Update app.py

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objs as go
 import numpy as np
 from datetime import datetime
 from dataclasses import dataclass, field
@@ -18,8 +19,8 @@ def read_google_sheet(sheet_id, sheet_number=0):
         return None
 
 # 📊 Google Sheets ID
-sheet_id = "1Wc15DZWq48MxL7nXAsROJ6sRvH5njSa1ea0aaOGUOVk"
-gid = "1168424766"
+sheet_id = "18wlbzQ-ZmFDeBONHnK7YNwfuvwUhnAA9_64ERHPkwzk"
+gid = "1564149687"
 
 @dataclass
 class SurveyMappings:
@@ -54,319 +55,123 @@ class SurveyAnalyzer:
         
     def calculate_age(self, birth_year_column):
         """🔢 計算年齡（從民國年到實際年齡）"""
-        # 獲取當前年份（西元年）
         current_year = datetime.now().year
-        
-        # 將 NaN 或無效值處理為 NaN
         birth_years = pd.to_numeric(birth_year_column, errors='coerce')
-        
-        # 民國年份轉西元年份 (民國年+1911=西元年)
         western_years = birth_years + 1911
-        
-        # 計算年齡
         ages = current_year - western_years
-        
         return ages
 
-    def generate_report(self, df: pd.DataFrame) -> Dict[str, Any]:
-        """📝 生成問卷調查報告"""
+    def generate_dynamic_basic_stats(self, df: pd.DataFrame) -> List[Dict[str, Any]]:
+        """📊 生成動態基本統計數據"""
+        # 性別統計
+        gender_stats = df['1.   性別'].value_counts().reset_index()
+        gender_stats.columns = ['性別', '人數']
+        gender_stats['百分比'] = (gender_stats['人數'] / gender_stats['人數'].sum() * 100).round(1)
+        
+        # 教育程度統計
+        education_stats = df['３．教育程度'].value_counts().reset_index()
+        education_stats.columns = ['教育程度', '人數']
+        education_stats['百分比'] = (education_stats['人數'] / education_stats['人數'].sum() * 100).round(1)
+        
         # 計算年齡
         ages = self.calculate_age(df['２．出生年（民國__年）'])
-        
-        # 取得教育程度分布（帶計數單位）
-        education_counts = df['３．教育程度'].value_counts().to_dict()
-        education_with_counts = {k: f"{v}人" for k, v in education_counts.items()}
-        
-        # 性別分布（帶計數單位）
-        gender_counts = df['1.   性別'].value_counts().to_dict()
-        gender_with_counts = {k: f"{v}人" for k, v in gender_counts.items()}
-        
-        # 計算每個滿意度項目的平均分數和標準差
-        satisfaction_stats = {}
-        for i, col in enumerate(self.satisfaction_columns):
-            mean_score = df[col].mean()
-            std_dev = df[col].std()
-            satisfaction_stats[self.satisfaction_short_names[i]] = {
-                '平均分數': f"{mean_score:.2f}",
-                '標準差': f"{std_dev:.2f}"
-            }
-            
-        return {
-            '基本統計': {
-                '總受訪人數': len(df),
-                '性別分布': gender_with_counts,
-                '教育程度分布': education_with_counts,
-                '平均年齡': f"{ages.mean():.1f}歲"
+        age_groups = pd.cut(ages, bins=[0, 55, 65, 75, 100], labels=['55歲以下', '56-65歲', '66-75歲', '75歲以上'])
+        age_stats = age_groups.value_counts().reset_index()
+        age_stats.columns = ['年齡組', '人數']
+        age_stats['百分比'] = (age_stats['人數'] / age_stats['人數'].sum() * 100).round(1)
+        
+        return [
+            {
+                'category': '性別分佈',
+                'data': gender_stats.to_dict('records')
+            },
+            {
+                'category': '教育程度分佈',
+                'data': education_stats.to_dict('records')
             },
-            '滿意度統計': {
-                '整體平均滿意度': f"{df[self.satisfaction_columns].mean().mean():.2f}",
-                '各項滿意度': satisfaction_stats
+            {
+                'category': '年齡分佈',
+                'data': age_stats.to_dict('records')
             }
-        }
+        ]
 
