"""
从 raw_cases/case_*.json 中提取 source_url / 帖子链接，
解析 channel_content_id，再从 .cache/content_search 中查找对应的原始帖子数据。

主函数：extract_sources_to_json(raw_cases_dir)
  - 扫描该目录下所有 case_{platform}.json
  - 解析每个 "工序发现[].帖子链接"（新格式）或 "cases[].source_url"（旧格式）
  - 从项目根的 .cache/content_search/*.json 中匹配 channel_content_id
  - 把匹配到的完整 post 写入 {raw_cases_dir}/source.json
  - 同时下载图片到 {raw_cases_dir}/images/{case_id}/
"""

import json
import re
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
import asyncio
import aiohttp
from urllib.parse import urlparse, parse_qs, urlencode


# ── URL → (platform, content_id) 解析 ────────────────────────────────

_URL_PATTERNS = [
    # B站: https://www.bilibili.com/video/BV1xxx
    ("bili", re.compile(r"bilibili\.com/video/(BV[\w]+)")),
    # 小红书: https://www.xiaohongshu.com/explore/{id} 或 /discovery/item/{id}
    ("xhs", re.compile(r"xiaohongshu\.com/(?:explore|discovery/item)/([a-f0-9]+)")),
    # YouTube: https://www.youtube.com/watch?v={id} 或 https://youtu.be/{id}
    ("youtube", re.compile(r"(?:youtube\.com/watch\?v=|youtu\.be/)([\w-]+)")),
    # X/Twitter: https://x.com/{user}/status/{id} 或 twitter.com
    ("x", re.compile(r"(?:x\.com|twitter\.com)/[^/]+/status/(\d+)")),
    # 知乎: https://zhuanlan.zhihu.com/p/{id} 或 zhihu.com/question/{qid}/answer/{aid}
    ("zhihu", re.compile(r"zhuanlan\.zhihu\.com/p/(\d+)")),
    ("zhihu", re.compile(r"zhihu\.com/question/\d+/answer/(\d+)")),
    # 公众号: 通过 __biz 或整个 URL 作为 id（后备）
    ("gzh", re.compile(r"mp\.weixin\.qq\.com/s[/?]([^\s\"']+)")),
]


def parse_url(url: str) -> Optional[Tuple[str, str]]:
    """从 URL 解析出 (platform, content_id)。返回 None 表示无法解析。"""
    if not url or not isinstance(url, str):
        return None
    for platform, pat in _URL_PATTERNS:
        m = pat.search(url)
        if m:
            return platform, m.group(1)
    return None


# ── 从 case 文件中抽取所有条目（URL + evaluation） ────────────────────────────────

def extract_entries_from_case(case_data: Any) -> List[Dict[str, Any]]:
    """
    从 case 数据中抽取条目，每条包含 url 和 agent 的 evaluation。

    返回: [{"url": str, "evaluation": dict | None, "title": str | None, "case_id": str | None}]
    """
    entries: List[Dict[str, Any]] = []

    if not isinstance(case_data, dict):
        return entries

    # 新格式：工序发现[].帖子链接
    for item in case_data.get("工序发现", []) or []:
        if isinstance(item, dict):
            link = item.get("帖子链接") or item.get("source_url")
            if link:
                entries.append({
                    "url": link,
                    "evaluation": item.get("evaluation"),
                    "title": item.get("title"),
                    "case_id": item.get("case_id"),
                })

    # 主格式：cases[]
    for item in case_data.get("cases", []) or []:
        if isinstance(item, dict):
            link = item.get("source_url") or item.get("帖子链接")
            if link:
                entries.append({
                    "url": link,
                    "evaluation": item.get("evaluation"),
                    "title": item.get("title"),
                    "case_id": item.get("case_id"),
                })

    return entries


def extract_urls_from_case(case_data: Any) -> List[str]:
    """[Legacy] 旧接口，保留供可能的外部调用。内部改用 extract_entries_from_case。"""
    return [e["url"] for e in extract_entries_from_case(case_data)]


# ── 过滤规则（统一入口） ────────────────────────────────

# 默认阈值：body_text 最少字符数、agent 评分下限
DEFAULT_MIN_BODY_LEN = 30
DEFAULT_MIN_SCORE = 70.0
DEFAULT_CUTOFF_DATE = (2025, 10, 1)


def _is_before_cutoff(source: Dict[str, Any], cutoff_ts: int) -> bool:
    """判断帖子是否早于截止时间戳（秒级）

