title_with_video/applications/spider/__init__.py

"""
@author: luojunhui
"""
from datetime import datetime

from applications.feishu import bot
from applications.const import server_const
from applications.functions.video_item import VideoProducer
from applications.log import logging
from applications.match_algorithm import title_similarity_with_nlp
from .spiderAB import SearchABTest
from .spiderSchedule import SearchMethod


async def save_video_to_mysql(video_obj, user, trace_id, platform, content_id, crawler_video_table, db_client,
                              similarity_score):
    """
    异步处理微信 video_obj
    公众号和站内账号一一对应
    :param similarity_score:
    :param crawler_video_table: 爬虫表
    :param db_client:  mysql
    :param content_id：
    :param platform:
    :param user:
    :param trace_id:
    :param video_obj:
    :return:
    """
    Video = VideoProducer()
    if platform == "xg_search":
        mq_obj = Video.xg_video_produce(
            video_obj=video_obj,
            user=user,
            trace_id=trace_id,
        )
    elif platform == "baidu_search":
        mq_obj = Video.baidu_video_produce(
            video_obj=video_obj,
            user=user,
            trace_id=trace_id,
        )
    elif platform == "wx_search":
        mq_obj = Video.wx_video_produce(
            video_obj=video_obj,
            user=user,
            trace_id=trace_id,
        )
    elif platform == "dy_search":
        mq_obj = Video.dy_video_produce(
            video_obj=video_obj,
            user=user,
            trace_id=trace_id,
        )
    else:
        mq_obj = {}
    mq_obj['trace_id'] = trace_id
    mq_obj['content_id'] = content_id
    insert_sql = f"""
    INSERT INTO {crawler_video_table}
    (content_id, out_video_id, platform, video_title, play_count, like_count, publish_time, crawler_time, duration, video_url, cover_url, user_id, trace_id, score)
    values 
    (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);
    """
    await db_client.async_insert(
        sql=insert_sql,
        params=(
            content_id,
            mq_obj['video_id'],
            platform,
            mq_obj['video_title'],
            mq_obj['play_cnt'],
            mq_obj['like_cnt'],
            datetime.fromtimestamp(mq_obj['publish_timestamp']).strftime('%Y-%m-%d %H:%M:%S'),
            datetime.now().__str__(),
            mq_obj['duration'],
            mq_obj['video_url'],
            mq_obj['cover_url'],
            mq_obj['user_id'],
            trace_id,
            similarity_score
        )
    )


async def search_videos_from_web(info, gh_id_map, db_client):
    """
    search and send msg to ETL
    :param db_client:
    :param gh_id_map:
    :param info:
    :return:
    """
    default_account_id = 69637498
    search_AB = SearchABTest(info=info, searchMethod=SearchMethod())
    # 启三个搜索，每个搜索都保证要搜索到， 分别用key1， key2， key3去搜索
    trace_id = info['trace_id']
    gh_id = info['gh_id']
    content_id = info['content_id']
    recall_list = await search_AB.ab_6()
    logging(
        code="1006",
        info="搜索到{}条视频".format(len(recall_list)),
        data=recall_list,
        trace_id=info['trace_id']
    )
    # 按照标题相似度排序
    ranked_result = await title_similarity_with_nlp(content_title=info['ori_title'].split("@@")[-1],
                                                    recall_list=recall_list)
    ranked_list = ranked_result['result']
    if recall_list and not ranked_list:
        bot(
            title="NLP服务请求失败",
            detail={
                "trace_id": info['trace_id']
            },
            mention=False
        )

    for recall_obj in ranked_list:
        if recall_obj:
            platform = recall_obj['platform']
            recall_video = recall_obj['result']
            score = recall_obj['score']

            # 过滤掉nlp分低于0.45的
            if score < server_const.NLP_SIMILARITY_THRESHOLD:
                continue

            if recall_video:
                await save_video_to_mysql(
                    video_obj=recall_video,
                    user=gh_id_map.get(gh_id, default_account_id),
                    trace_id=trace_id,
                    platform=platform,
                    content_id=content_id,
                    crawler_video_table=info['crawler_video_table'],
                    db_client=db_client,
                    similarity_score=score
                )
    return len(ranked_list)