import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
from datetime import datetime, timedelta
from app.services.data_query_service import DataQueryService
from app.services.extruder_service import ExtruderService
from app.services.main_process_service import MainProcessService

def show_comprehensive_dashboard():
    # 初始化服务
    sorting_service = DataQueryService()
    extruder_service = ExtruderService()
    main_process_service = MainProcessService()

    # 页面标题
    st.title("条重综合分析")

    # 初始化会话状态用于日期同步
    if 'comp_start_date' not in st.session_state:
        st.session_state['comp_start_date'] = datetime.now().date() - timedelta(days=7)
    if 'comp_end_date' not in st.session_state:
        st.session_state['comp_end_date'] = datetime.now().date()
    if 'comp_quick_select' not in st.session_state:
        st.session_state['comp_quick_select'] = "最近7天"
    if 'time_offset' not in st.session_state:
        st.session_state['time_offset'] = 0

    # 定义回调函数
    def update_dates(qs):
        st.session_state['comp_quick_select'] = qs
        today = datetime.now().date()
        if qs == "今天":
            st.session_state['comp_start_date'] = today
            st.session_state['comp_end_date'] = today
        elif qs == "最近3天":
            st.session_state['comp_start_date'] = today - timedelta(days=3)
            st.session_state['comp_end_date'] = today
        elif qs == "最近7天":
            st.session_state['comp_start_date'] = today - timedelta(days=7)
            st.session_state['comp_end_date'] = today
        elif qs == "最近30天":
            st.session_state['comp_start_date'] = today - timedelta(days=30)
            st.session_state['comp_end_date'] = today

    def on_date_change():
        st.session_state['comp_quick_select'] = "自定义"

    # 查询条件区域
    with st.expander("🔍 查询配置", expanded=True):
        # 添加自定义 CSS 实现响应式换行
        st.markdown("""
            <style>
            /* 强制列容器换行 */
            [data-testid="stExpander"] [data-testid="column"] {
                flex: 1 1 120px !important;
                min-width: 120px !important;
            }
            /* 针对日期输入框列稍微加宽一点 */
            @media (min-width: 768px) {
                [data-testid="stExpander"] [data-testid="column"]:nth-child(6),
                [data-testid="stExpander"] [data-testid="column"]:nth-child(7) {
                    flex: 2 1 180px !important;
                    min-width: 180px !important;
                }
            }
            </style>
            """, unsafe_allow_html=True)
            
        # 创建布局
        cols = st.columns([1, 1, 1, 1, 1, 1.5, 1.5, 1])
        
        options = ["今天", "最近3天", "最近7天", "最近30天", "自定义"]
        for i, option in enumerate(options):
            with cols[i]:
                # 根据当前选择状态决定按钮类型
                button_type = "primary" if st.session_state['comp_quick_select'] == option else "secondary"
                if st.button(option, key=f"btn_comp_{option}", width='stretch', type=button_type):
                    update_dates(option)
                    st.rerun()

        with cols[5]:
            start_date = st.date_input(
                "开始日期", 
                label_visibility="collapsed",
                key="comp_start_date",
                on_change=on_date_change
            )
        
        with cols[6]:
            end_date = st.date_input(
                "结束日期", 
                label_visibility="collapsed",
                key="comp_end_date",
                on_change=on_date_change
            )

        # 在第二行添加时间偏移配置
        st.markdown("---")
        offset_cols = st.columns([2, 4, 2])
        with offset_cols[0]:
            st.write("⏱️ **生产对齐配置**")
        with offset_cols[1]:
            time_offset = st.slider(
                "挤出/主流程数据向后偏移 (分钟)", 
                min_value=0, 
                max_value=60, 
                value=st.session_state['time_offset'],
                help="由于胎面从挤出到分拣需要时间，将上游数据向后移动，使其与分拣磅秤上的重量数据在时间轴上对齐。"
            )
            st.session_state['time_offset'] = time_offset
        with offset_cols[2]:
            query_button = st.button("🚀 开始分析", key="comp_query", width='stretch')

    # 转换为datetime对象
    start_dt = datetime.combine(start_date, datetime.min.time())
    end_dt = datetime.combine(end_date, datetime.max.time())

    # 查询处理
    if query_button:
        with st.spinner("正在聚合多源数据..."):
            # 获取偏移量
            offset_delta = timedelta(minutes=st.session_state['time_offset'])
            
