zhoujie
88faca150d
- 新增项目配置文件(.gitignore, config.json)和核心文档(Todo.md, mcp.md) - 实现配置管理模块(config_manager.py)，支持单例模式和自动保存 - 实现LLM服务模块(llm_service.py)，包含文案生成、热点分析、评论回复等Prompt模板 - 实现SD服务模块(sd_service.py)，封装Stable Diffusion WebUI API调用 - 实现MCP客户端模块(mcp_client.py)，封装小红书MCP服务HTTP调用 - 实现主程序(main.py)，构建Gradio界面，包含内容创作、热点探测、评论管家、账号登录、数据看板五大功能模块 - 保留V1版本备份(main_v1_backup.py)供参考 - 添加项目依赖文件(requirements.txt)
1132 lines
42 KiB
Python
1132 lines
42 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 time
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import logging
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import platform
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import subprocess
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from PIL import Image
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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
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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 = MCPClient(cfg.get("mcp_url", "http://localhost:18060/mcp"))
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# ==================================================
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# Tab 1: 内容创作
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# ==================================================
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def connect_llm(api_key, base_url):
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"""连接 LLM 并获取模型列表"""
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if not api_key or not base_url:
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return gr.update(choices=[]), "⚠️ 请先填写 API Key 和 Base URL"
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try:
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svc = LLMService(api_key, base_url)
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models = svc.get_models()
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cfg.update({"api_key": api_key, "base_url": base_url})
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return (
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gr.update(choices=models, value=models[0] if models else None),
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f"✅ 已连接，加载 {len(models)} 个模型",
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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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return gr.update(), f"❌ 连接失败: {e}"
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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 = MCPClient(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 = MCPClient(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 check_login(mcp_url):
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"""检查小红书登录状态"""
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try:
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client = MCPClient(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']}"
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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}"
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return f"🟢 {text}"
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except Exception as e:
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return f"❌ 检查登录状态失败: {e}"
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def generate_copy(api_key, base_url, model, topic, style):
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"""生成文案"""
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if not api_key:
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return "", "", "", "", "❌ 缺少 API Key"
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try:
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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 (
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data.get("title", ""),
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data.get("content", ""),
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data.get("sd_prompt", ""),
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", ".join(tags) if tags else "",
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"✅ 文案生成完毕",
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)
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except Exception as e:
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logger.error("文案生成失败: %s", e)
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return "", "", "", "", f"❌ 生成失败: {e}"
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def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale):
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"""生成图片"""
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if not model:
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return None, [], "❌ 未选择 SD 模型"
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try:
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svc = SDService(sd_url)
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images = svc.txt2img(
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prompt=prompt,
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negative_prompt=neg_prompt,
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model=model,
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steps=int(steps),
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cfg_scale=float(cfg_scale),
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)
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return images, images, f"✅ 生成 {len(images)} 张图片"
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except Exception as e:
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logger.error("图片生成失败: %s", e)
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return None, [], f"❌ 绘图失败: {e}"
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def one_click_export(title, content, images):
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"""导出文案和图片到本地"""
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if not title:
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return "❌ 无法导出：没有标题"
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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)
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with open(os.path.join(folder_path, "文案.txt"), "w", encoding="utf-8") as f:
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f.write(f"{title}\n\n{content}")
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saved_paths = []
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if images:
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for idx, img in enumerate(images):
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path = os.path.join(folder_path, f"图{idx+1}.png")
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if isinstance(img, Image.Image):
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img.save(path)
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saved_paths.append(os.path.abspath(path))
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# 尝试打开文件夹
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try:
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abs_path = os.path.abspath(folder_path)
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if platform.system() == "Windows":
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os.startfile(abs_path)
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elif platform.system() == "Darwin":
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subprocess.call(["open", abs_path])
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else:
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subprocess.call(["xdg-open", abs_path])
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except Exception:
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pass
