Update app.py

app.py

@@ -82,54 +82,56 @@ def run_analysis():
     try:
         since_date = (datetime.now() - timedelta(days=days_back)).strftime('%Y-%m-%d')
         until_date = datetime.now().strftime('%Y-%m-%d')
-        
+
         # 1. 抓貼文
         all_tweets = []
         for candidate in candidates:
             tweets = fetch_tweets_via_x_tools(candidate, since_date, until_date)
             all_tweets.extend(tweets)
-        
+
         if not all_tweets:
             raise ValueError("No tweets fetched. Using full dummy data.")
-        
+
         df_tweets = pd.DataFrame(all_tweets, columns=["日期", "使用者", "內容", "候選人"])
-        
+
         # 2. 情緒分析
-        df_tweets['情緒'] = df_tweets['內容'].apply(lambda x: sentiment(x)[0]['label'])
-        df_tweets['信心度'] = df_tweets['內容'].apply(lambda x: sentiment(x)[0]['score'])
-        
+        df_tweets['情緒'] = df_tweets['內容'].apply(lambda x: sentiment(x)['label'])
+        df_tweets['信心度'] = df_tweets['內容'].apply(lambda x: sentiment(x)['score'])
+
         # 統計每位候選人情緒比例
         summary = df_tweets.groupby(['候選人', '情緒']).size().unstack(fill_value=0)
         summary['總貼文'] = summary.sum(axis=1)
         summary['正面比率'] = summary.get('positive', 0) / summary['總貼文']
         summary['負面比率'] = summary.get('negative', 0) / summary['總貼文']
-        summary['日期'] = datetime.now().strftime('%Y-%m-%d %H:%M %Z')
-        
+
         # 3. 更新歷史資料
+        summary['日期'] = datetime.now().strftime('%Y-%m-%d %H:%M %Z')
         if os.path.exists(history_file):
             df_history = pd.read_csv(history_file)
-            df_history = pd.concat([df_history, summary.reset_index()[['日期', '候選人', '正面比率', '負面比率']]], ignore_index=True)
+            df_history = pd.concat([df_history, summary.reset_index()[['日期','候選人','正面比率','負面比率']]], ignore_index=True)
         else:
-            df_history = summary.reset_index()[['日期', '候選人', '正面比率', '負面比率']]
+            df_history = summary.reset_index()[['日期','候選人','正面比率','負面比率']]
         df_history.to_csv(history_file, index=False)
 
-        # 4-11. 可視化
-        plt.figure(figsize=(8, 5))
-        summary[['正面比率', '負面比率']].plot(kind='bar', stacked=True, colormap='coolwarm')
+        # ----------------- 圖表生成 -----------------
+        # 當日情緒比例
+        plt.figure(figsize=(8,5))
+        summary[['正面比率','負面比率']].plot(kind='bar', stacked=True, colormap='coolwarm')
         plt.title("候選人當日社群情緒比例")
         plt.ylabel("比例")
         plt.xlabel("候選人")
         plt.xticks(rotation=0)
         plt.tight_layout()
         buf = io.BytesIO()
-        plt.savefig(buf, format="png")
+        plt.savefig(buf, format='png')
         buf.seek(0)
-        img_b64_today = base64.b64encode(buf.read()).decode("utf-8")
+        img_b64_today = base64.b64encode(buf.read()).decode('utf-8')
         buf.close()
 
-        plt.figure(figsize=(10, 5))
+        # 歷史情緒趨勢
+        plt.figure(figsize=(10,5))
         for c in candidates:
-            temp = df_history[df_history['候選人'] == c]
+            temp = df_history[df_history['候選人']==c]
             plt.plot(temp['日期'], temp['正面比率'], marker='o', label=f"{c} 正面")
             plt.plot(temp['日期'], temp['負面比率'], marker='x', label=f"{c} 負面")
         plt.xticks(rotation=45)
@@ -138,12 +140,35 @@ def run_analysis():
         plt.legend()
         plt.tight_layout()
         buf = io.BytesIO()
-        plt.savefig(buf, format="png")
+        plt.savefig(buf, format='png')
         buf.seek(0)
-        img_b64_trend = base64.b64encode(buf.read()).decode("utf-8")
+        img_b64_trend = base64.b64encode(buf.read()).decode('utf-8')
         buf.close()
 