-    def plot_satisfaction_scores(self, df: pd.DataFrame, venues=None, month=None, age_range=None):
-        """📊 各項滿意度平均分數圖表 - 美化版"""
+    def plot_sunburst_gender_distribution(self, df: pd.DataFrame, venues=None):
+        """🌞 性別分佈旭日圖"""
         # 過濾數據
         filtered_df = df.copy()
         if venues and '全部' not in venues:
-            filtered_df = filtered_df[filtered_df['場域名稱'].isin(venues)]
-        if month and month != '全部':
-            # 假設有一個月份欄位，如果沒有請調整
-            filtered_df = filtered_df[filtered_df['月份'] == month]
-        
-        # 年齡篩選
-        if age_range:
-            ages = self.calculate_age(filtered_df['２．出生年（民國__年）'])
-            age_mask = (ages >= age_range[0]) & (ages <= age_range[1])
-            filtered_df = filtered_df[age_mask]
-            
-        # 計算過濾後數據的平均和標準差
-        satisfaction_means = [filtered_df[col].mean() for col in self.satisfaction_columns]
-        satisfaction_stds = [filtered_df[col].std() for col in self.satisfaction_columns]
-        
-        # 創建數據框
-        satisfaction_df = pd.DataFrame({
-            '滿意度項目': self.satisfaction_short_names,
-            '平均分數': satisfaction_means,
-            '標準差': satisfaction_stds
-        })
-        
-        # 排序結果（可選）
-        satisfaction_df = satisfaction_df.sort_values(by='平均分數', ascending=False)
-        
-        # 建立顏色漸變映射
-        color_scale = [
-            [0, '#90CAF9'],  # 淺藍色
-            [0.5, '#2196F3'],  # 中藍色
-            [1, '#1565C0']  # 深藍色
-        ]
-        
-        # 繪製條形圖
-        fig = px.bar(
-            satisfaction_df, 
-            x='滿意度項目', 
-            y='平均分數',
-            error_y='標準差',
-            title='📊 各項滿意度平均分數與標準差分析',
-            color='平均分數',
-            color_continuous_scale=color_scale,
-            text='平均分數',
-            hover_data={
-                '滿意度項目': True, 
-                '平均分數': ':.2f', 
-                '標準差': ':.2f'
-            }
+            filtered_df = filtered_df[filtered_df['單位名稱'].isin(venues)]
+        
+        # 準備旭日圖數據
+        gender_edu_counts = filtered_df.groupby(['1.   性別', '３．教育程度']).size().reset_index(name='count')
+        
+        # 創建旭日圖
+        fig = px.sunburst(
+            gender_edu_counts, 
+            path=['1.   性別', '３．教育程度'], 
+            values='count',
+            title='👥 性別與教育程度分佈旭日圖',
+            color='1.   性別',
+            color_discrete_map={'男性': '#2196F3', '女性': '#F50057'}
         )
         
-        # 調整圖表佈局
+        # 更新佈局
         fig.update_layout(
-            font=dict(family="Arial", size=16),
-            title_font=dict(family="Arial Black", size=24),
-            title_x=0.5,  # 標題置中
-            xaxis_title="滿意度項目",
-            yaxis_title="平均分數",
-            yaxis_range=[0, 5],  # 評分範圍從0開始，視覺上更明顯
-            plot_bgcolor='rgba(240,240,240,0.8)',  # 淺灰色背景
-            paper_bgcolor='white',
-            xaxis_tickangle=-25,  # 斜角標籤，避免重疊
-            margin=dict(l=40, r=40, t=80, b=60),
-            legend_title_text="平均分數",
-            shapes=[
-                # 添加參考線 - 例如4分
-                dict(
-                    type='line',
-                    yref='y', y0=4, y1=4,
-                    xref='paper', x0=0, x1=1,
-                    line=dict(color='rgba(220,20,60,0.5)', width=2, dash='dash')
-                )
-            ],
-            annotations=[
-                # 參考線標籤
-                dict(
-                    x=0.02, y=4.1,
-                    xref='paper', yref='y',
-                    text='優良標準 (4分)',
-                    showarrow=False,
-                    font=dict(size=14, color='rgba(220,20,60,0.8)')
-                )
-            ]
-        )
-        
-        # 調整文字格式
-        fig.update_traces(
-            texttemplate='%{y:.2f}', 
-            textposition='outside',
-            marker_line_color='rgb(8,48,107)',
-            marker_line_width=1.5,
-            opacity=0.85
-        )
-        
-        # 添加受訪人數標註
-        num_respondents = len(filtered_df)
-        fig.add_annotation(
-            x=0.5,
-            xref='paper',
-            yref='paper',
-            text=f'受訪人數: {num_respondents}人',
-            showarrow=False,
-            font=dict(size=16),
-            bgcolor='rgba(255,255,255,0.8)',
-            bordercolor='rgba(0,0,0,0.2)',
-            borderwidth=1,
-            borderpad=4,
-            y=-0.2
+            title_font_size=24,
+            title_x=0.5,
+            width=700,
+            height=700
         )
         
         st.plotly_chart(fig, use_container_width=True)
 