    如果 timestamp 为 0 或不存在，返回 False（保留）
    """
    post = source.get("post", {})
    if not isinstance(post, dict):
        return False

    ts = post.get("publish_timestamp")
    # 没有 timestamp 或为 0，保留
    if not ts or ts == 0:
        return False

    try:
        ts = int(ts)
        # 毫秒级转秒级
        if ts > 1000000000000:
            ts = ts / 1000
        return ts < cutoff_ts
    except (ValueError, TypeError):
        pass

    # 尝试解析字符串格式 "2025-05-02 19:25:30"
    try:
        from datetime import datetime
        dt = datetime.strptime(str(ts), "%Y-%m-%d %H:%M:%S")
        return dt.timestamp() < cutoff_ts
    except Exception:
        # 解析失败，保留
        return False


def _check_filters(
    source: Dict[str, Any],
    cutoff_ts: int,
    min_body_len: int,
    min_score: float,
) -> Optional[str]:
    """
    对一条 source 逐条检查过滤规则。

    返回:
        None          —— 条目合格
        非 None 字符串 —— 不合格的原因（写入 filter_reason）
    """
    post = source.get("post", {}) or {}

    # 1. body_text 完整性
    body = post.get("body_text") or post.get("desc") or ""
    if not isinstance(body, str) or len(body.strip()) < min_body_len:
        return f"missing_body_text:len={len(body.strip()) if isinstance(body, str) else 0}"

    # 2. agent 评分
    evaluation = source.get("evaluation")
    if not isinstance(evaluation, dict):
        return "missing_evaluation"
    quality = evaluation.get("quality")
    if not isinstance(quality, dict):
        return "missing_evaluation"
    score = quality.get("overall_score")
    if not isinstance(score, (int, float)):
        return "invalid_score"
    if score < min_score:
        return f"low_score:{score}"

    # 3. 过时（复用原 _is_before_cutoff 对时间戳的解析）
    if _is_before_cutoff(source, cutoff_ts):
        return "outdated"

    return None


# ── 从 cache 中构建 (platform, content_id) → post 索引 ────────────────────────────────

def _normalize_url(url: str) -> Optional[str]:
    """规范化 URL：排序 query 参数，去掉尾斜杠"""
    try:
        parsed = urlparse(url)
        if parsed.query:
            # 排序 query 参数
            params = parse_qs(parsed.query, keep_blank_values=True)
            sorted_query = urlencode(sorted(params.items()), doseq=True)
            normalized = f"{parsed.scheme}://{parsed.netloc}{parsed.path}?{sorted_query}"
        else:
            normalized = f"{parsed.scheme}://{parsed.netloc}{parsed.path}"
        return normalized.rstrip('/')
    except:
        return None


def build_cache_index(cache_dir: Path, trace_ids: Optional[List[str]] = None) -> Dict[Tuple[str, str], Dict[str, Any]]:
    """
    构建 (platform, channel_content_id) -> post 映射。

    Args:
        cache_dir: cache 目录路径
        trace_ids: 可选的 trace_id 列表。如果提供，只加载这些特定的 cache 文件；
                   否则扫描所有 cache 文件

    Returns:
        (platform, content_id) -> post 的映射字典
    """
    index: Dict[Tuple[str, str], Dict[str, Any]] = {}
    if not cache_dir.exists():
        return index

    # 如果提供了 trace_ids，只加载这些特定文件
    if trace_ids:
        cache_files = [cache_dir / f"{tid}.json" for tid in trace_ids if tid]
        cache_files = [f for f in cache_files if f.exists()]
    else:
        # 否则扫描所有 cache 文件
        cache_files = list(cache_dir.glob("*.json"))

    for cache_file in cache_files:
        try:
            with open(cache_file, "r", encoding="utf-8") as f:
                data = json.load(f)
        except Exception:
            continue

        for key, entry in data.items():
            if not key.startswith("search:"):
                continue
            platform = key.split(":", 1)[1]

            # 新格式：entry = {"history": [...], "latest_index": n}
            # 旧格式：entry = {"keyword": ..., "posts": [...]}
            if isinstance(entry, dict) and "history" in entry:
                post_lists = [h.get("posts", []) for h in entry.get("history", [])]
            elif isinstance(entry, dict) and "posts" in entry:
                post_lists = [entry.get("posts", [])]
            else:
                continue

            for posts in post_lists:
                for post in posts or []:
                    if not isinstance(post, dict):
                        continue
                    cid = post.get("channel_content_id")