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return f"✅ 已导出至: {folder_path} ({len(saved_paths)} 张图片)"
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def publish_to_xhs(title, content, tags_str, images, local_images, mcp_url, schedule_time):
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"""通过 MCP 发布到小红书"""
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if not title:
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return "❌ 缺少标题"
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client = MCPClient(mcp_url)
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# 收集图片路径
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image_paths = []
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# 先保存 AI 生成的图片到临时目录
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if images:
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temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish")
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os.makedirs(temp_dir, exist_ok=True)
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for idx, img in enumerate(images):
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if isinstance(img, Image.Image):
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path = os.path.abspath(os.path.join(temp_dir, f"ai_{idx}.png"))
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img.save(path)
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image_paths.append(path)
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# 添加本地上传的图片
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if local_images:
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for img_file in local_images:
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# Gradio File 组件返回的是 NamedString 或 tempfile path
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img_path = img_file.name if hasattr(img_file, 'name') else str(img_file)
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if os.path.exists(img_path):
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image_paths.append(os.path.abspath(img_path))
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if not image_paths:
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return "❌ 至少需要 1 张图片才能发布"
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# 解析标签
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tags = [t.strip().lstrip("#") for t in tags_str.split(",") if t.strip()] if tags_str else None
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# 定时发布
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schedule = schedule_time if schedule_time and schedule_time.strip() else None
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try:
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result = client.publish_content(
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title=title,
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content=content,
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images=image_paths,
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tags=tags,
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schedule_at=schedule,
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)
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if "error" in result:
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return f"❌ 发布失败: {result['error']}"
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return f"✅ 发布成功！\n{result.get('text', '')}"
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except Exception as e:
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logger.error("发布失败: %s", e)
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return f"❌ 发布异常: {e}"
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# ==================================================
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# Tab 2: 热点探测
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# ==================================================
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def search_hotspots(keyword, sort_by, mcp_url):
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"""搜索小红书热门内容"""
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if not keyword:
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return "❌ 请输入搜索关键词", ""
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try:
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client = MCPClient(mcp_url)
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result = client.search_feeds(keyword, sort_by=sort_by)
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if "error" in result:
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return f"❌ 搜索失败: {result['error']}", ""
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text = result.get("text", "无结果")
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return "✅ 搜索完成", text
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except Exception as e:
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logger.error("热点搜索失败: %s", e)
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return f"❌ 搜索失败: {e}", ""
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def analyze_and_suggest(api_key, base_url, model, keyword, search_result):
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"""AI 分析热点并给出建议"""
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if not search_result:
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return "❌ 请先搜索", "", ""
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try:
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svc = LLMService(api_key, base_url, model)
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analysis = svc.analyze_hotspots(search_result)
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topics = "\n".join(f"• {t}" for t in analysis.get("hot_topics", []))
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patterns = "\n".join(f"• {p}" for p in analysis.get("title_patterns", []))
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suggestions = "\n".join(
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f"**{s['topic']}** - {s['reason']}"
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for s in analysis.get("suggestions", [])
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)
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structure = analysis.get("content_structure", "")
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summary = (
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f"## 🔥 热门选题\n{topics}\n\n"
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f"## 📝 标题套路\n{patterns}\n\n"
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f"## 📐 内容结构\n{structure}\n\n"
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f"## 💡 推荐选题\n{suggestions}"
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)
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return "✅ 分析完成", summary, keyword
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except Exception as e:
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||
logger.error("热点分析失败: %s", e)
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||
return f"❌ 分析失败: {e}", "", ""
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||
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||
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def generate_from_hotspot(api_key, base_url, model, topic_from_hotspot, style, search_result):
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||
"""基于热点分析生成文案"""
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if not topic_from_hotspot:
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return "", "", "", "", "❌ 请先选择或输入选题"
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try:
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svc = LLMService(api_key, base_url, model)
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data = svc.generate_copy_with_reference(