-    def plot_gender_distribution(self, df: pd.DataFrame, venues=None, month=None, age_range=None):
-        """🟠 性別分佈圓餅圖 - 增強精緻版"""
+    def plot_satisfaction_treemap(self, df: pd.DataFrame, venues=None):
+        """🌈 滿意度指標樹狀圖"""
         # 過濾數據
         filtered_df = df.copy()
         if venues and '全部' not in venues:
-            filtered_df = filtered_df[filtered_df['場域名稱'].isin(venues)]
-        if month and month != '全部':
-            # 假設有一個月份欄位，如果沒有請調整
-            filtered_df = filtered_df[filtered_df['月份'] == month]
-            
-        # 年齡篩選
-        if age_range:
-            ages = self.calculate_age(filtered_df['２．出生年（民國__年）'])
-            age_mask = (ages >= age_range[0]) & (ages <= age_range[1])
-            filtered_df = filtered_df[age_mask]
-        
-        # 取得性別資料
-        gender_counts = filtered_df['1.   性別'].value_counts().reset_index()
-        gender_counts.columns = ['性別', '人數']
-        
-        # 計算百分比
-        total = gender_counts['人數'].sum()
-        gender_counts['百分比'] = (gender_counts['人數'] / total * 100).round(1)
-        gender_counts['標籤'] = gender_counts.apply(lambda x: f"{x['性別']}: {x['人數']}人 ({x['百分比']}%)", axis=1)
-        
-        # 獲取篩選條件說明
-        filter_description = []
-        if venues and '全部' not in venues:
-            filter_description.append(f"場域: {', '.join(venues)}")
-        if month and month != '全部':
-            filter_description.append(f"月份: {month}")
-        if age_range and (age_range[0] != min(self.calculate_age(df['２．出生年（民國__年）'])) or 
-                          age_range[1] != max(self.calculate_age(df['２．出生年（民國__年）']))):
-            filter_description.append(f"年齡: {age_range[0]}-{age_range[1]}歲")
-        
-        filter_text = "（" + ", ".join(filter_description) + "）" if filter_description else ""
-        
-        # 設定顏色映射 - 男性藍色，女性紅色 - 使用更精緻的顏色
-        color_map = {'男性': '#1976D2', '女性': '#D32F2F'}
-        
-        # 建立子圖佈局以添加更多自定義元素
-        fig = px.pie(
-            gender_counts, 
-            names='性別', 
-            values='人數', 
-            title=f'👥 受訪者性別分布{filter_text}',
-            color='性別',
-            color_discrete_map=color_map,
-            hover_data=['人數', '百分比'],
-            labels={'人數': '人數', '百分比': '百分比'},
-            custom_data=['標籤']
+            filtered_df = filtered_df[filtered_df['單位名稱'].isin(venues)]
+        
+        # 計算各滿意度項目的平均分數
+        satisfaction_means = {}
+        for col, short_name in zip(self.satisfaction_columns, self.satisfaction_short_names):
+            satisfaction_means[short_name] = filtered_df[col].mean()
+        
+        # 準備樹狀圖數據
+        treemap_data = pd.DataFrame.from_dict(satisfaction_means, orient='index', columns=['score']).reset_index()
+        treemap_data.columns = ['滿意度項目', '平均分數']
+        treemap_data['分數等級'] = pd.cut(
+            treemap_data['平均分數'], 
+            bins=[0, 3, 4, 5], 
+            labels=['一般', '良好', '優秀']
         )
         