                    # YouTube 平台用 video_id 而非 channel_content_id
                    if not cid and post.get("video_id"):
                        cid = post.get("video_id")
                        post["channel_content_id"] = cid  # 补全字段

                    if cid:
                        # 用 (platform, content_id) 作为索引键
                        index[(platform, str(cid))] = post

                    # 额外用 link / url 字段建立索引（用于 GZH 等平台）
                    link = post.get("link") or post.get("url") or ""
                    if link:
                        index[("url", link)] = post
                        # 规范化 URL 索引（排序 query 参数，去掉尾斜杠）
                        norm = _normalize_url(link)
                        if norm:
                            index[("norm_url", norm)] = post

    return index


# ── 主入口 ────────────────────────────────

def extract_sources_to_json(
    raw_cases_dir: Path,
    cache_dir: Optional[Path] = None,
    output_name: str = "source.json",
    trace_ids: Optional[List[str]] = None,
    min_body_len: int = DEFAULT_MIN_BODY_LEN,
    min_score: float = DEFAULT_MIN_SCORE,
    cutoff_date: Tuple[int, int, int] = DEFAULT_CUTOFF_DATE,
) -> Dict[str, Any]:
    """
    扫描 raw_cases_dir 下的 case_*.json，
    从 cache 中找出原始帖子，并对结果执行统一过滤（body_text / 评分 / 时效）。

    过滤规则（均可通过参数调整）:
      - body_text 为空或少于 min_body_len 字符  → filter_reason=missing_body_text
      - agent evaluation 缺失或 weighted_score < min_score → filter_reason=missing_evaluation / invalid_score / low_score
      - publish_timestamp 早于 cutoff_date → filter_reason=outdated

    参数:
        min_body_len: body_text 最少字符数，默认 30
        min_score:    agent 评分下限，默认 70
        cutoff_date:  过时截止日期 (year, month, day)，默认 (2025, 10, 1)

    返回统计信息 dict。
    """
    raw_cases_dir = Path(raw_cases_dir)
    if cache_dir is None:
        # 项目根目录：script 文件往上三级
        project_root = Path(__file__).resolve().parent.parent.parent.parent
        cache_dir = project_root / ".cache" / "content_search"
    cache_dir = Path(cache_dir)

    # 1. 构建 cache 索引
    cache_index = build_cache_index(cache_dir, trace_ids=trace_ids)

    # 2. 加载已有的 source.json（如果存在）
    output_file = raw_cases_dir / output_name
    existing_sources = []
    existing_ids = set()  # (platform, channel_content_id) 集合用于去重

    if output_file.exists():
        try:
            with open(output_file, "r", encoding="utf-8") as f:
                existing_data = json.load(f)
                existing_sources = existing_data.get("sources", [])
                # 构建已有的 ID 集合
                for src in existing_sources:
                    key = (src.get("platform"), src.get("channel_content_id"))
                    existing_ids.add(key)
        except Exception as e:
            print(f"Warning: Failed to load existing source.json: {e}")

    # 2.1 加载已有的 filtered_cases.json（如果存在）
    filtered_output_file = raw_cases_dir / "filtered_cases.json"
    existing_filtered_sources = []
    existing_filtered_ids = set()

    if filtered_output_file.exists():
        try:
            with open(filtered_output_file, "r", encoding="utf-8") as f:
                filtered_data = json.load(f)
                existing_filtered_sources = filtered_data.get("sources", [])
                for src in existing_filtered_sources:
                    key = (src.get("platform"), src.get("channel_content_id"))
                    existing_filtered_ids.add(key)
        except Exception as e:
            print(f"Warning: Failed to load existing filtered_cases.json: {e}")

    # 3. 扫描所有 case 文件
    matched: List[Dict[str, Any]] = []
    unmatched: List[Dict[str, Any]] = []
    seen_keys: set = set(existing_ids)  # 从已有的 ID 开始

    for case_file in sorted(raw_cases_dir.glob("case_*.json")):
        # 跳过自己（如果 source.json 误被命名成 case_*）
        if case_file.name == output_name:
            continue
        try:
            with open(case_file, "r", encoding="utf-8") as f:
                case_data = json.load(f)
        except Exception as e:
            # 尝试自动修复 JSON 格式错误
            try:
                from examples.process_pipeline.script.fix_json_quotes import try_fix_and_parse
                with open(case_file, "r", encoding="utf-8") as f:
                    raw_content = f.read()
                success, case_data, fix_desc = try_fix_and_parse(raw_content)
                if success:
                    # 修复成功，写回文件
                    with open(case_file, "w", encoding="utf-8") as f:
                        json.dump(case_data, f, ensure_ascii=False, indent=2)
                    print(f"   🔧 [Auto-Fix] Fixed {case_file.name}: {fix_desc}")
                else:
                    unmatched.append({"case_file": case_file.name, "error": str(e)})
                    continue
            except Exception:
                unmatched.append({"case_file": case_file.name, "error": str(e)})
                continue

