xhs_factory/services/engagement.py

"""
services/engagement.py
评论管家：手动评论、回复、笔记加载等互动功能
"""
import logging

import gradio as gr

from mcp_client import get_mcp_client
from llm_service import LLMService
from services.connection import _get_llm_config
from services.hotspot import _pick_from_cache, _set_cache, _get_cache

logger = logging.getLogger("autobot")

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"""
    persona = _resolve_persona(persona)
    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"""
    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", ""),
            })

        _set_cache("my_notes", 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"""
    persona = _resolve_persona(persona)
    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}"