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topic=topic_from_hotspot,
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style=style,
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reference_notes=search_result[:2000], # 截断防止超长
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)
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tags = data.get("tags", [])
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return (
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data.get("title", ""),
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data.get("content", ""),
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||
data.get("sd_prompt", ""),
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||
", ".join(tags),
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"✅ 基于热点的文案已生成",
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||
)
|
||
except Exception as e:
|
||
return "", "", "", "", f"❌ 生成失败: {e}"
|
||
|
||
|
||
# ==================================================
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# Tab 3: 评论管家
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||
# ==================================================
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||
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# ---- 共用: 笔记列表缓存 ----
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||
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# 主动评论缓存
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||
_cached_proactive_entries: list[dict] = []
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# 我的笔记评论缓存
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_cached_my_note_entries: list[dict] = []
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||
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||
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||
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
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||
try:
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||
client = MCPClient(mcp_url)
|
||
if keyword and keyword.strip():
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entries = client.search_feeds_parsed(keyword.strip())
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src = f"搜索「{keyword.strip()}」"
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||
else:
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||
entries = client.list_feeds_parsed()
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src = "首页推荐"
|
||
|
||
if cache_name == "proactive":
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_cached_proactive_entries = entries
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||
else:
|
||
_cached_my_note_entries = entries
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||
|
||
if not entries:
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||
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']}"
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||
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:
|
||
idx = int(selected.split("]")[0].replace("[", "")) - 1
|
||
e = cache[idx]
|
||
return e["feed_id"], e["xsec_token"], e.get("title", "")
|
||
except (ValueError, IndexError):
|
||
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 = MCPClient(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(api_key, base_url, model, persona,
|
||
post_title, post_content, existing_comments):
|
||
"""AI 生成主动评论"""
|
||
if not api_key or not base_url:
|
||
return "⚠️ 请先配置 API Key", "❌ 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 = MCPClient(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(keyword, mcp_url):
|
||
return _fetch_and_cache(keyword, mcp_url, "my_notes")
|
||
|
||
|
||
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 = MCPClient(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(api_key, base_url, model, persona, post_title, comment_text):
|
||
"""AI 生成评论回复"""
|
||
if not api_key or not base_url:
|
||
return "⚠️ 请先在全局设置中填写 API Key 和 Base URL", "❌ 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 = MCPClient(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
|
||
# ==================================================
|
||
|
||
|
||
# 全局缓存: 最近获取的用户列表
|
||
_cached_user_entries: list[dict] = []
|
||
|
||
|
||
def fetch_user_list_from_feeds(mcp_url):
|
||
"""从推荐列表中提取用户列表, 供数据看板使用"""
|
||
global _cached_user_entries
|
||
try:
|
||
client = MCPClient(mcp_url)
|
||
entries = client.list_feeds_parsed()
|
||
|
||
# 去重: 按 user_id
|
||
seen = set()
|
||
users = []
|
||
for e in entries:
|
||
uid = e.get("user_id", "")
|
||
if uid and uid not in seen:
|
||
seen.add(uid)
|
||
users.append({
|
||
"user_id": uid,
|
||
"xsec_token": e.get("xsec_token", ""),
|
||
"nickname": e.get("author", "未知"),
|
||
})
|
||
|
||
_cached_user_entries = users
|
||
|
||
if not users:
|
||
return gr.update(choices=[], value=None), "⚠️ 未找到用户信息"
|
||
|
||
choices = [
|
||
f"@{u['nickname']} ({u['user_id'][:8]}...)"
|
||
for u in users
|
||
]
|
||
return (
|
||
gr.update(choices=choices, value=choices[0]),
|
||
f"✅ 发现 {len(users)} 位用户，请在下拉框中选择",
|
||
)
|
||
except Exception as e:
|
||
_cached_user_entries = []
|
||
return gr.update(choices=[], value=None), f"❌ {e}"
|
||
|
||
|
||
def on_user_selected(selected_user):
|
||
"""用户下拉框选择回调, 自动填充 user_id 和 xsec_token"""
|
||
global _cached_user_entries
|
||
if not selected_user or not _cached_user_entries:
|
||
return gr.update(), gr.update()
|
||
|
||
# 匹配 "(user_id_prefix...)"
|
||
for u in _cached_user_entries:
|
||
if u["user_id"][:8] in selected_user:
|
||
return u["user_id"], u["xsec_token"]
|
||
|
||
return gr.update(), gr.update()
|
||
|
||
|
||
def fetch_user_data(user_id, xsec_token, mcp_url):
|
||
"""获取用户主页数据"""
|
||
if not user_id or not xsec_token:
|
||
return "❌ 请填写用户 ID 和 xsec_token", ""
|
||
try:
|
||
client = MCPClient(mcp_url)
|
||
result = client.get_user_profile(user_id, xsec_token)
|
||
if "error" in result:
|
||
return f"❌ 获取失败: {result['error']}", ""
|
||
return "✅ 数据加载完成", result.get("text", "无数据")
|
||
except Exception as e:
|
||
return f"❌ 获取数据失败: {e}", ""
|
||
|
||
|
||
def fetch_homepage_feeds(mcp_url):
|
||
"""获取首页推荐"""
|
||
try:
|
||
client = MCPClient(mcp_url)
|
||
result = client.list_feeds()
|
||
if "error" in result:
|
||
return f"❌ {result['error']}", ""
|
||
return "✅ 推荐列表已加载", result.get("text", "无数据")
|
||
except Exception as e:
|
||
return f"❌ {e}", ""
|
||
|
||
|
||
# ==================================================
|
||
# 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):
|
||
with gr.Row():
|
||
api_key = gr.Textbox(
|
||
label="LLM API Key", value=config["api_key"],
|
||
type="password", scale=2,
|
||
)
|
||
base_url = gr.Textbox(
|
||
label="LLM Base URL", value=config["base_url"], scale=2,
|
||
)
|
||
mcp_url = gr.Textbox(
|
||
label="MCP Server URL", value=config["mcp_url"], scale=2,
|
||
)
|
||
with gr.Row():
|
||
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_llm = gr.Button("🔗 连接 LLM", size="sm")
|
||
btn_connect_sd = gr.Button("🎨 连接 SD", size="sm")
|
||
btn_check_mcp = gr.Button("📡 检查 MCP", size="sm")
|
||
with gr.Row():
|
||
llm_model = gr.Dropdown(
|
||
label="LLM 模型", value=config["model"],
|
||
allow_custom_value=True, interactive=True, scale=2,
|
||
)
|
||
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
|
||
with gr.Row():
|
||
my_keyword = gr.Textbox(
|
||
label="🔍 搜索我的笔记关键词 (留空获取推荐)",
|
||
placeholder="我发布过的笔记关键词…",
|
||
)
|
||
btn_my_fetch = gr.Button("🔍 获取笔记", 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"
|
||
"> 点击获取二维码 → 用小红书 App 扫码 → 确认登录 → 检查状态"
|
||
)
|
||
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」\n"
|
||
"3. 用小红书 App 扫码并确认\n"
|
||
"4. 点击「检查登录状态」验证\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", variant="secondary", size="lg",
|
||
)
|
||
login_status = gr.Markdown("🔄 等待操作...")
