"""
小红书 AI 爆文生产工坊 V2.0
全自动工作台：灵感 -> 文案 -> 绘图 -> 发布 -> 运营
"""
import gradio as gr
import os
import re
import time
import logging
import platform
import subprocess
from PIL import Image

from config_manager import ConfigManager, OUTPUT_DIR
from llm_service import LLMService
from sd_service import SDService, DEFAULT_NEGATIVE
from mcp_client import MCPClient

# ================= 日志配置 =================

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
    handlers=[
        logging.StreamHandler(),
        logging.FileHandler("autobot.log", encoding="utf-8"),
    ],
)
logger = logging.getLogger("autobot")

# 强制不走代理连接本地服务
os.environ["NO_PROXY"] = "127.0.0.1,localhost"

# ================= 全局服务初始化 =================

cfg = ConfigManager()
cfg.ensure_workspace()

mcp = MCPClient(cfg.get("mcp_url", "http://localhost:18060/mcp"))

# ==================================================
#                  Tab 1: 内容创作
# ==================================================


def connect_llm(api_key, base_url):
    """连接 LLM 并获取模型列表"""
    if not api_key or not base_url:
        return gr.update(choices=[]), "⚠️ 请先填写 API Key 和 Base URL"
    try:
        svc = LLMService(api_key, base_url)
        models = svc.get_models()
        cfg.update({"api_key": api_key, "base_url": base_url})
        return (
            gr.update(choices=models, value=models[0] if models else None),
            f"✅ 已连接，加载 {len(models)} 个模型",
        )
    except Exception as e:
        logger.error("LLM 连接失败: %s", e)
        return gr.update(), f"❌ 连接失败: {e}"


def connect_sd(sd_url):
    """连接 SD 并获取模型列表"""
    try:
        svc = SDService(sd_url)
        ok, msg = svc.check_connection()
        if ok:
            models = svc.get_models()
            cfg.set("sd_url", sd_url)
            return gr.update(choices=models, value=models[0] if models else None), f"✅ {msg}"
        return gr.update(choices=[]), f"❌ {msg}"
    except Exception as e:
        logger.error("SD 连接失败: %s", e)
        return gr.update(choices=[]), f"❌ SD 连接失败: {e}"


def check_mcp_status(mcp_url):
    """检查 MCP 连接状态"""
    try:
        client = MCPClient(mcp_url)
        ok, msg = client.check_connection()
        if ok:
            cfg.set("mcp_url", mcp_url)
            return f"✅ MCP 服务正常 - {msg}"
        return f"❌ {msg}"
    except Exception as e:
        return f"❌ MCP 连接失败: {e}"


# ==================================================
#              小红书账号登录
# ==================================================


def get_login_qrcode(mcp_url):
    """获取小红书登录二维码"""
    try:
        client = MCPClient(mcp_url)
        result = client.get_login_qrcode()
        if "error" in result:
            return None, f"❌ 获取二维码失败: {result['error']}"
        qr_image = result.get("qr_image")
        msg = result.get("text", "")
        if qr_image:
            return qr_image, f"✅ 二维码已生成，请用小红书 App 扫码\n{msg}"
        return None, f"⚠️ 未获取到二维码图片，MCP 返回:\n{msg}"
    except Exception as e:
        logger.error("获取登录二维码失败: %s", e)
        return None, f"❌ 获取二维码失败: {e}"


def check_login(mcp_url):
    """检查小红书登录状态"""
    try:
        client = MCPClient(mcp_url)
        result = client.check_login_status()
        if "error" in result:
            return f"❌ {result['error']}"
        text = result.get("text", "")
        if "未登录" in text:
            return f"🔴 {text}"
        return f"🟢 {text}"
    except Exception as e:
        return f"❌ 检查登录状态失败: {e}"


def generate_copy(api_key, base_url, model, topic, style):
    """生成文案"""
    if not api_key:
        return "", "", "", "", "❌ 缺少 API Key"
    try:
        svc = LLMService(api_key, base_url, model)
        data = svc.generate_copy(topic, style)
        cfg.set("model", model)
        tags = data.get("tags", [])
        return (
            data.get("title", ""),
            data.get("content", ""),
            data.get("sd_prompt", ""),
            ", ".join(tags) if tags else "",
            "✅ 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]
    folder_name = f"{int(time.time())}_{safe_title}"
    folder_path = os.path.join(OUTPUT_DIR, folder_name)
    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)
            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 = MCPClient(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 = MCPClient(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(api_key, base_url, model, keyword, search_result):
    """AI 分析热点并给出建议"""
    if not search_result:
        return "❌ 请先搜索", "", ""
    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(api_key, base_url, 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"
    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 = MCPClient(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:
        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)