import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from app.services.extruder_service import ExtruderService
from app.services.main_process_service import MainProcessService
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error


def show_metered_weight_regression():
    # 初始化服务
    extruder_service = ExtruderService()
    main_process_service = MainProcessService()

    # 页面标题
    st.title("米重多元线性回归分析")

    # 初始化会话状态用于日期同步
    if 'mr_start_date' not in st.session_state:
        st.session_state['mr_start_date'] = datetime.now().date() - timedelta(days=7)
    if 'mr_end_date' not in st.session_state:
        st.session_state['mr_end_date'] = datetime.now().date()
    if 'mr_quick_select' not in st.session_state:
        st.session_state['mr_quick_select'] = "最近7天"
    if 'mr_time_offset' not in st.session_state:
        st.session_state['mr_time_offset'] = 0.0
    if 'mr_selected_features' not in st.session_state:
        st.session_state['mr_selected_features'] = [
            '螺杆转速', '机头压力', '流程主速', '螺杆温度', 
            '后机筒温度', '前机筒温度', '机头温度'
        ]

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

    def on_date_change():
        st.session_state['mr_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['mr_quick_select'] == option else "secondary"
                if st.button(option, key=f"btn_mr_{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="mr_start_date",
                on_change=on_date_change
            )

        with cols[6]:
            end_date = st.date_input(
                "结束日期",
                label_visibility="collapsed",
                key="mr_end_date",
                on_change=on_date_change
            )

        with cols[7]:
            query_button = st.button("🚀 开始分析", key="mr_query", width='stretch')

        # 数据对齐调整
        st.markdown("---")
        offset_cols = st.columns([2, 4, 2])
        with offset_cols[0]:
            st.write("⏱️ **数据对齐调整**")
        with offset_cols[1]:
            time_offset = st.slider(
                "时间偏移 (分钟)",
                min_value=0.0,
                max_value=5.0,
                value=st.session_state['mr_time_offset'],
                step=0.1,
                help="调整主流程和温度数据的时间偏移，使其与挤出机米重数据对齐。"
            )
            st.session_state['mr_time_offset'] = time_offset
        with offset_cols[2]:
            st.write(f"当前偏移: {time_offset} 分钟")

        # 特征选择
        st.markdown("---")
        st.write("📋 **特征选择**")
        feature_cols = st.columns(2)
        all_features = [
            '螺杆转速', '机头压力', '流程主速', '螺杆温度',
            '后机筒温度', '前机筒温度', '机头温度'
        ]
        for i, feature in enumerate(all_features):
            with feature_cols[i % 2]:
                st.session_state['mr_selected_features'] = [
                    f for f in st.session_state['mr_selected_features'] if f in all_features
                ]
                if st.checkbox(
                    feature, 
                    key=f"feat_{feature}",
                    value=feature in st.session_state['mr_selected_features']
                ):
                    if feature not in st.session_state['mr_selected_features']:
                        st.session_state['mr_selected_features'].append(feature)
                else:
                    if feature in st.session_state['mr_selected_features']:
                        st.session_state['mr_selected_features'].remove(feature)

        if not st.session_state['mr_selected_features']:
            st.warning("至少需要选择一个特征变量")

    # 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 - 仅获取数据并缓存到会话状态
    if query_button:
        with st.spinner("正在获取数据..."):
            # 1. 获取完整的挤出机数据（包含所有时间点的螺杆转速和机头压力）
            df_extruder_full = extruder_service.get_extruder_data(start_dt, end_dt)

            # 2. 获取主流程控制数据
            df_main_speed = main_process_service.get_cutting_setting_data(start_dt, end_dt)

            df_temp = main_process_service.get_temperature_control_data(start_dt, end_dt)

            # 检查是否有数据
            has_data = any([
                df_extruder_full is not None and not df_extruder_full.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。")
                # 清除缓存数据
                for key in ['cached_extruder_full', 'cached_main_speed', 'cached_temp', 'last_query_start', 'last_query_end']:
                    if key in st.session_state:
                        del st.session_state[key]
                return

            # 缓存数据到会话状态
            st.session_state['cached_extruder_full'] = df_extruder_full
            st.session_state['cached_main_speed'] = df_main_speed
            st.session_state['cached_temp'] = df_temp
            st.session_state['last_query_start'] = start_dt
            st.session_state['last_query_end'] = end_dt

    # 数据处理和图表渲染 - 每次应用重新运行时执行（包括调整时间偏移时）
    if all(key in st.session_state for key in ['cached_extruder_full', 'cached_main_speed', 'cached_temp']):
        with st.spinner("正在分析数据..."):
            # 获取缓存数据
            df_extruder_full = st.session_state['cached_extruder_full']
            df_main_speed = st.session_state['cached_main_speed']
            df_temp = st.session_state['cached_temp']

