import streamlit as st
import pandas as pd
import requests
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
import tempfile
import os

# 設置頁面配置
st.set_page_config(
    page_title="碳排放數據可視化分析",
    page_icon="🌱",
    layout="wide",
    initial_sidebar_state="expanded"
)

# 標題和介紹
st.title("🌱 碳排放數據可視化分析")
st.markdown("---")
st.write("此應用程式分析台灣公司的碳排放數據，包括範疇一和範疇二的排放量。")

# 側邊欄設置
st.sidebar.header("⚙️ 設置選項")

# 數據載入功能
@st.cache_data
def load_data():
    """載入並處理碳排放數據"""
    try:
        # 顯示載入狀態
        with st.spinner("正在載入數據..."):
            url = "https://mopsfin.twse.com.tw/opendata/t187ap46_O_1.csv"
            response = requests.get(url)
            
            # 使用臨時文件
            with tempfile.NamedTemporaryFile(mode='wb', suffix='.csv', delete=False) as tmp_file:
                tmp_file.write(response.content)
                tmp_file_path = tmp_file.name
            
            # 讀取CSV文件
            df = pd.read_csv(tmp_file_path, encoding="utf-8-sig")
            
            # 清理臨時文件
            os.unlink(tmp_file_path)
            
            # 數據清理
            original_shape = df.shape
            df = df.dropna()
            
            # 尋找正確的欄位名稱
            company_cols = [col for col in df.columns if "公司" in col or "代號" in col or "股票" in col]
            emission_cols = [col for col in df.columns if "排放" in col]
            
            # 自動識別欄位
            company_col = "公司代號"
            scope1_col = "範疇一排放量(公噸CO2e)"
            scope2_col = "範疇二排放量(公噸CO2e)"
            
            if company_col not in df.columns and company_cols:
                company_col = company_cols[0]
            
            if scope1_col not in df.columns:
                scope1_candidates = [col for col in emission_cols if "範疇一" in col or "Scope1" in col]
                if scope1_candidates:
                    scope1_col = scope1_candidates[0]
            
            if scope2_col not in df.columns:
                scope2_candidates = [col for col in emission_cols if "範疇二" in col or "Scope2" in col]
                if scope2_candidates:
                    scope2_col = scope2_candidates[0]
            
            # 轉換數值格式
            if scope1_col in df.columns:
                df[scope1_col] = pd.to_numeric(df[scope1_col], errors='coerce')
            if scope2_col in df.columns:
                df[scope2_col] = pd.to_numeric(df[scope2_col], errors='coerce')
            
            # 移除轉換後的空值
            available_cols = [col for col in [scope1_col, scope2_col, company_col] if col in df.columns]
            df = df.dropna(subset=available_cols)
            
            return df, original_shape, company_col, scope1_col, scope2_col, company_cols, emission_cols
    
    except Exception as e:
        st.error(f"載入數據時發生錯誤: {str(e)}")
        return None, None, None, None, None, None, None

# 載入數據
data_result = load_data()
if data_result[0] is not None:
    df, original_shape, company_col, scope1_col, scope2_col, company_cols, emission_cols = data_result
    
    # 顯示數據基本信息
    col1, col2, col3 = st.columns(3)
    with col1:
        st.metric("原始數據筆數", original_shape[0])
    with col2:
        st.metric("處理後數據筆數", df.shape[0])
    with col3:
        st.metric("總欄位數", df.shape[1])
    
    # 側邊欄控制項
    st.sidebar.subheader("📊 圖表選項")
    
    # 圖表類型選擇
    chart_types = st.sidebar.multiselect(
        "選擇要顯示的圖表:",
        ["旭日圖", "雙層圓餅圖", "散點圖", "綜合旭日圖"],
        default=["旭日圖", "雙層圓餅圖"]
    )
    
    # 公司數量選擇
    max_companies = min(30, len(df))
    num_companies = st.sidebar.slider(
        "顯示公司數量:",
        min_value=5,
        max_value=max_companies,
        value=min(15, max_companies),
        step=5
    )
    
