ocr_verify/streamlit_test/tools/history.py

import streamlit as st
import pandas as pd
from datetime import datetime, timedelta
import random
import plotly.express as px
import plotly.graph_objects as go

st.title("📚 History")
st.markdown("操作历史和活动记录")

# 模拟历史数据
@st.cache_data
def load_history_data():
    actions = ["登录", "登出", "创建", "编辑", "删除", "查看", "下载", "上传"]
    objects = ["用户账户", "订单记录", "产品信息", "系统设置", "报告文件", "配置文件"]
    users = ["张三", "李四", "王五", "赵六", "钱七", "孙八"]
    
    data = []
    for i in range(500):
        timestamp = datetime.now() - timedelta(
            days=random.randint(0, 90),
            hours=random.randint(0, 23),
            minutes=random.randint(0, 59)
        )
        
        data.append({
            'id': f"HIST-{4000 + i}",
            'timestamp': timestamp,
            'user': random.choice(users),
            'action': random.choice(actions),
            'object': random.choice(objects),
            'ip_address': f"192.168.1.{random.randint(1, 254)}",
            'status': random.choice(['成功', '失败', '警告']),
            'details': f"操作详情 {i+1}: 用户执行了相关操作",
            'session_id': f"SES-{random.randint(10000, 99999)}"
        })
    
    return pd.DataFrame(data)

# 加载数据
history_df = load_history_data()

# 时间范围选择
st.subheader("📅 时间范围")
col1, col2, col3 = st.columns(3)

with col1:
    time_filter = st.selectbox(
        "快速选择",
        ["最近1小时", "最近24小时", "最近7天", "最近30天", "最近90天", "自定义范围"]
    )

with col2:
    if time_filter == "自定义范围":
        start_date = st.date_input("开始日期", value=datetime.now() - timedelta(days=7))
    else:
        start_date = None

with col3:
    if time_filter == "自定义范围":
        end_date = st.date_input("结束日期", value=datetime.now())
    else:
        end_date = None

# 应用时间筛选
if time_filter == "最近1小时":
    cutoff = datetime.now() - timedelta(hours=1)
    filtered_df = history_df[history_df['timestamp'] >= cutoff]
elif time_filter == "最近24小时":
    cutoff = datetime.now() - timedelta(days=1)
    filtered_df = history_df[history_df['timestamp'] >= cutoff]
elif time_filter == "最近7天":
    cutoff = datetime.now() - timedelta(days=7)
    filtered_df = history_df[history_df['timestamp'] >= cutoff]
elif time_filter == "最近30天":
    cutoff = datetime.now() - timedelta(days=30)
    filtered_df = history_df[history_df['timestamp'] >= cutoff]
elif time_filter == "最近90天":
    cutoff = datetime.now() - timedelta(days=90)
    filtered_df = history_df[history_df['timestamp'] >= cutoff]
else:  # 自定义范围
    if start_date and end_date:
        filtered_df = history_df[
            (history_df['timestamp'].dt.date >= start_date) &
            (history_df['timestamp'].dt.date <= end_date)
        ]
    else:
        filtered_df = history_df

# 统计概览
st.subheader("📊 活动概览")
col1, col2, col3, col4 = st.columns(4)

with col1:
    st.metric("总操作数", len(filtered_df))

with col2:
    unique_users = filtered_df['user'].nunique()
    st.metric("活跃用户", unique_users)

with col3:
    success_rate = len(filtered_df[filtered_df['status'] == '成功']) / len(filtered_df) * 100 if len(filtered_df) > 0 else 0
    st.metric("成功率", f"{success_rate:.1f}%")

with col4:
    failed_ops = len(filtered_df[filtered_df['status'] == '失败'])
    st.metric("失败操作", failed_ops)