        entries = extract_entries_from_case(case_data)
        for entry in entries:
            url = entry["url"]
            evaluation = entry.get("evaluation")
            # 解析 URL 得到 platform 和 content_id
            parsed = parse_url(url)
            if not parsed:
                unmatched.append({
                    "case_file": case_file.name,
                    "url": url,
                    "reason": "url_parse_failed",
                })
                continue

            platform, cid = parsed
            key = (platform, cid)
            if key in seen_keys:
                continue
            seen_keys.add(key)

            # 多级匹配：
            # 1. (platform, content_id) 精确匹配
            # 2. 完整 URL 匹配
            # 3. 规范化 URL 匹配
            post = cache_index.get(key)

            if not post:
                # 2. 完整 URL
                post = cache_index.get(("url", url))

            if not post:
                # 3. 规范化 URL
                norm = _normalize_url(url)
                if norm:
                    post = cache_index.get(("norm_url", norm))

            if post:
                # 统一用 cache 中的 channel_content_id 生成 case_id
                # 这样保证 case_id 和 cache 中的 ID 一致
                actual_cid = post.get("channel_content_id") or post.get("video_id") or cid
                actual_case_id = f"{platform}_{actual_cid}"

                matched.append({
                    "case_id": actual_case_id,
                    "case_file": case_file.name,
                    "platform": platform,
                    "channel_content_id": str(actual_cid),
                    "source_url": url,
                    "evaluation": evaluation,
                    "post": post,
                })
            else:
                unmatched.append({
                    "case_id": f"{platform}_{cid}",  # 统一格式的 ID
                    "case_file": case_file.name,
                    "platform": platform,
                    "channel_content_id": cid,
                    "source_url": url,
                    "reason": "not_in_cache",
                })

    # 4. 合并已有数据和新匹配的数据
    all_sources = existing_sources + matched

    # 5. 统一过滤：body_text 完整性 / agent 评分 / 时效
    from datetime import datetime as _dt
    cutoff_ts = int(_dt(*cutoff_date).timestamp())

    kept_sources: List[Dict[str, Any]] = []
    filtered_sources: List[Dict[str, Any]] = []
    reason_counts: Dict[str, int] = {}

    for s in all_sources:
        reason = _check_filters(s, cutoff_ts, min_body_len, min_score)
        if reason is None:
            kept_sources.append(s)
        else:
            # 记录过滤原因
            s_copy = dict(s)
            s_copy["filter_reason"] = reason
            filtered_sources.append(s_copy)
            # 统计原因类型（只取冒号前的类别）
            category = reason.split(":", 1)[0]
            reason_counts[category] = reason_counts.get(category, 0) + 1

    all_sources = kept_sources
    filtered_count = len(filtered_sources)

    # 6. 转换 timestamp 为可读格式
    _convert_timestamps(all_sources)
    _convert_timestamps(filtered_sources)

    # 7. 写 source.json
    output = {
        "total": len(all_sources),
        "cache_dir": str(cache_dir),
        "sources": all_sources,
    }

    output_file.parent.mkdir(parents=True, exist_ok=True)
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(output, f, ensure_ascii=False, indent=2)

    # 8. 写 filtered_cases.json（被过滤掉的帖子，按原因分组）
    if filtered_sources:
        for fs in filtered_sources:
            key = (fs.get("platform"), fs.get("channel_content_id"))
            if key not in existing_filtered_ids:
                existing_filtered_sources.append(fs)
                existing_filtered_ids.add(key)

        # 按原因类别分组
        by_reason: Dict[str, List[Dict[str, Any]]] = {}
        for fs in existing_filtered_sources:
            reason = fs.get("filter_reason", "unknown")
            category = reason.split(":", 1)[0]
            by_reason.setdefault(category, []).append(fs)

        filtered_output = {
            "total": len(existing_filtered_sources),
            "by_reason": {
                category: {
                    "count": len(items),
                    "sources": items,
                }
                for category, items in by_reason.items()
            },
        }
        with open(filtered_output_file, "w", encoding="utf-8") as f:
            json.dump(filtered_output, f, ensure_ascii=False, indent=2)