-        # 更新圖表佈局
-        fig.update_layout(
-            font=dict(family="Arial", size=16),
-            title_font=dict(family="Arial Black", size=24),
-            title_x=0.5,  # 標題置中
-            legend_title_text="性別",
-            legend=dict(
-                orientation="h",
-                yanchor="bottom",
-                y=-0.2,
-                xanchor="center",
-                x=0.5,
-                font=dict(size=16),
-                bordercolor="#E0E0E0",
-                borderwidth=2
-            ),
-            margin=dict(l=20, r=20, t=80, b=100),
-            paper_bgcolor='white',
-            annotations=[
-                dict(
-                    text=f"總受訪人數: {total}人",
-                    x=0.5, y=-0.3,
-                    xref="paper",
-                    yref="paper",
-                    showarrow=False,
-                    font=dict(size=16, color="#616161")
-                )
-            ]
-        )
-        
-        # 添加男女比例標籤
-        male_count = gender_counts.loc[gender_counts['性別'] == '男性', '人數'].values[0] if '男性' in gender_counts['性別'].values else 0
-        female_count = gender_counts.loc[gender_counts['性別'] == '女性', '人數'].values[0] if '女性' in gender_counts['性別'].values else 0
-        
-        # 計算男女比例
-        if male_count > 0 and female_count > 0:
-            ratio = round(male_count / female_count, 2)
-            ratio_text = f"男女比例 = {ratio}:1"
-        elif male_count > 0 and female_count == 0:
-            ratio_text = "僅有男性"
-        elif female_count > 0 and male_count == 0:
-            ratio_text = "僅有女性"
-        else:
-            ratio_text = "無性別數據"
-            
-        fig.add_annotation(
-            text=ratio_text,
-            x=0.5, y=-0.15,
-            xref="paper",
-            yref="paper",
-            showarrow=False,
-            font=dict(size=16, color="#424242", family="Arial Bold")
+        # 創建樹狀圖
+        fig = px.treemap(
+            treemap_data, 
+            path=['分數等級', '滿意度項目'], 
+            values='平均分數',
+            color='平均分數',
+            color_continuous_scale='RdYlBu',
+            title='📊 滿意度指標樹狀圖'
         )
         
-        # 更新懸停資訊
-        fig.update_traces(
-            textinfo='percent+label',
-            hovertemplate='%{customdata[0]}',
-            textfont_size=16,
-            marker=dict(line=dict(color='#FFFFFF', width=2)),
-            pull=[0.03, 0.03],  # 稍微分離餅圖片段
-            rotation=45  # 旋轉角度
+        # 更新佈局
+        fig.update_layout(
+            title_font_size=24,
+            title_x=0.5,
+            width=800,
+            height=600
         )
         
         st.plotly_chart(fig, use_container_width=True)
-        
-        # 在圓餅圖下方添加簡單分析
-        st.markdown("""
-        <div style="background-color:#F5F5F5; padding:15px; border-radius:10px; margin-top:10px; border-left:5px solid #1976D2;">
-        <h4 style="color:#1976D2;">📊 性別分佈簡易分析</h4>
-        """, unsafe_allow_html=True)
-        
-        # 生成簡單分析文字
-        if total > 0:
-            majority_gender = '男性' if male_count > female_count else '女性' if female_count > male_count else '男女相等'
-            majority_pct = max(male_count, female_count) / total * 100 if male_count != female_count else 50
-            
-            if male_count != female_count:
-                st.markdown(f"""
-                <p>本次調查中，<strong>{majority_gender}</strong>佔多數，約佔總體的<strong>{majority_pct:.1f}%</strong>。</p>
-                """, unsafe_allow_html=True)
-            else:
-                st.markdown("<p>本次調查中，男女比例相等，各佔50%。</p>", unsafe_allow_html=True)
-        else:
-            st.markdown("<p>目前沒有足夠的性別數據進行分析。</p>", unsafe_allow_html=True)
-            
-        st.markdown("</div>", unsafe_allow_html=True)
 
-# 🎨 Streamlit UI
 def main():
     st.set_page_config(
-    page_title="數位示範場域問卷調查分析",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
+        page_title="數位示範場域問卷調查分析",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
 
     # 自定義CSS樣式
     st.markdown("""
@@ -386,24 +191,6 @@ def main():
         text-align: center;
         margin-bottom: 30px;
     }
-    .unit-name {
-        font-size: 28px;
-        font-weight: bold;
-        color: #1565C0;
-        text-align: center;
-        padding: 10px;
-        background-color: #E3F2FD;
-        border-radius: 8px;
-        margin: 20px 0;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-    }
-    .card {
-        padding: 20px;
-        border-radius: 10px;
-        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
-        margin-bottom: 20px;
-        background-color: white;
-    }
     </style>
     """, unsafe_allow_html=True)
     
