||
)
|
||
|
||
btn_analyze.click(
|
||
fn=analyze_and_suggest,
|
||
inputs=[llm_model, hot_keyword, search_output],
|
||
outputs=[analysis_status, analysis_output, topic_from_hot],
|
||
)
|
||
|
||
btn_gen_from_hot.click(
|
||
fn=generate_from_hotspot,
|
||
inputs=[llm_model, topic_from_hot, hot_style, search_output],
|
||
outputs=[hot_title, hot_content, hot_prompt, hot_tags, hot_gen_status],
|
||
)
|
||
|
||
# 同步热点文案到内容创作 Tab
|
||
btn_sync_to_create.click(
|
||
fn=lambda t, c, p, tg: (t, c, p, tg, "✅ 已同步到「内容创作」，可切换 Tab 继续绘图和发布"),
|
||
inputs=[hot_title, hot_content, hot_prompt, hot_tags],
|
||
outputs=[res_title, res_content, res_prompt, res_tags, status_bar],
|
||
)
|
||
|
||
# ---- Tab 3: 评论管家 ----
|
||
|
||
# == 子 Tab A: 主动评论引流 ==
|
||
btn_pro_fetch.click(
|
||
fn=fetch_proactive_notes,
|
||
inputs=[pro_keyword, mcp_url],
|
||
outputs=[pro_selector, pro_fetch_status],
|
||
)
|
||
pro_selector.change(
|
||
fn=on_proactive_note_selected,
|
||
inputs=[pro_selector],
|
||
outputs=[pro_feed_id, pro_xsec_token, pro_title],
|
||
)
|
||
btn_pro_load.click(
|
||
fn=load_note_for_comment,
|
||
inputs=[pro_feed_id, pro_xsec_token, mcp_url],
|
||
outputs=[pro_load_status, pro_content, pro_comments, pro_full_text],
|
||
)
|
||
btn_pro_ai.click(
|
||
fn=ai_generate_comment,
|
||
inputs=[llm_model, persona,
|
||
pro_title, pro_content, pro_comments],
|
||
outputs=[pro_comment_text, pro_ai_status],
|
||
)
|
||
btn_pro_send.click(
|
||
fn=send_comment,
|
||
inputs=[pro_feed_id, pro_xsec_token, pro_comment_text, mcp_url],
|
||
outputs=[pro_send_status],
|
||
)
|
||
|
||
# == 子 Tab B: 回复粉丝评论 ==
|
||
btn_my_fetch.click(
|
||
fn=fetch_my_notes,
|
||
inputs=[mcp_url],
|
||
outputs=[my_selector, my_fetch_status],
|
||
)
|
||
my_selector.change(
|
||
fn=on_my_note_selected,
|
||
inputs=[my_selector],
|
||
outputs=[my_feed_id, my_xsec_token, my_title],
|
||
)
|
||
btn_my_load_comments.click(
|
||
fn=fetch_my_note_comments,
|
||
inputs=[my_feed_id, my_xsec_token, mcp_url],
|
||
outputs=[my_comment_status, my_comments_display],
|
||
)
|
||
btn_my_ai_reply.click(
|
||
fn=ai_reply_comment,
|
||
inputs=[llm_model, persona,
|
||
my_title, my_target_comment],
|
||
outputs=[my_reply_content, my_reply_gen_status],
|
||
)
|
||
btn_my_send_reply.click(
|
||
fn=send_reply,
|
||
inputs=[my_feed_id, my_xsec_token, my_reply_content, mcp_url],
|
||
outputs=[my_reply_status],
|
||
)
|
||
|
||
# ---- Tab 4: 账号登录 ----
|
||
btn_get_qrcode.click(
|
||
fn=get_login_qrcode,
|
||
inputs=[mcp_url],
|
||
outputs=[qr_image, login_status],
|
||
)
|
||
btn_check_login.click(
|
||
fn=check_login,
|
||
inputs=[mcp_url],
|
||
outputs=[login_status, login_user_id, login_xsec_token],
|
||
)
|
||
btn_logout.click(
