import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta
from app.services.extruder_service import ExtruderService
from app.services.data_processing_service import DataProcessingService

# 初始化服务
extruder_service = ExtruderService()
processing_service = DataProcessingService()

def run_extruder_dashboard():
    # 页面标题
    st.title("挤出机数据分析仪表盘")

    # 侧边栏：日期范围选择
    st.sidebar.header("查询配置")
    start_date = st.sidebar.date_input("开始日期", datetime.now() - timedelta(days=7))
    end_date = st.sidebar.date_input("结束日期", datetime.now())

    # 转换为datetime对象（包含时间）
    start_datetime = datetime.combine(start_date, datetime.min.time())
    end_datetime = datetime.combine(end_date, datetime.max.time())

    # 查询按钮
    if st.sidebar.button("查询数据"):
        # 验证日期范围
        if start_datetime > end_datetime:
            st.error("开始日期不能晚于结束日期！")
        else:
            # 显示加载状态
            with st.spinner("正在查询数据..."):
                # 查询数据
                raw_data = extruder_service.get_extruder_data(start_datetime, end_datetime)
                
                if raw_data is None or raw_data.empty:
                    st.warning("未查询到数据，请检查日期范围或数据库连接！")
                else:
                    # 清洗数据
                    cleaned_data = processing_service.clean_data(raw_data)
                    
                    # 检测换批事件
                    batch_changes = extruder_service.detect_batch_changes(cleaned_data)
                    
                    # 分析参数趋势
                    trends = extruder_service.analyze_parameter_trends(cleaned_data)
                    
                    # 检测异常
                    anomalies = extruder_service.detect_anomalies(cleaned_data)
                    
                    # 显示数据概览
                    st.subheader("数据概览")
                    col1, col2, col3, col4 = st.columns(4)
                    
                    with col1:
                        st.metric("总记录数", len(cleaned_data))
                    
                    with col2:
                        st.metric("换批次数", len(batch_changes))
                    
                    with col3:
                        st.metric("数据时间范围", f"{cleaned_data['time'].min()} 至 {cleaned_data['time'].max()}")
                    
                    with col4:
                        st.metric("异常数量", len(anomalies))
                    
                    # 显示换批分析
                    st.subheader("换批分析")
                    if not batch_changes.empty:
                        # 准备展示数据
                        batch_display = batch_changes[['spec_name', 'start_time', 'end_time', 'duration_minutes', 'production_kg']].copy()
                        batch_display.columns = ['SPEC编号', '开始时间', '结束时间', '持续时长(分钟)', '生产数量(kg)']
                        
                        # 格式化时间
                        batch_display['开始时间'] = batch_display['开始时间'].dt.strftime('%Y-%m-%d %H:%M:%S')
                        batch_display['结束时间'] = batch_display['结束时间'].dt.strftime('%Y-%m-%d %H:%M:%S')
                        
                        # 显示数据表格
                        st.dataframe(batch_display, use_container_width=True)
                    else:
                        st.warning("未检测到换批事件")
                    
                    # 显示参数趋势图
                    st.subheader("参数趋势图")
                    
                    # 螺杆转速趋势
                    st.subheader("螺杆转速趋势")
                    fig1 = px.line(
                        cleaned_data,
                        x='time',
                        y=['screw_speed_set', 'screw_speed_actual'],
                        labels={
                            'time': '时间',
                            'value': '转速(rpm)',
                            'variable': '参数'
                        },
                        title='螺杆转速趋势',
                        color_discrete_map={
                            'screw_speed_set': 'blue',
                            'screw_speed_actual': 'green'
                        }
                    )
                    fig1.for_each_trace(lambda t: t.update(name={
                        'screw_speed_set': '设定转速',
                        'screw_speed_actual': '实际转速'
                    }[t.name]))
                    st.plotly_chart(fig1, use_container_width=True)
                    
                    # 挤出机电流趋势
                    st.subheader("挤出机电流趋势")
                    fig2 = px.line(
                        cleaned_data,
                        x='time',
                        y='extruder_current',
                        labels={
                            'time': '时间',
                            'extruder_current': '电流(A)'
                        },
                        title='挤出机电流趋势'
                    )
                    st.plotly_chart(fig2, use_container_width=True)
                    
                    # 机头压力趋势
                    st.subheader("机头压力趋势")
                    fig3 = px.line(
                        cleaned_data,
                        x='time',
                        y='head_pressure',
                        labels={
                            'time': '时间',
                            'head_pressure': '压力(MPa)'
                        },
                        title='机头压力趋势'
                    )
                    st.plotly_chart(fig3, use_container_width=True)
                    
                    # 米称重量趋势
                    st.subheader("米称重量趋势")
                    fig4 = px.line(
                        cleaned_data,
                        x='time',
                        y='metered_weight',
                        labels={
                            'time': '时间',
                            'metered_weight': '重量(kg)'
                        },
                        title='米称重量趋势'
                    )
                    st.plotly_chart(fig4, use_container_width=True)
                    
                    
                    # 显示原始数据
                    st.subheader("原始数据")
                    st.dataframe(cleaned_data, use_container_width=True)

    # 关闭数据库连接
    def on_app_close():
        extruder_service.close_connection()

    # 注册应用关闭回调
    st.session_state['app_close'] = on_app_close