-        # 6. 新聞線索整合
+        # 其他圖表 placeholder（可自行生成圖表後轉 base64）
+        img_social_sentiment = ""
+        img_platform_performance = ""
+        img_candidate_volume = ""
+        img_candidate_sentiment = ""
+        img_knowledge_graph = ""
+
+        # 社群參與表格
+        engagement_table = f"""
+        <table class="min-w-full bg-white border border-gray-200">
+            <tr class="bg-gray-100 border-b">
+                <th class="py-2 px-4 border-r">總參與數</th>
+                <td class="py-2 px-4 border-r">{len(df_tweets)}</td>
+                <th class="py-2 px-4 border-r">正面情緒比例</th>
+                <td class="py-2 px-4 border-r">{df_tweets['情緒'].value_counts(normalize=True).get('positive',0):.1%}</td>
+                <th class="py-2 px-4 border-r">平均互動率</th>
+                <td class="py-2 px-4 border-r">3.9%</td>
+                <th class="py-2 px-4 border-r">活躍平台</th>
+                <td class="py-2 px-4">6</td>
+            </tr>
+        </table>
+        """
+
+        # 新聞��料
         if os.path.exists(news_file):
             df_news = pd.read_csv(news_file)
             news_summary = df_news.groupby('類別').size().to_dict()
@@ -152,29 +177,70 @@ def run_analysis():
             news_summary = {}
             news_table = "<p>未提供新聞資料</p>"
 
-        # 12. 載入模板
-        with open("templates/index.html", "r", encoding="utf-8") as f:
-            html_template = f.read()
+        # ----------------- 內嵌 HTML 模板 -----------------
+        html_template = """<!DOCTYPE html>
+<html lang="zh-TW">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>高雄市長選戰輿情分析</title>
+<script src="https://cdn.tailwindcss.com"></script>
+<style>
+body {{
+    background-color: #f3f4f6;
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+}}
+.card {{
+    background-color: white;
+    border-radius: 0.5rem;
+    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+    padding: 1.5rem;
+    margin-bottom: 1.5rem;
+}}
+.chart-container {{
+    max-width: 100%;
+    overflow-x: auto;
+}}
+</style>
+</head>
+<body class="p-6">
+<header class="bg-blue-600 text-white p-4 rounded-lg mb-6">
+<h1 class="text-3xl font-bold">高雄市長選戰輿情分析</h1>
+<p class="text-sm">更新時間: {report_date}</p>
+</header>
+<main class="grid grid-cols-1 md:grid-cols-2 gap-6">
+<div class="card">
+<h2 class="text-xl font-semibold mb-4">1. 當日社群貼文情緒</h2>
+<div class="chart-container">
+<img src="data:image/png;base64,{img_b64_today}" class="w-full">
+</div></div>
+<div class="card">
+<h2 class="text-xl font-semibold mb-4">2. 歷史情緒趨勢</h2>
+<div class="chart-container">
+<img src="data:image/png;base64,{img_b64_trend}" class="w-full">
+</div></div>
+<div class="card md:col-span-2">
+<h2 class="text-xl font-semibold mb-4">3. 社群媒體參與概況</h2>
+{engagement_table}
+</div>
+<div class="card md:col-span-2">
+<h2 class="text-xl font-semibold mb-4">9. 新聞議題統計</h2>
+<p>各類別新聞數量: {news_summary}</p>
+{news_table}
+</div>
+</main>
+<footer class="mt-6 text-center text-gray-500">
+<p>© 2025 高雄市長選戰輿情分析系統 | 由 xAI 技術支持</p>
+</footer>
+</body>
+</html>"""
 
         html_content = html_template.format(
-            report_date=datetime.now().strftime('%Y-%m-%d %H:%M %Z'),
+            report_date=datetime.now().strftime('%Y-%m-%d %H:%M'),
             img_b64_today=img_b64_today,
             img_b64_trend=img_b64_trend,
-            engagement_table="""
-                <table class="min-w-full bg-white border border-gray-200">
-                    <tr class="bg-gray-100 border-b">
-                        <th class="py-2 px-4 border-r">總參與數</th>
-                        <td class="py-2 px-4 border-r">{total_tweets}</td>
-                        <th class="py-2 px-4 border-r">正面情緒比例</th>
-                        <td class="py-2 px-4 border-r">{positive_pct:.1%}</td>
-                        <th class="py-2 px-4 border-r">平均互動率</th>
-                        <td class="py-2 px-4 border-r">3.9%</td>
-                        <th class="py-2 px-4 border-r">活躍平台</th>
-                        <td class="py-2 px-4">6</td>
-                    </tr>
-                </table>
-            """.format(total_tweets=len(df_tweets), positive_pct=df_tweets['情緒'].value_counts(normalize=True).get('positive', 0)),
-            news_summary=str(news_summary),
+            engagement_table=engagement_table,
+            news_summary=news_summary,
             news_table=news_table
         )