Update app.py

app.py

@@ -68,6 +68,11 @@ def fig_to_base64():
 def generate_charts(all_df, summary, df_hist):
     results = {}
     # 1. 每日情緒比例
+    # ===== 字型與樣式 =====
+    plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'Noto Sans TC', 'SimHei', 'Arial Unicode MS']
+    plt.rcParams['axes.unicode_minus'] = False
+    plt.style.use("seaborn-v0_8")
+
     fig = plt.figure(figsize=(8, 5))
     summary[['Positive Ratio', 'Negative Ratio', 'Neutral Ratio']].plot(
         kind='bar', stacked=True, colormap='coolwarm', ax=fig.gca()
@@ -77,6 +82,11 @@ def generate_charts(all_df, summary, df_hist):
     plt.xlabel("候選人")
     results["img_b64_today"] = fig_to_base64()
     # 2. 歷史情緒趨勢
+    # ===== 字型與樣式 =====
+    plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'Noto Sans TC', 'SimHei', 'Arial Unicode MS']
+    plt.rcParams['axes.unicode_minus'] = False
+    plt.style.use("seaborn-v0_8")
+
     fig = plt.figure(figsize=(10, 5))
     for c in candidates:
         temp = df_hist[df_hist['Candidate'] == c]
@@ -90,6 +100,11 @@ def generate_charts(all_df, summary, df_hist):
     plt.legend()
     results["img_b64_trend"] = fig_to_base64()
     # 3. 社群情緒趨勢
+    # ===== 字型與樣式 =====
+    plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'Noto Sans TC', 'SimHei', 'Arial Unicode MS']
+    plt.rcParams['axes.unicode_minus'] = False
+    plt.style.use("seaborn-v0_8")
+
     sentiment_trend = all_df.groupby([pd.Grouper(key='Date', freq='D'), 'Sentiment']).size().unstack(fill_value=0)
     sentiment_trend = sentiment_trend.div(sentiment_trend.sum(axis=1), axis=0).fillna(0)
     fig = plt.figure(figsize=(8, 5))
@@ -102,6 +117,11 @@ def generate_charts(all_df, summary, df_hist):
     plt.legend()
     results["img_social_sentiment"] = fig_to_base64()
     # 4. 平台表現
+    # ===== 字型與樣式 =====
+    plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'Noto Sans TC', 'SimHei', 'Arial Unicode MS']
+    plt.rcParams['axes.unicode_minus'] = False
+    plt.style.use("seaborn-v0_8")
+
     platforms = ["X", "Facebook", "Instagram", "PTT", "Line"]
     platform_counts = pd.Series({p: random.randint(10, 100) for p in platforms})
     fig = plt.figure(figsize=(8, 5))
@@ -111,6 +131,11 @@ def generate_charts(all_df, summary, df_hist):
     plt.ylabel("貼文數量")
     results["img_platform_performance"] = fig_to_base64()
     # 5. 候選人聲量趨勢
+    # ===== 字型與樣式 =====
+    plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'Noto Sans TC', 'SimHei', 'Arial Unicode MS']
+    plt.rcParams['axes.unicode_minus'] = False
+    plt.style.use("seaborn-v0_8")
+
     candidate_trend = all_df.groupby([pd.Grouper(key='Date', freq='D'), 'Candidate']).size().unstack(fill_value=0)
     fig = plt.figure(figsize=(8, 5))
     for c in candidates:
@@ -122,6 +147,11 @@ def generate_charts(all_df, summary, df_hist):
     plt.legend()
     results["img_candidate_volume"] = fig_to_base64()
     # 6. 候選人情緒分析
+    # ===== 字型與樣式 =====
+    plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'Noto Sans TC', 'SimHei', 'Arial Unicode MS']
+    plt.rcParams['axes.unicode_minus'] = False
+    plt.style.use("seaborn-v0_8")
+
     fig = plt.figure(figsize=(8, 5))
     summary[['Positive Ratio', 'Negative Ratio', 'Neutral Ratio']].plot(
         kind='bar', stacked=True, colormap='coolwarm', ax=fig.gca()