            # 获取当前时间偏移量
            offset_delta = timedelta(minutes=st.session_state['mr_time_offset'])

            # 处理数据
            if df_extruder_full is not None and not df_extruder_full.empty:
                # 过滤机头压力大于2的值
                df_extruder_filtered = df_extruder_full[df_extruder_full['head_pressure'] <= 2]

                # 为米重数据创建偏移后的时间列（只对米重数据进行时间偏移）
                df_extruder_filtered['weight_time'] = df_extruder_filtered['time'] - offset_delta
            else:
                df_extruder_filtered = None

            # 检查是否有数据
            has_data = any([
                df_extruder_filtered is not None and not df_extruder_filtered.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

            # 数据整合与预处理
            def integrate_data(df_extruder_filtered, df_main_speed, df_temp):
                # 确保挤出机数据存在
                if df_extruder_filtered is None or df_extruder_filtered.empty:
                    return None

                # 创建只包含米重和偏移时间的主数据集
                df_weight = df_extruder_filtered[['weight_time', 'metered_weight']].copy()
                df_weight.rename(columns={'weight_time': 'time'}, inplace=True)  # 将weight_time重命名为time作为基准时间

                # 创建包含螺杆转速和原始时间的完整数据集
                df_screw = df_extruder_filtered[['time', 'screw_speed_actual']].copy()

                # 创建包含机头压力和原始时间的完整数据集
                df_pressure = df_extruder_filtered[['time', 'head_pressure']].copy()

                # 使用偏移后的米重时间整合螺杆转速数据
                df_merged = pd.merge_asof(
                    df_weight.sort_values('time'),
                    df_screw.sort_values('time'),
                    on='time',
                    direction='nearest',
                    tolerance=pd.Timedelta('1min')
                )

                # 使用偏移后的米重时间整合机头压力数据
                df_merged = pd.merge_asof(
                    df_merged.sort_values('time'),
                    df_pressure.sort_values('time'),
                    on='time',
                    direction='nearest',
                    tolerance=pd.Timedelta('1min')
                )

                # 整合主流程数据
                if df_main_speed is not None and not df_main_speed.empty:
                    df_main_speed = df_main_speed[['time', 'process_main_speed']]
                    df_merged = pd.merge_asof(
                        df_merged.sort_values('time'),
                        df_main_speed.sort_values('time'),
                        on='time',
                        direction='nearest',
                        tolerance=pd.Timedelta('1min')
                    )

                # 整合温度数据
                if df_temp is not None and not df_temp.empty:
                    temp_cols = ['time', 'nakata_extruder_screw_display_temp',
                               'nakata_extruder_rear_barrel_display_temp',
                               'nakata_extruder_front_barrel_display_temp',
                               'nakata_extruder_head_display_temp']
                    df_temp_subset = df_temp[temp_cols].copy()
                    df_merged = pd.merge_asof(
                        df_merged.sort_values('time'),
                        df_temp_subset.sort_values('time'),
                        on='time',
                        direction='nearest',
                        tolerance=pd.Timedelta('1min')
                    )

                # 重命名列以提高可读性
                df_merged.rename(columns={
                    'screw_speed_actual': '螺杆转速',
                    'head_pressure': '机头压力',
                    'process_main_speed': '流程主速',
                    'nakata_extruder_screw_display_temp': '螺杆温度',
                    'nakata_extruder_rear_barrel_display_temp': '后机筒温度',
                    'nakata_extruder_front_barrel_display_temp': '前机筒温度',
                    'nakata_extruder_head_display_temp': '机头温度'
                }, inplace=True)

                # 清理数据
                df_merged.dropna(subset=['metered_weight'], inplace=True)

                return df_merged

            # 执行数据整合
            df_analysis = integrate_data(df_extruder_filtered, df_main_speed, df_temp)

            if df_analysis is None or df_analysis.empty:
                st.warning("数据整合失败，请检查数据质量或调整时间范围。")
                return

            # 重命名米重列
            df_analysis.rename(columns={'metered_weight': '米重'}, inplace=True)

            # --- 原始数据趋势图 ---
            st.subheader("📈 原始数据趋势图")

            # 创建趋势图
            fig_trend = go.Figure()

            # 添加米重数据（使用偏移后的时间）
            if df_extruder_filtered is not None and not df_extruder_filtered.empty:
                fig_trend.add_trace(go.Scatter(
                    x=df_extruder_filtered['weight_time'],  # 使用偏移后的时间
                    y=df_extruder_filtered['metered_weight'],
                    name='米重 (Kg/m) [已偏移]',
                    mode='lines',
                    line=dict(color='blue', width=2)
                ))