|
||
fn=logout_xhs,
|
||
inputs=[mcp_url],
|
||
outputs=[login_status],
|
||
)
|
||
btn_save_uid.click(
|
||
fn=save_my_user_id,
|
||
inputs=[login_user_id],
|
||
outputs=[save_uid_status],
|
||
)
|
||
|
||
# ---- Tab 5: 数据看板 ----
|
||
def refresh_xsec_token(mcp_url):
|
||
token = _auto_fetch_xsec_token(mcp_url)
|
||
if token:
|
||
cfg.set("xsec_token", token)
|
||
return gr.update(value=token), "✅ Token 已刷新"
|
||
return gr.update(value=cfg.get("xsec_token", "")), "❌ 刷新失败，请确认已登录"
|
||
|
||
btn_refresh_token.click(
|
||
fn=refresh_xsec_token,
|
||
inputs=[mcp_url],
|
||
outputs=[data_xsec_token, data_status],
|
||
)
|
||
btn_load_my_data.click(
|
||
fn=fetch_my_profile,
|
||
inputs=[data_user_id, data_xsec_token, mcp_url],
|
||
outputs=[data_status, profile_card, chart_interact, chart_notes, notes_detail],
|
||
)
|
||
|
||
# ---- Tab 6: 自动运营 ----
|
||
btn_auto_comment.click(
|
||
fn=_auto_comment_with_log,
|
||
inputs=[auto_comment_keywords, mcp_url, llm_model, persona],
|
||
outputs=[auto_comment_result, auto_log_display],
|
||
)
|
||
btn_auto_like.click(
|
||
fn=_auto_like_with_log,
|
||
inputs=[auto_comment_keywords, auto_like_count, mcp_url],
|
||
outputs=[auto_like_result, auto_log_display],
|
||
)
|
||
btn_auto_reply.click(
|
||
fn=_auto_reply_with_log,
|
||
inputs=[auto_reply_max, mcp_url, llm_model, persona],
|
||
outputs=[auto_reply_result, auto_log_display],
|
||
)
|
||
btn_auto_publish.click(
|
||
fn=_auto_publish_with_log,
|
||
inputs=[auto_publish_topics, mcp_url, sd_url, sd_model, llm_model],
|
||
outputs=[auto_publish_result, auto_log_display],
|
||
)
|
||
btn_start_sched.click(
|
||
fn=start_scheduler,
|
||
inputs=[sched_comment_on, sched_publish_on, sched_reply_on, sched_like_on,
|
||
sched_c_min, sched_c_max, sched_p_min, sched_p_max,
|
||
sched_r_min, sched_r_max, sched_reply_max,
|
||
sched_l_min, sched_l_max, sched_like_count,
|
||
auto_comment_keywords, auto_publish_topics,
|
||
mcp_url, sd_url, sd_model, llm_model, persona],
|
||
outputs=[sched_result],
|
||
)
|
||
btn_stop_sched.click(
|
||
fn=stop_scheduler,
|
||
inputs=[],
|
||
outputs=[sched_result],
|
||
)
|
||
btn_refresh_log.click(
|
||
fn=lambda: (get_auto_log(), get_scheduler_status()),
|
||
inputs=[],
|
||
outputs=[auto_log_display, sched_status],
|
||
)
|
||
btn_clear_log.click(
|
||
fn=lambda: (_auto_log.clear() or "📋 日志已清空"),
|
||
inputs=[],
|
||
outputs=[auto_log_display],
|
||
)
|
||
|
||
# ---- 启动时自动刷新 SD ----
|
||
app.load(fn=connect_sd, inputs=[sd_url], outputs=[sd_model, status_bar])
|
||
|
||
|
||
# ==================================================
|
||
if __name__ == "__main__":
|
||
logger.info("🍒 小红书 AI 爆文工坊 V2.0 启动中...")
|
||
app.launch(inbrowser=True, share=False)
|