Update app.py

app.py

@@ -104,25 +104,11 @@ class SurveyAnalyzer:
             }
         }
 
-    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"""
-        # 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]
+    def plot_satisfaction_scores(self, df: pd.DataFrame):
+        """📊 各項滿意度平均分數圖表"""
+        # 準備數據
+        satisfaction_means = [df[col].mean() for col in self.satisfaction_columns]
+        satisfaction_stds = [df[col].std() for col in self.satisfaction_columns]
         
         # 創建數據框
         satisfaction_df = pd.DataFrame({
@@ -131,96 +117,37 @@ class SurveyAnalyzer:
             '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',
+            title='📊 各項滿意度平均分數與標準差',
             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'
-            }
+            color_continuous_scale='Viridis',
+            text='平均分數'
         )
         
         # 調整圖表佈局
         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
+            font=dict(size=16),
+            title_font=dict(size=24),
             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)')
-                )
-            ]
+            yaxis_range=[1, 5],  # 假設評分範圍是 1-5
         )
         
         # 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
+            textposition='outside'
         )
         
         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���佈圓餅圖 - 增強精緻版"""
+    def plot_gender_distribution(self, df: pd.DataFrame, venues=None, month=None):
+        """🟠 性別分佈圓餅圖（使用藍色和紅色）"""
         # 過濾數據
         filtered_df = df.copy()
         if venues and '全部' not in venues:
@@ -229,13 +156,6 @@ class SurveyAnalyzer:
             # 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']
         
@@ -244,27 +164,14 @@ class SurveyAnalyzer:
         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
+        color_map = {'男性': '#2171b5', '女性': '#cb181d'}
         
-        # 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}',
+            title='🟠 受訪者性別分布',
             color='性別',
             color_discrete_map=color_map,
             hover_data=['人數', '百分比'],
@@ -272,144 +179,20 @@ class SurveyAnalyzer:
             custom_data=['標籤']
         )
         
-        # 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")
-        )
-        
-        # 更新懸停資訊
+        # 更新悬停信息
         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
+            hovertemplate='%{customdata[0]}'
         )
         
         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"
-)
+    st.set_page_config(page_title="問卷調查分析", layout="wide")
 
-    # 自定義CSS樣式
-    st.markdown("""
-    <style>
-    .main-header {
-        font-size: 42px;
-        font-weight: bold;
-        color: #1E88E5;
-        text-align: center;
-        margin-bottom: 10px;
-        padding-bottom: 15px;
-        border-bottom: 2px solid #e0e0e0;
-    }
-    .sub-header {
-        font-size: 24px;
-        color: #424242;
-        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)
-    
-    # 主標題與副標題
-    st.markdown('<div class="main-header">📊 數位示範場域問卷調查分析報告</div>', unsafe_allow_html=True)
-    st.markdown('<div class="sub-header">本報告提供全面的問卷調查分析與視覺化圖表，協助了解民眾滿意度與使用者特性</div>', unsafe_allow_html=True)
+    st.title("📊 問卷調查分析報告")
+    st.write("本頁面展示問卷調查數據的分析結果，包括統計信息與視覺化圖表。")
 
     # 讀取數據
     df = read_google_sheet(sheet_id, gid)
@@ -417,137 +200,52 @@ 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)
-        
-        # 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("---")
+        # 新增場域和月份篩選器
+        st.sidebar.header("🔍 數據篩選")
         
-        # 場域選擇
+        # 假設數據有「場域名稱」欄位，如果名稱不同請調整
         if '場域名稱' in df.columns:
             venues = ['全部'] + sorted(df['場域名稱'].unique().tolist())
+            selected_venues = st.sidebar.multiselect("選擇場域", venues, default=['全部'])
         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
+            venues = ['全部'] + [f'場域{i+1}' for i in range(10)]
+            selected_venues = st.sidebar.multiselect("選擇場域", venues, default=['全部'])
             
-        selected_venues = st.sidebar.multiselect(
-            "📍 **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
+        # 假設數據有「月份」欄位，如果沒有請調整
         if '月份' in df.columns:
             months = ['全部'] + sorted(df['月份'].unique().tolist())
+            selected_month = st.sidebar.selectbox("選擇月份", months)
         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(
-            "📅 **選擇月份**", 
-            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"
-        )
+            months = ['全部'] + [f'{i+1}月' for i in range(12)]
+            selected_month = st.sidebar.selectbox("選擇月份", months)
 
         # 📌 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
-        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)
-            
-            # 使用卡片樣式顯示統計信息
-            col1, col2 = st.columns(2)
-            
-            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)
-            
-            with col2:
-                st.markdown('<div class="card">', unsafe_allow_html=True)
-                # Add the rest of the code here
+            st.header("📋 問卷統計報告")
+            report = analyzer.generate_report(df)
+            for category, stats in report.items():
+                with st.expander(f"🔍 {category}", expanded=True):
+                    for key, value in stats.items():
+                        if key == '各項滿意度':
+                            st.write(f"**{key}:**")
+                            for item, item_stats in value.items():
+                                st.write(f"  - **{item}**: {', '.join([f'{k}: {v}' for k, v in item_stats.items()])}")
+                        else:
+                            st.write(f"**{key}**: {value}")
+
+        elif selected_analysis == "📊 滿意度統計":
+            st.header("📊 滿意度統計")
+            analyzer.plot_satisfaction_scores(df)
+
+        elif selected_analysis == "🟠 性別分佈":
+            st.header("🟠 性別分佈")
+            analyzer.plot_gender_distribution(df, selected_venues, selected_month)
 
 if __name__ == "__main__":
     main()