|
||
|
||
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("### 账号数据概览")
|
||
|
||
# ---- 用户选择器 ----
|
||
gr.Markdown("#### 🔍 快速选择用户 (从推荐列表提取)")
|
||
with gr.Row():
|
||
btn_fetch_users = gr.Button(
|
||
"👥 从推荐获取用户", variant="primary",
|
||
)
|
||
user_selector = gr.Dropdown(
|
||
label="选择用户 (自动填充下方 ID)",
|
||
choices=[], interactive=True,
|
||
)
|
||
user_fetch_status = gr.Markdown("")
|
||
|
||
gr.Markdown("---")
|
||
with gr.Row():
|
||
with gr.Column(scale=1):
|
||
data_user_id = gr.Textbox(label="用户 ID")
|
||
data_xsec_token = gr.Textbox(label="xsec_token")
|
||
btn_load_profile = gr.Button("📊 加载用户数据", variant="primary")
|
||
data_status = gr.Markdown("")
|
||
|
||
gr.Markdown("---")
|
||
btn_load_feeds = gr.Button("🏠 查看首页推荐", variant="secondary")
|
||
feeds_status = gr.Markdown("")
|
||
|
||
with gr.Column(scale=2):
|
||
profile_display = gr.TextArea(
|
||
label="用户信息 & 笔记数据", lines=15,
|
||
interactive=False,
|
||
)
|
||
feeds_display = gr.TextArea(
|
||
label="首页推荐", lines=10, interactive=False,
|
||
)
|
||
|
||
# ==================================================
|
||
# 事件绑定
|
||
# ==================================================
|
||
|
||
# ---- 全局设置 ----
|
||
btn_connect_llm.click(
|
||
fn=connect_llm, inputs=[api_key, base_url],
|
||
outputs=[llm_model, status_bar],
|
||
)
|
||
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=[api_key, base_url, 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=[api_key, base_url, llm_model, hot_keyword, search_output],
|
||
outputs=[analysis_status, analysis_output, topic_from_hot],
|
||
)
|
||
|
||
btn_gen_from_hot.click(
|
||
fn=generate_from_hotspot,
|
||
inputs=[api_key, base_url, 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=[api_key, base_url, 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=[my_keyword, 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=[api_key, base_url, 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],
|
||
)
|
||
|
||
# ---- Tab 5: 数据看板 ----
|
||
# 从推荐获取用户列表
|
||
btn_fetch_users.click(
|
||
fn=fetch_user_list_from_feeds,
|
||
inputs=[mcp_url],
|
||
outputs=[user_selector, user_fetch_status],
|
||
)
|
||
# 选择用户 -> 自动填充 user_id / xsec_token
|
||
user_selector.change(
|
||
fn=on_user_selected,
|
||
inputs=[user_selector],
|
||
outputs=[data_user_id, data_xsec_token],
|
||
)
|
||
|
||
btn_load_profile.click(
|
||
fn=fetch_user_data,
|
||
inputs=[data_user_id, data_xsec_token, mcp_url],
|
||
outputs=[data_status, profile_display],
|
||
)
|
||
|
||
btn_load_feeds.click(
|
||
fn=fetch_homepage_feeds,
|
||
inputs=[mcp_url],
|
||
outputs=[feeds_status, feeds_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)
|