@@ -418,137 +205,79 @@ def main():
     if df is not None:
         analyzer = SurveyAnalyzer()
 
-        # 設置單位名稱（假設有「單位名稱」欄位，若無則顯示預設值）
-        unit_name = "數位示範場域滿意度調查中心"
-        if '單位名稱' in df.columns and not df['單位名稱'].isnull().all():
-            unit_names = df['單位名稱'].unique()
-            if len(unit_names) == 1:
-                unit_name = unit_names[0]
-        
-        # 顯示單位名稱
-        st.markdown(f'<div class="unit-name">{unit_name}</div>', unsafe_allow_html=True)
+        # 場域名稱選擇
+        if '單位名稱' in df.columns:
+            venues = ['全部'] + sorted(df['單位名稱'].unique().tolist())
+        else:
+            venues = ['全部']
         
-        # 新增場域和月份篩選器（使用更美觀的設計）
-        st.sidebar.markdown("### 🔍 **數據篩選**")
-        st.sidebar.markdown("---")
+        # 側邊欄篩選
+        st.sidebar.header("🔍 數據篩選")
         
         # 場域選擇
-        if '場域名稱' in df.columns:
-            venues = ['全部'] + sorted(df['場域名稱'].unique().tolist())
-        else:
-            # 如果沒有場域欄位，創建10個虛擬場域供選擇
-            venue_names = [
-                "臺北數位樂學園", "新北創新學院", "桃園智慧中心", 
-                "臺中數位學苑", "臺南創客基地", "高雄創新園區",
-                "宜蘭數位中心", "花蓮創新基地", "臺東學習中心", "金門數位樂園"
-            ]
-            venues = ['全部'] + venue_names
-            
         selected_venues = st.sidebar.multiselect(
-            "📍 **選擇場域**", 
+            "📍 選擇場域", 
             venues, 
             default=['全部'],
             help="可選擇多個場域進行數據分析比較"
         )
-            
-        # 月份選擇
-        if '月份' in df.columns:
-            months = ['全部'] + sorted(df['月份'].unique().tolist())
-        else:
-            # 如果沒有月份欄位，可以創建虛擬月份選項
-            current_year = datetime.now().year
-            months = ['全部'] + [f'{current_year}年{i+1}月' for i in range(12)]
-            
-        selected_month = st.sidebar.selectbox(
-            "📅 **��擇月份**", 
-            months,
-            help="選擇特定月份查看數據趨勢"
-        )
         
-        # 年齡區間篩選
-        st.sidebar.markdown("### 📊 **年齡區間篩選**")
-        ages = analyzer.calculate_age(df['２．出生年（民國__年）'])
-        min_age, max_age = int(ages.min()), int(ages.max())
-        age_range = st.sidebar.slider(
-            "選擇年齡範圍", 
-            min_age, 
-            max_age, 
-            (min_age, max_age),
-            help="拖曳調整以篩選特定年齡區間的受訪者"
+        # 選擇分析類型
+        analysis_type = st.sidebar.radio(
+            "選擇分析類型",
+            ["📊 基本統計", "👥 性別分佈", "📈 滿意度分析"]
         )
 