                # 添加螺杆转速（使用原始时间）
                fig_trend.add_trace(go.Scatter(
                    x=df_extruder_filtered['time'],  # 使用原始时间
                    y=df_extruder_filtered['screw_speed_actual'],
                    name='螺杆转速 (RPM)',
                    mode='lines',
                    line=dict(color='green', width=1.5),
                    yaxis='y2'
                ))

                # 添加机头压力（使用原始时间，已过滤大于2的值）
                fig_trend.add_trace(go.Scatter(
                    x=df_extruder_filtered['time'],  # 使用原始时间
                    y=df_extruder_filtered['head_pressure'],
                    name='机头压力 (≤2)',
                    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_trend.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='y4'
                ))

            # 添加温度数据
            if df_temp is not None and not df_temp.empty:
                # 螺杆温度
                fig_trend.add_trace(go.Scatter(
                    x=df_temp['time'],
                    y=df_temp['nakata_extruder_screw_display_temp'],
                    name='螺杆温度 (°C)',
                    mode='lines',
                    line=dict(color='purple', width=1),
                    yaxis='y5'
                ))

            # 配置趋势图布局
            fig_trend.update_layout(
                title=f'原始数据趋势 (米重向前偏移 {st.session_state["mr_time_offset"]} 分钟)',
                xaxis=dict(
                    title='时间',
                    rangeslider=dict(visible=True),
                    type='date'
                ),
                yaxis=dict(
                    title='米重 (Kg/m)',
                    title_font=dict(color='blue'),
                    tickfont=dict(color='blue')
                ),
                yaxis2=dict(
                    title='螺杆转速 (RPM)',
                    title_font=dict(color='green'),
                    tickfont=dict(color='green'),
                    overlaying='y',
                    side='right'
                ),
                yaxis3=dict(
                    title='机头压力',
                    title_font=dict(color='orange'),
                    tickfont=dict(color='orange'),
                    overlaying='y',
                    side='right',
                    anchor='free',
                    position=0.85
                ),
                yaxis4=dict(
                    title='流程主速 (M/Min)',
                    title_font=dict(color='red'),
                    tickfont=dict(color='red'),
                    overlaying='y',
                    side='right',
                    anchor='free',
                    position=0.75
                ),
                yaxis5=dict(
                    title='温度 (°C)',
                    title_font=dict(color='purple'),
                    tickfont=dict(color='purple'),
                    overlaying='y',
                    side='left',
                    anchor='free',
                    position=0.15
                ),
                legend=dict(
                    orientation="h",
                    yanchor="bottom",
                    y=1.02,
                    xanchor="right",
                    x=1
                ),
                height=600,
                margin=dict(l=100, r=200, t=100, b=100),
                hovermode='x unified'
            )

            # 显示趋势图
            st.plotly_chart(fig_trend, width='stretch', config={'scrollZoom': True})

            # --- 多元线性回归分析 ---
            st.subheader("📊 多元线性回归分析")

            # 检查是否选择了特征
            if not st.session_state['mr_selected_features']:
                st.warning("请至少选择一个特征变量进行回归分析")
            else:
                # 检查所有选择的特征是否在数据中
                missing_features = [f for f in st.session_state['mr_selected_features'] if f not in df_analysis.columns]
                if missing_features:
                    st.warning(f"数据中缺少以下特征: {', '.join(missing_features)}")
                else:
                    # 准备数据
                    X = df_analysis[st.session_state['mr_selected_features']]
                    y = df_analysis['米重']

                    # 清理数据中的NaN值
                    combined = pd.concat([X, y], axis=1)
                    combined_clean = combined.dropna()
                    
                    # 检查清理后的数据量
                    if len(combined_clean) < 10:
                        st.warning("数据量不足或包含过多NaN值，无法进行有效的回归分析")
                    else:
                        # 重新分离X和y
                        X_clean = combined_clean[st.session_state['mr_selected_features']]
                        y_clean = combined_clean['米重']
                        
                        # 分割训练集和测试集
                        X_train, X_test, y_train, y_test = train_test_split(X_clean, y_clean, test_size=0.2, random_state=42)

                        # 训练模型
                        model = LinearRegression()
                        model.fit(X_train, y_train)

                        # 预测
                        y_pred = model.predict(X_test)
                        y_train_pred = model.predict(X_train)

                        # 计算评估指标
                        r2 = r2_score(y_test, y_pred)
                        mse = mean_squared_error(y_test, y_pred)
                        mae = mean_absolute_error(y_test, y_pred)
                        rmse = np.sqrt(mse)

                        # 显示模型性能
                        metrics_cols = st.columns(2)
                        with metrics_cols[0]:
                            st.metric("R² 得分", f"{r2:.4f}")
                            st.metric("均方误差 (MSE)", f"{mse:.6f}")
                        with metrics_cols[1]:
                            st.metric("平均绝对误差 (MAE)", f"{mae:.6f}")
                            st.metric("均方根误差 (RMSE)", f"{rmse:.6f}")