            # 1. 获取分拣磅秤数据 (作为基准，不偏移)
            df_sorting = sorting_service.get_sorting_scale_data(start_dt, end_dt)
            
            # 2. 获取挤出机数据 (应用偏移)
            df_extruder = extruder_service.get_extruder_data(start_dt, end_dt)
            if df_extruder is not None and not df_extruder.empty:
                df_extruder['time'] = df_extruder['time'] + offset_delta
                
            # 3. 获取主流程控制数据 (应用偏移)
            df_main_speed = main_process_service.get_cutting_setting_data(start_dt, end_dt)
            if df_main_speed is not None and not df_main_speed.empty:
                df_main_speed['time'] = df_main_speed['time'] + offset_delta
                
            df_temp = main_process_service.get_temperature_control_data(start_dt, end_dt)
            if df_temp is not None and not df_temp.empty:
                df_temp['time'] = df_temp['time'] + offset_delta

            # 检查是否有数据
            has_data = any([
                df_sorting is not None and not df_sorting.empty,
                df_extruder is not None and not df_extruder.empty,
                df_main_speed is not None and not df_main_speed.empty,
                df_temp is not None and not df_temp.empty       
            ])

            if not has_data:
                st.warning("所选时间段内未找到任何数据，请尝试调整查询条件。")
                return

            # 创建综合分析图表
            fig = go.Figure()

            # 添加分拣磅秤重量 (基图)
            if df_sorting is not None and not df_sorting.empty:
                # 检查是否包含阈值相关字段
                has_thresholds = all(col in df_sorting.columns for col in ['over_difference', 'under_difference'])
                
                if has_thresholds:
                    # 分离正常和异常数据点
                    # 复制数据以避免修改原始数据，并处理可能的零值/空值
                    plot_df = df_sorting.copy()
                    is_out_of_range = (plot_df['weight'] > plot_df['over_difference']) | (plot_df['weight'] < plot_df['under_difference'])
                    
                    normal_points = plot_df[~is_out_of_range]
                    anomaly_points = plot_df[is_out_of_range]
                    
                    # 1.1 正常重量以蓝色数据点显示
                    if not normal_points.empty:
                        fig.add_trace(go.Scatter(
                            x=normal_points['time'], 
                            y=normal_points['weight'],
                            name='分拣重量 (正常)',
                            mode='markers',
                            marker=dict(size=5, color='blue', opacity=0.7)
                        ))
                    
                    # 1.2 异常重量以红色三角显示
                    if not anomaly_points.empty:
                        fig.add_trace(go.Scatter(
                            x=anomaly_points['time'], 
                            y=anomaly_points['weight'],
                            name='分拣重量 (异常)',
                            mode='markers',
                            marker=dict(
                                size=7, 
                                color='red', 
                                symbol='triangle-up',
                                line=dict(width=1, color='darkred')
                            )
                        ))
                else:
                    # 如果没有阈值，则全部显示为普通蓝色点
                    fig.add_trace(go.Scatter(
                        x=df_sorting['time'], 
                        y=df_sorting['weight'],
                        name='分拣重量 (kg)',
                        mode='markers',
                        marker=dict(size=5, color='blue', opacity=0.7)
                    ))
                
                # 2. 显示上下限和标准值的曲线
                if 'baseline_value' in df_sorting.columns:
                     fig.add_trace(go.Scatter(
                         x=df_sorting['time'], 
                         y=df_sorting['baseline_value'],
                         name='基准值',
                         mode='lines',
                         line=dict(color='green', width=2),
                         opacity=0.6
                     ))
                
                if 'over_difference' in df_sorting.columns:
                    fig.add_trace(go.Scatter(
                        x=df_sorting['time'], 
                        y=df_sorting['over_difference'],
                        name='上限阈值',
                        mode='lines',
                        line=dict(color='red', width=1.5),
                        opacity=0.5
                    ))
                    
                if 'under_difference' in df_sorting.columns:
                    fig.add_trace(go.Scatter(
                        x=df_sorting['time'], 
                        y=df_sorting['under_difference'],
                        name='下限阈值',
                        mode='lines',
                        line=dict(color='orange', width=1.5),
                        opacity=0.5
                    ))

            # 添加挤出机米重
            if df_extruder is not None and not df_extruder.empty:
                # fig.add_trace(go.Scatter(
                #     x=df_extruder['time'], 
                #     y=df_extruder['metered_weight'],
                #     name='挤出机米重 (Kg/m)',
                #     mode='lines',
                #     line=dict(color='green', width=1.5),
                #     yaxis='y2'
                # ))
                # 添加挤出机实际转速
                fig.add_trace(go.Scatter(
                    x=df_extruder['time'], 
                    y=df_extruder['screw_speed_actual'],
                    name='挤出机实际转速 (RPM)',
                    mode='lines',
                    line=dict(color='orange', width=1.5),
                    yaxis='y3'
                ))