-        # 📌 基本統計數據
-        st.sidebar.header("📌 選擇數據分析")
-        selected_analysis = st.sidebar.radio("選擇要查看的分析", 
-                                             ["📋 問卷統計報告", "📊 滿意度統計", "🟠 性別分佈"])
-
-        # 應用所有篩選條件
+        # 過濾數據
         filtered_df = df.copy()
         if selected_venues and '全部' not in selected_venues:
-            if '場域名稱' in filtered_df.columns:
-                filtered_df = filtered_df[filtered_df['場域名稱'].isin(selected_venues)]
-        if selected_month and selected_month != '全部':
-            if '月份' in filtered_df.columns:
-                filtered_df = filtered_df[filtered_df['月份'] == selected_month]
-        
-        # 年齡篩選
-        if age_range:
-            ages = analyzer.calculate_age(filtered_df['２．出生年（民國__年）'])
-            age_mask = (ages >= age_range[0]) & (ages <= age_range[1])
-            filtered_df = filtered_df[age_mask]
-        
-        # 顯示當前選擇的篩選器
-        filter_status = []
-        if selected_venues and '全部' not in selected_venues:
-            filter_status.append(f"📍 場域: {', '.join(selected_venues)}")
-        if selected_month and selected_month != '全部':
-            filter_status.append(f"📅 月份: {selected_month}")
-        if age_range and (age_range[0] != min(analyzer.calculate_age(df['２．出生年（民國__年）'])) or 
-                          age_range[1] != max(analyzer.calculate_age(df['２．出生年（民國__年）']))):
-            filter_status.append(f"👥 年齡: {age_range[0]}-{age_range[1]}歲")
-            
-        if filter_status:
-            st.markdown("""
-            <div style="background-color:#E3F2FD; padding:10px; border-radius:8px; margin-bottom:20px; border-left:4px solid #1976D2;">
-            <h4 style="margin-bottom:10px; color:#1565C0;">🔍 當前篩選條件</h4>
-            """, unsafe_allow_html=True)
-            
-            for status in filter_status:
-                st.markdown(f"<p style='margin:5px 0;'>{status}</p>", unsafe_allow_html=True)
-                
-            # 顯示篩選後的樣本數
-            st.markdown(f"""
-            <p style='margin-top:10px; font-weight:bold;'>📊 篩選後樣本數: {len(filtered_df)}人</p>
-            </div>
-            """, unsafe_allow_html=True)
-            
-        # 數據分析區塊
-        if selected_analysis == "📋 問卷統計報告":
-            st.markdown('<h2 style="color:#1976D2;">📋 問卷統計報告</h2>', unsafe_allow_html=True)
-            
-            # 生成目前篩選條件下的報告
-            report = analyzer.generate_report(filtered_df)
+            filtered_df = filtered_df[filtered_df['單位名稱'].isin(selected_venues)]
+
+        # 根據選擇的分析類型顯示不同的可視化
+        if analysis_type == "📊 基本統計":
+            st.header("📊 基本統計")
             
-            # 使用卡片樣式顯示統計信息
-            col1, col2 = st.columns(2)
+            # 生成動態統計數據
+            dynamic_stats = analyzer.generate_dynamic_basic_stats(filtered_df)
             
-            with col1:
-                st.markdown('<div class="card">', unsafe_allow_html=True)
-                st.markdown('<h3 style="color:#1976D2; border-bottom:1px solid #e0e0e0; padding-bottom:10px;">📊 基本統計數據</h3>', unsafe_allow_html=True)
-                
-                for key, value in report['基本統計'].items():
-                    if isinstance(value, dict):
-                        st.markdown(f"<p><strong>{key}:</strong></p>", unsafe_allow_html=True)
-                        for k, v in value.items():
-                            st.markdown(f"<p style='margin-left:20px;'>- {k}: {v}</p>", unsafe_allow_html=True)
-                    else:
-                        st.markdown(f"<p><strong>{key}:</strong> {value}</p>", unsafe_allow_html=True)
-                        
-                st.markdown('</div>', unsafe_allow_html=True)
+            # 創建三個列來顯示不同的統計數據
+            cols = st.columns(3)
             
-            with col2:
-                st.markdown('<div class="card">', unsafe_allow_html=True)
-                # Add the rest of the code here
+            for i, stat_group in enumerate(dynamic_stats):
+                with cols[i]:
+                    # 創建 Plotly 圖表
+                    df_stat = pd.DataFrame(stat_group['data'])
+                    
+                    # 創建長條圖
+                    fig = px.bar(
+                        df_stat, 
+                        x=df_stat.columns[0], 
+                        y='人數', 
+                        text='百分比',
+                        title=f"{stat_group['category']}",
+                        color=df_stat.columns[0]
+                    )
+                    
+                    # 更新佈局
+                    fig.update_traces(
+                        texttemplate='%{text}%', 
+                        textposition='outside'
+                    )
+                    fig.update_layout(
+                        height=400,
+                        yaxis_title='人數',
+                        xaxis_title=df_stat.columns[0]
+                    )
+                    
+                    st.plotly_chart(fig, use_container_width=True)
+
+        elif analysis_type == "👥 性別分佈":
+            st.header("👥 性別分佈")
+            analyzer.plot_sunburst_gender_distribution(df, selected_venues)
+
+        elif analysis_type == "📈 滿意度分析":
+            st.header("📈 滿意度分析")
+            analyzer.plot_satisfaction_treemap(df, selected_venues)
 
 if __name__ == "__main__":
     main()