                        # --- 实际值与预测值对比 ---
                        st.subheader("🔄 实际值与预测值对比")

                        # 创建对比数据
                        compare_df = pd.DataFrame({
                            '实际值': y_test,
                            '预测值': y_pred
                        })
                        compare_df = compare_df.sort_index()

                        # 创建对比图
                        fig_compare = go.Figure()
                        fig_compare.add_trace(go.Scatter(
                            x=compare_df.index,
                            y=compare_df['实际值'],
                            name='实际值',
                            mode='lines+markers',
                            line=dict(color='blue', width=2)
                        ))
                        fig_compare.add_trace(go.Scatter(
                            x=compare_df.index,
                            y=compare_df['预测值'],
                            name='预测值',
                            mode='lines+markers',
                            line=dict(color='red', width=2, dash='dash')
                        ))
                        fig_compare.update_layout(
                            title='测试集: 实际米重 vs 预测米重',
                            xaxis=dict(title='样本索引'),
                            yaxis=dict(title='米重 (Kg/m)'),
                            legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1),
                            height=400
                        )
                        st.plotly_chart(fig_compare, width='stretch')

                        # --- 残差分析 ---
                        st.subheader("📉 残差分析")

                        # 计算残差
                        residuals = y_test - y_pred

                        # 创建残差图
                        fig_residual = go.Figure()
                        fig_residual.add_trace(go.Scatter(
                            x=y_pred,
                            y=residuals,
                            mode='markers',
                            marker=dict(color='green', size=8, opacity=0.6)
                        ))
                        fig_residual.add_shape(
                            type="line",
                            x0=y_pred.min(),
                            y0=0,
                            x1=y_pred.max(),
                            y1=0,
                            line=dict(color="red", width=2, dash="dash")
                        )
                        fig_residual.update_layout(
                            title='残差图',
                            xaxis=dict(title='预测值'),
                            yaxis=dict(title='残差'),
                            height=400
                        )
                        st.plotly_chart(fig_residual, width='stretch')

                        # --- 特征重要性 ---
                        st.subheader("⚖️ 特征重要性分析")

                        # 计算特征重要性（基于系数绝对值）
                        feature_importance = pd.DataFrame({
                            '特征': st.session_state['mr_selected_features'],
                            '系数': model.coef_,
                            '重要性': np.abs(model.coef_)
                        })
                        feature_importance = feature_importance.sort_values('重要性', ascending=False)

                        # 创建特征重要性图
                        fig_importance = px.bar(
                            feature_importance,
                            x='特征',
                            y='重要性',
                            title='特征重要性（基于系数绝对值）',
                            color='重要性',
                            color_continuous_scale='viridis'
                        )
                        fig_importance.update_layout(
                            xaxis=dict(tickangle=-45),
                            height=400
                        )
                        st.plotly_chart(fig_importance, width='stretch')

                        # 显示系数表
                        st.write("### 模型系数")
                        coef_df = pd.DataFrame({
                            '特征': ['截距'] + st.session_state['mr_selected_features'],
                            '系数': [model.intercept_] + list(model.coef_)
                        })
                        st.dataframe(coef_df, use_container_width=True)

                        # --- 预测功能 ---
                        st.subheader("🔮 米重预测")

                        # 创建预测表单
                        st.write("输入特征值进行米重预测:")
                        predict_cols = st.columns(2)
                        input_features = {}

                        for i, feature in enumerate(st.session_state['mr_selected_features']):
                            with predict_cols[i % 2]:
                                # 获取特征的统计信息
                                min_val = df_analysis[feature].min()
                                max_val = df_analysis[feature].max()
                                mean_val = df_analysis[feature].mean()

                                input_features[feature] = st.number_input(
                                    f"{feature}",
                                    key=f"pred_{feature}",
                                    value=float(mean_val),
                                    min_value=float(min_val),
                                    max_value=float(max_val),
                                    step=0.1
                                )

                        if st.button("预测米重"):
                            # 准备预测数据
                            input_data = [[input_features[feature] for feature in st.session_state['mr_selected_features']]]
                            # 预测
                            predicted_weight = model.predict(input_data)[0]
                            # 显示预测结果
                            st.success(f"预测米重: {predicted_weight:.4f} Kg/m")

                        # --- 数据预览 ---
                        st.subheader("🔍 数据预览")
                        st.dataframe(df_analysis.head(20), use_container_width=True)
    else:
        # 提示用户点击开始分析按钮
        st.info("请选择时间范围并点击'开始分析'按钮获取数据。")