import Query
import BertEmbedding
import gradio as gr


def queryVideoInfoByTitle(title):
    if title is None or len(title) == 0:
        return '查询失败'
    results = Query.queryCollection(BertEmbedding.text_to_vector(title))
    if results is None or len(results) == 0:
        return '未找到结果'
    videoInfos = []

    videoInfos.append(
        '标题\t曝光人数\t曝光次数\t播放人数\t播放次数\t分享人数\t分享次数\t回流人数\t回流次数\t相似度得分\n')
    distances = results[0].distances
    for i in range(len(results[0])):
        hit = results[0][i]
        distance = distances[i]
        videoInfo = hit.entity
        title = videoInfo.get('title')
        preview_users = videoInfo.get('preview_users')
        preview_times = videoInfo.get('preview_times')
        view_users = videoInfo.get('view_users')
        view_times = videoInfo.get('view_times')
        play_users = videoInfo.get('play_users')
        play_times = videoInfo.get('play_times')
        share_users = videoInfo.get('share_users')
        share_times = videoInfo.get('share_times')
        return_users = videoInfo.get('return_users')
        return_times = videoInfo.get('return_times')
        str = f"{title}\t{preview_users}\t{preview_times}\t{view_users}\t{view_times}\t{play_users}\t{play_times}\t{share_users}\t{share_times}\t{return_users}\t{return_times}\t{distance}\n"
        videoInfos.append(str)

    videoInfos.append(
        f'{title}\t{preview_users}\t{preview_times}\t{view_users}\t{view_times}\t{play_users}\t{play_times}\t{share_users}\t{share_times}\t{return_users}\t{return_times}\t{distance}\n')
    return '\n'.join(videoInfos)


iface = gr.Interface(fn=queryVideoInfoByTitle,
                     inputs=gr.components.Textbox(
                         lines=7, label="请输入文本内容"),
                     outputs="text",
                     title="视频内容向量检索，相似度匹配")
iface.launch(share=False)