    # 顯示數據統計
    if st.sidebar.checkbox("顯示數據統計", value=True):
        st.subheader("📈 數據統計摘要")
        
        if all(col in df.columns for col in [scope1_col, scope2_col]):
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**範疇一排放量統計:**")
                scope1_stats = df[scope1_col].describe()
                st.write(f"- 平均值: {scope1_stats['mean']:.2f} 公噸CO2e")
                st.write(f"- 中位數: {scope1_stats['50%']:.2f} 公噸CO2e")
                st.write(f"- 最大值: {scope1_stats['max']:.2f} 公噸CO2e")
                st.write(f"- 最小值: {scope1_stats['min']:.2f} 公噸CO2e")
            
            with col2:
                st.write("**範疇二排放量統計:**")
                scope2_stats = df[scope2_col].describe()
                st.write(f"- 平均值: {scope2_stats['mean']:.2f} 公噸CO2e")
                st.write(f"- 中位數: {scope2_stats['50%']:.2f} 公噸CO2e")
                st.write(f"- 最大值: {scope2_stats['max']:.2f} 公噸CO2e")
                st.write(f"- 最小值: {scope2_stats['min']:.2f} 公噸CO2e")
    
    # 圖表生成函數
    def create_sunburst_chart(df, num_companies):
        """創建旭日圖"""
        if all(col in df.columns for col in [company_col, scope1_col, scope2_col]):
            df_top = df.nlargest(num_companies, scope1_col)
            sunburst_data = []
            
            for _, row in df_top.iterrows():
                company = str(row[company_col])
                scope1 = row[scope1_col]
                scope2 = row[scope2_col]
                
                sunburst_data.extend([
                    dict(ids=f"公司-{company}", labels=f"公司 {company}", parents="", values=scope1 + scope2),
                    dict(ids=f"範疇一-{company}", labels=f"範疇一: {scope1:.0f}", parents=f"公司-{company}", values=scope1),
                    dict(ids=f"範疇二-{company}", labels=f"範疇二: {scope2:.0f}", parents=f"公司-{company}", values=scope2)
                ])
            
            fig_sunburst = go.Figure(go.Sunburst(
                ids=[d['ids'] for d in sunburst_data],
                labels=[d['labels'] for d in sunburst_data],
                parents=[d['parents'] for d in sunburst_data],
                values=[d['values'] for d in sunburst_data],
                branchvalues="total",
                hovertemplate='<b>%{label}</b><br>排放量: %{value:.0f} 公噸CO2e<extra></extra>',
                maxdepth=3
            ))
            
            fig_sunburst.update_layout(
                title=f"碳排放量旭日圖 (前{num_companies}家公司)",
                font_size=12,
                height=600
            )
            
            return fig_sunburst
        return None
    
    def create_nested_pie_chart(df, num_companies):
        """創建雙層圓餅圖"""
        if all(col in df.columns for col in [company_col, scope1_col, scope2_col]):
            df_top = df.nlargest(num_companies, scope1_col)
            
            fig = make_subplots(
                rows=1, cols=2,
                specs=[[{"type": "pie"}, {"type": "pie"}]],
                subplot_titles=("範疇一排放量", "範疇二排放量")
            )
            
            fig.add_trace(go.Pie(
                labels=df_top[company_col],
                values=df_top[scope1_col],
                name="範疇一",
                hovertemplate='<b>%{label}</b><br>範疇一排放量: %{value:.0f} 公噸CO2e<br>佔比: %{percent}<extra></extra>',
                textinfo='label+percent',
                textposition='auto'
            ), row=1, col=1)
            
            fig.add_trace(go.Pie(
                labels=df_top[company_col],
                values=df_top[scope2_col],
                name="範疇二",
                hovertemplate='<b>%{label}</b><br>範疇二排放量: %{value:.0f} 公噸CO2e<br>佔比: %{percent}<extra></extra>',
                textinfo='label+percent',
                textposition='auto'
            ), row=1, col=2)
            
            fig.update_layout(
                title_text=f"碳排放量圓餅圖比較 (前{num_companies}家公司)",
                showlegend=True,
                height=600
            )
            
            return fig
        return None
    
    def create_scatter_plot(df):
        """創建散點圖"""
        if all(col in df.columns for col in [company_col, scope1_col, scope2_col]):
            fig_scatter = px.scatter(
                df,
                x=scope1_col,
                y=scope2_col,
                hover_data=[company_col],
                title="範疇一 vs 範疇二排放量散點圖",
                labels={
                    scope1_col: "範疇一排放量 (公噸CO2e)",
                    scope2_col: "範疇二排放量 (公噸CO2e)"
                },
                hover_name=company_col
            )
            
            fig_scatter.update_layout(height=600)
            return fig_scatter
        return None
    
    def create_comprehensive_sunburst(df, num_companies):
        """創建綜合旭日圖"""
        if all(col in df.columns for col in [company_col, scope1_col, scope2_col]):
            df_copy = df.copy()
            df_copy['total_emission'] = df_copy[scope1_col] + df_copy[scope2_col]
            df_copy['emission_level'] = pd.cut(df_copy['total_emission'], 
                                            bins=[0, 1000, 5000, 20000, float('inf')], 
                                            labels=['低排放(<1K)', '中排放(1K-5K)', '高排放(5K-20K)', '超高排放(>20K)'])
            