    # 9. 下载图片到 raw_cases/images/{case_id}/
    images_downloaded = download_images_for_sources(matched, raw_cases_dir)

    # 10. 构建被过滤条目的摘要（供续跑 feedback 使用）
    filtered_details: List[Dict[str, Any]] = []
    for fs in filtered_sources:
        post = fs.get("post", {}) or {}
        title = post.get("title") or fs.get("source_url", "")
        filtered_details.append({
            "case_id": fs.get("case_id", ""),
            "platform": fs.get("platform", ""),
            "title": title[:60] if title else "",
            "filter_reason": fs.get("filter_reason", ""),
        })

    # 返回统计信息
    return {
        "total_matched": len(matched),
        "total_existing": len(existing_sources),
        "total_unmatched": len(unmatched),
        "filtered_total": filtered_count,
        "filtered_reasons": reason_counts,
        "filtered_details": filtered_details,
        "images_downloaded": images_downloaded,
        "output_file": str(output_file),
    }


# ── 图片下载 ────────────────────────────────

def _get_image_urls_from_post(post: Dict[str, Any]) -> List[str]:
    """从 post 中提取所有图片 URL"""
    urls = []
    images = post.get("images", [])
    if isinstance(images, list):
        for img in images:
            if isinstance(img, str) and img.startswith("http"):
                urls.append(img)
            elif isinstance(img, dict) and "url" in img:
                urls.append(img["url"])

    image_url_list = post.get("image_url_list", [])
    if isinstance(image_url_list, list):
        for img_obj in image_url_list:
            if isinstance(img_obj, dict) and "image_url" in img_obj:
                urls.append(img_obj["image_url"])

    return urls


def _format_timestamp(ts: Any) -> Optional[str]:
    """将时间戳（秒/毫秒）转换为可读格式"""
    from datetime import datetime

    if ts is None or ts == 0 or ts == "":
        return None

    try:
        ts = int(ts)
        # 毫秒级时间戳
        if ts > 1000000000000:
            ts = ts / 1000
        dt = datetime.fromtimestamp(ts)
        return dt.strftime("%Y-%m-%d %H:%M:%S")
    except Exception:
        return None


def _convert_timestamps(sources: List[Dict[str, Any]]) -> None:
    """将 source 列表中 post 的时间戳字段替换为可读格式"""
    timestamp_fields = ["publish_timestamp", "modify_timestamp", "update_timestamp"]

    for src in sources:
        post = src.get("post", {})
        if not isinstance(post, dict):
            continue

        for field in timestamp_fields:
            if field in post:
                readable = _format_timestamp(post.get(field))
                if readable:
                    post[field] = readable


def download_images_for_sources(sources: List[Dict[str, Any]], raw_cases_dir: Path) -> int:
    """
    为新匹配的 sources 下载图片到 raw_cases/images/{case_id}/

    Returns:
        下载成功的图片总数
    """
    import urllib.request
    import urllib.error

    images_base = raw_cases_dir / "images"
    total_downloaded = 0

    # 设置 headers 避免被拒（X/Twitter 需要 User-Agent）
    opener = urllib.request.build_opener()
    opener.addheaders = [("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36")]
    urllib.request.install_opener(opener)

    for src in sources:
        case_id = src.get("case_id", "")
        post = src.get("post", {})
        image_urls = _get_image_urls_from_post(post)

        if not image_urls:
            continue

        case_dir = images_base / case_id
        case_dir.mkdir(parents=True, exist_ok=True)

        for idx, url in enumerate(image_urls):
            # 确定文件扩展名
            ext = ".jpg"
            if ".png" in url.lower():
                ext = ".png"
            elif ".webp" in url.lower():
                ext = ".webp"

            save_path = case_dir / f"{idx:02d}{ext}"
            if save_path.exists():
                total_downloaded += 1
                continue

            try:
                urllib.request.urlretrieve(url, str(save_path))
                total_downloaded += 1
            except Exception:
                # 下载失败就跳过，不中断流程
                pass

    return total_downloaded


if __name__ == "__main__":
    # CLI：python extract_sources.py <raw_cases_dir> [cache_dir]
    import sys
    if len(sys.argv) < 2:
        print("Usage: python extract_sources.py <raw_cases_dir> [cache_dir]")
        sys.exit(1)

    raw_cases_dir = Path(sys.argv[1])
    cache_dir = Path(sys.argv[2]) if len(sys.argv) > 2 else None

    result = extract_sources_to_json(raw_cases_dir, cache_dir=cache_dir)
    print(f"[OK] Matched: {result['total_matched']}, Unmatched: {result['total_unmatched']}")
    print(f"     Output: {raw_cases_dir / 'source.json'}")