            # 添加流程主速
            if df_main_speed is not None and not df_main_speed.empty:
                fig.add_trace(go.Scatter(
                    x=df_main_speed['time'], 
                    y=df_main_speed['process_main_speed'],
                    name='流程主速 (M/Min)',
                    mode='lines',
                    line=dict(color='red', width=1.5),
                    yaxis='y3' # 共用速度轴
                ))
                
                # 添加裁切计数
                if 'cutting_count' in df_main_speed.columns:
                    fig.add_trace(go.Scatter(
                        x=df_main_speed['time'], 
                        y=df_main_speed['cutting_count'],
                        name='裁切计数',
                        mode='lines',
                        line=dict(color='purple', width=1.5),
                        yaxis='y5'
                    ))

            # 添加温度设定值
            if df_temp is not None and not df_temp.empty:
                temp_fields = {
                    'nakata_extruder_screw_display_temp': '螺杆显示 (°C)',
                    'nakata_extruder_rear_barrel_display_temp': '后机筒显示 (°C)',
                    'nakata_extruder_front_barrel_display_temp': '前机筒显示 (°C)',
                    'nakata_extruder_head_display_temp': '机头显示 (°C)'
                  
                }
                colors = ['#FF4B4B', '#FF8C00', '#FFD700', '#DA70D6']
                for i, (field, label) in enumerate(temp_fields.items()):
                    fig.add_trace(go.Scatter(
                        x=df_temp['time'], 
                        y=df_temp[field],
                        name=label,
                        mode='lines',
                        line=dict(width=1),
                        yaxis='y4'
                    ))

            # 设置多坐标轴布局
            fig.update_layout(
                title='条重综合趋势分析',
                xaxis=dict(
                    title='时间',
                    rangeslider=dict(visible=True),
                    type='date'
                ),
                yaxis=dict(
                    title='重量 (kg)',
                    title_font=dict(color='blue'),
                    tickfont=dict(color='blue')
                ),
                yaxis2=dict(
                    title='米重 (Kg/m)',
                    title_font=dict(color='green'),
                    tickfont=dict(color='green'),
                    overlaying='y',
                    side='right'
                ),
                yaxis3=dict(
                    title='速度 (RPM / M/Min)',
                    title_font=dict(color='red'),
                    tickfont=dict(color='red'),
                    overlaying='y',
                    side='right',
                    anchor='free',
                    position=0.85
                ),
                yaxis4=dict(
                    title='温度 (°C)',
                    title_font=dict(color='purple'),
                    tickfont=dict(color='purple'),
                    overlaying='y',
                    side='left',
                    anchor='free',
                    position=0.15
                ),
                yaxis5=dict(
                    title='裁切计数',
                    title_font=dict(color='purple'),
                    tickfont=dict(color='purple'),
                    overlaying='y',
                    side='right',
                    anchor='free',
                    position=0.7
                ),
                legend=dict(
                    orientation="h",
                    yanchor="bottom",
                    y=1.02,
                    xanchor="right",
                    x=1
                ),
                height=700,
                margin=dict(l=100, r=100, t=100, b=100),
                hovermode='x unified'
            )

            # 显示图表
            st.plotly_chart(fig, width='stretch', config={'scrollZoom': True})
            
            # 数据摘要
            # st.subheader("📊 数据摘要")
            # summary_cols = st.columns(4)
            
            # with summary_cols[0]:
            #     if df_sorting is not None and not df_sorting.empty:
            #         st.metric("平均重量", f"{df_sorting['weight'].mean():.2f} kg")
            
            # with summary_cols[1]:
            #     if df_extruder is not None and not df_extruder.empty:
            #         st.metric("平均米重", f"{df_extruder['metered_weight'].mean():.2f} Kg/m")
            
            # with summary_cols[2]:
            #     if df_main_speed is not None and not df_main_speed.empty:
            #         st.metric("平均主速", f"{df_main_speed['process_main_speed'].mean():.2f} M/Min")
            
            # with summary_cols[3]:
            #     if df_temp is not None and not df_temp.empty:
            #         st.metric("平均螺杆温控", f"{df_temp['nakata_extruder_screw_set_temp'].mean():.1f} °C")