            sunburst_data = []
            companies_per_level = max(1, num_companies // 4)
            
            for level in df_copy['emission_level'].unique():
                if pd.isna(level):
                    continue
                level_companies = df_copy[df_copy['emission_level'] == level].nlargest(companies_per_level, 'total_emission')
                
                for _, row in level_companies.iterrows():
                    company = str(row[company_col])
                    scope1 = row[scope1_col]
                    scope2 = row[scope2_col]
                    total = scope1 + scope2
                    
                    sunburst_data.extend([
                        dict(ids=str(level), labels=str(level), parents="", values=total),
                        dict(ids=f"{level}-{company}", labels=f"{company}", parents=str(level), values=total),
                        dict(ids=f"{level}-{company}-範疇一", labels=f"範疇一({scope1:.0f})", 
                             parents=f"{level}-{company}", values=scope1),
                        dict(ids=f"{level}-{company}-範疇二", labels=f"範疇二({scope2:.0f})", 
                             parents=f"{level}-{company}", values=scope2)
                    ])
            
            fig_comprehensive = go.Figure(go.Sunburst(
                ids=[d['ids'] for d in sunburst_data],
                labels=[d['labels'] for d in sunburst_data],
                parents=[d['parents'] for d in sunburst_data],
                values=[d['values'] for d in sunburst_data],
                branchvalues="total",
                hovertemplate='<b>%{label}</b><br>排放量: %{value:.0f} 公噸CO2e<extra></extra>',
                maxdepth=4
            ))
            
            fig_comprehensive.update_layout(
                title="分級碳排放量旭日圖",
                font_size=10,
                height=700
            )
            
            return fig_comprehensive
        return None
    
    # 顯示選中的圖表
    st.subheader("📊 互動式圖表")
    
    if "旭日圖" in chart_types:
        st.write("### 🌞 旭日圖")
        fig1 = create_sunburst_chart(df, num_companies)
        if fig1:
            st.plotly_chart(fig1, use_container_width=True)
        else:
            st.error("無法創建旭日圖，缺少必要欄位")
    
    if "雙層圓餅圖" in chart_types:
        st.write("### 🥧 雙層圓餅圖")
        fig2 = create_nested_pie_chart(df, num_companies)
        if fig2:
            st.plotly_chart(fig2, use_container_width=True)
        else:
            st.error("無法創建圓餅圖，缺少必要欄位")
    
    if "散點圖" in chart_types:
        st.write("### 📈 散點圖")
        fig3 = create_scatter_plot(df)
        if fig3:
            st.plotly_chart(fig3, use_container_width=True)
        else:
            st.error("無法創建散點圖，缺少必要欄位")
    
    if "綜合旭日圖" in chart_types:
        st.write("### 🌟 綜合旭日圖")
        fig4 = create_comprehensive_sunburst(df, num_companies)
        if fig4:
            st.plotly_chart(fig4, use_container_width=True)
        else:
            st.error("無法創建綜合旭日圖，缺少必要欄位")
    
    # 顯示原始數據
    if st.sidebar.checkbox("顯示原始數據"):
        st.subheader("📋 原始數據預覽")
        st.dataframe(df.head(100), use_container_width=True)
    
    # 數據下載功能
    if st.sidebar.button("下載處理後數據"):
        csv = df.to_csv(index=False, encoding='utf-8-sig')
        st.sidebar.download_button(
            label="💾 下載 CSV 文件",
            data=csv,
            file_name="carbon_emission_data.csv",
            mime="text/csv"
        )
    
    # 偵錯信息
    if st.sidebar.checkbox("顯示偵錯信息"):
        st.subheader("🔧 偵錯信息")
        st.write("**識別的欄位:**")
        st.write(f"- 公司欄位: {company_col}")
        st.write(f"- 範疇一欄位: {scope1_col}")
        st.write(f"- 範疇二欄位: {scope2_col}")
        st.write("**所有可用欄位:**")
        st.write(df.columns.tolist())

else:
    st.error("無法載入數據，請檢查網路連接或數據源。")

# 頁面底部信息
st.markdown("---")
st.markdown(
    """
    **數據來源:** 台灣證券交易所公開資訊觀測站  
    **更新時間:** 根據數據源自動更新  
    **製作:** Streamlit 碳排放數據分析應用
    """
)