# 可视化图表
st.subheader("📈 活动趋势")

tab1, tab2, tab3 = st.tabs(["时间趋势", "用户活动", "操作分布"])

with tab1:
    # 按小时统计活动
    if len(filtered_df) > 0:
        hourly_activity = filtered_df.groupby(filtered_df['timestamp'].dt.hour).size().reset_index()
        hourly_activity.columns = ['hour', 'count']
        
        fig_hourly = px.bar(hourly_activity, x='hour', y='count', 
                           title='按小时统计的活动分布')
        st.plotly_chart(fig_hourly, use_container_width=True)

with tab2:
    # 用户活动统计
    if len(filtered_df) > 0:
        user_activity = filtered_df['user'].value_counts().reset_index()
        user_activity.columns = ['user', 'count']
        
        fig_users = px.pie(user_activity, values='count', names='user', 
                          title='用户活动分布')
        st.plotly_chart(fig_users, use_container_width=True)

with tab3:
    # 操作类型分布
    if len(filtered_df) > 0:
        action_counts = filtered_df['action'].value_counts().reset_index()
        action_counts.columns = ['action', 'count']
        
        fig_actions = px.bar(action_counts, x='action', y='count',
                            title='操作类型分布')
        st.plotly_chart(fig_actions, use_container_width=True)

# 筛选器
st.subheader("🔍 详细筛选")
col1, col2, col3, col4 = st.columns(4)

with col1:
    selected_users = st.multiselect(
        "用户",
        options=filtered_df['user'].unique(),
        default=filtered_df['user'].unique()
    )

with col2:
    selected_actions = st.multiselect(
        "操作类型",
        options=filtered_df['action'].unique(),
        default=filtered_df['action'].unique()
    )

with col3:
    selected_status = st.multiselect(
        "状态",
        options=filtered_df['status'].unique(),
        default=filtered_df['status'].unique()
    )

with col4:
    search_term = st.text_input("搜索", placeholder="搜索详情...")

# 应用筛选
final_df = filtered_df[
    (filtered_df['user'].isin(selected_users)) &
    (filtered_df['action'].isin(selected_actions)) &
    (filtered_df['status'].isin(selected_status))
]

if search_term:
    final_df = final_df[
        final_df['details'].str.contains(search_term, case=False, na=False) |
        final_df['object'].str.contains(search_term, case=False, na=False)
    ]

# 历史记录表格
st.subheader("📋 历史记录")

# 排序选项
sort_order = st.radio("排序", ["最新在前", "最旧在前"], horizontal=True)
final_df = final_df.sort_values('timestamp', ascending=(sort_order == "最旧在前"))

# 分页
items_per_page = st.selectbox("每页显示", [25, 50, 100], index=1)
total_pages = (len(final_df) - 1) // items_per_page + 1 if len(final_df) > 0 else 1

if total_pages > 1:
    page = st.selectbox("页面", range(1, total_pages + 1))
    start_idx = (page - 1) * items_per_page
    end_idx = start_idx + items_per_page
    display_df = final_df.iloc[start_idx:end_idx]
else:
    display_df = final_df

# 显示数据
if len(display_df) > 0:
    st.dataframe(
        display_df[['timestamp', 'user', 'action', 'object', 'status', 'ip_address', 'details']],
        use_container_width=True,
        column_config={
            'timestamp': st.column_config.DatetimeColumn(
                "时间",
                format="MM-DD HH:mm:ss"
            ),
            'status': st.column_config.SelectboxColumn(
                "状态",
                options=['成功', '失败', '警告']
            )
        }
    )
    
    # 导出功能
    if st.button("📥 导出当前数据"):
        csv = display_df.to_csv(index=False)
        st.download_button(
            label="下载CSV文件",
            data=csv,
            file_name=f"history_export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
            mime="text/csv"
        )
else:
    st.warning("没有找到匹配的历史记录")

# 实时更新选项
st.subheader("🔄 实时更新")
auto_refresh = st.checkbox("启用自动刷新 (30秒)")

if auto_refresh:
    st.info("⏱️ 页面将每30秒自动刷新")
    time.sleep(30)
    st.rerun()