perf: offload heavy blocking I/O (matplotlib, pandas) to asyncio threads

SubMind.py

@@ -1,4 +1,5 @@
 import sqlite3
+import asyncio
 import os
 import html
 import requests
@@ -447,186 +448,208 @@ async def stats(update: Update, context: CallbackContext):
     user_id = update.effective_user.id
     await update.message.reply_text("正在为您生成更美观的统计图，请稍候...")
 
-    font_prop = get_chinese_font()
-    main_currency = get_user_main_currency(user_id)
-    with get_db_connection() as conn:
-        df = pd.read_sql_query("SELECT * FROM subscriptions WHERE user_id = ?", conn, params=(user_id,))
-    if df.empty:
-        await update.message.reply_text("您还没有任何订阅数据。")
-        return
+    def generate_chart():
+        font_prop = get_chinese_font()
+        main_currency = get_user_main_currency(user_id)
+        with get_db_connection() as conn:
+            df = pd.read_sql_query("SELECT * FROM subscriptions WHERE user_id = ?", conn, params=(user_id,))
+            cursor = conn.cursor()
+            cursor.execute("SELECT from_currency, to_currency, rate FROM exchange_rates WHERE to_currency = ?", (main_currency.upper(),))
+            rate_cache = {(row['from_currency'], row['to_currency']): row['rate'] for row in cursor.fetchall()}
             
-    df['converted_cost'] = df.apply(lambda row: convert_currency(row['cost'], row['currency'], main_currency), axis=1)
-    unit_to_days = {'day': 1, 'week': 7, 'month': 30.4375, 'year': 365.25}
+        if df.empty:
+            return False, "您还没有任何订阅数据。"
 
-    def normalize_to_monthly(row):
-        if pd.isna(row['frequency_unit']) or pd.isna(row['frequency_value']) or row['frequency_value'] == 0:
-            return 0
-        total_days = row['frequency_value'] * unit_to_days.get(row['frequency_unit'], 0)
-        if total_days == 0:
-            return 0
-        return (row['converted_cost'] / total_days) * 30.4375
+        def fast_convert(amount, from_curr, to_curr):
+            if from_curr.upper() == to_curr.upper():
+                return amount
+            cached_rate = rate_cache.get((from_curr.upper(), to_curr.upper()))
+            if cached_rate is not None:
+                return amount * cached_rate
+            return convert_currency(amount, from_curr, to_curr)
 
-    df['monthly_cost'] = df.apply(normalize_to_monthly, axis=1)
-    category_costs = df.groupby('category')['monthly_cost'].sum().sort_values(ascending=False)
+        df['converted_cost'] = df.apply(lambda row: fast_convert(row['cost'], row['currency'], main_currency), axis=1)
+        unit_to_days = {'day': 1, 'week': 7, 'month': 30.4375, 'year': 365.25}
 
-    if category_costs.empty or category_costs.sum() == 0:
-        await update.message.reply_text("您的订阅没有有效的费用信息。")
-        return
+        def normalize_to_monthly(row):
+            if pd.isna(row['frequency_unit']) or pd.isna(row['frequency_value']) or row['frequency_value'] == 0:
+                return 0
+            total_days = row['frequency_value'] * unit_to_days.get(row['frequency_unit'], 0)
+            if total_days == 0:
+                return 0
+            return (row['converted_cost'] / total_days) * 30.4375
 
-    max_categories = 8
-    if len(category_costs) > max_categories:
-        top = category_costs.iloc[:max_categories]
-        others_sum = category_costs.iloc[max_categories:].sum()
-        if others_sum > 0:
-            category_costs = pd.concat([top, pd.Series({'其他': others_sum})])
-        else:
-            category_costs = top
+        df['monthly_cost'] = df.apply(normalize_to_monthly, axis=1)
+        category_costs = df.groupby('category')['monthly_cost'].sum().sort_values(ascending=False)
 
-    total_monthly = category_costs.sum()
-    currency_symbols = {'USD': '$', 'CNY': '¥', 'EUR': '€', 'GBP': '£', 'JPY': '¥'}
-    symbol = currency_symbols.get(main_currency.upper(), f'{main_currency.upper()} ')
+        if category_costs.empty or category_costs.sum() == 0:
+            return False, "您的订阅没有有效的费用信息。"
 
-    def autopct_if_large(pct):
-        if pct < 4:
-            return ''
-        value = total_monthly * pct / 100
-        return f"{pct:.1f}%\\n{symbol}{value:.2f}"
+        max_categories = 8
+        if len(category_costs) > max_categories:
+            top = category_costs.iloc[:max_categories]
+            others_sum = category_costs.iloc[max_categories:].sum()
+            if others_sum > 0:
+                category_costs = pd.concat([top, pd.Series({'其他': others_sum})])
+            else:
+                category_costs = top
 
-    # Setup figure with a clean, modern background
-    fig = plt.figure(figsize=(15, 8.5), facecolor='#FAFAFA')
-    gs = fig.add_gridspec(1, 2, width_ratios=[1.1, 1], wspace=0.15)
-    ax_pie = fig.add_subplot(gs[0, 0])
-    ax_bar = fig.add_subplot(gs[0, 1])
-    image_path = None
+        total_monthly = category_costs.sum()
+        currency_symbols = {'USD': '$', 'CNY': '¥', 'EUR': '€', 'GBP': '£', 'JPY': '¥'}
+        symbol = currency_symbols.get(main_currency.upper(), f'{main_currency.upper()} ')
 
-    try:
-        # Modern color palette (Tailwind-inspired)
-        theme_colors = ['#3B82F6', '#10B981', '#F59E0B', '#EF4444', '#8B5CF6', '#EC4899', '#14B8A6', '#F97316', '#6366F1', '#84CC16']
-        if len(category_costs) > len(theme_colors):
-            import matplotlib.pyplot as plt
-            extra_colors = [matplotlib.colors.to_hex(c) for c in plt.get_cmap('tab20').colors]
-            theme_colors.extend(extra_colors)
+        def autopct_if_large(pct):
+            if pct < 4:
+                return ''
+            value = total_monthly * pct / 100
+            return f"{pct:.1f}%\n{symbol}{value:.2f}"
 
-        color_map = {cat: theme_colors[i] for i, cat in enumerate(category_costs.index)}
-        pie_colors = [color_map[cat] for cat in category_costs.index]
+        fig = plt.figure(figsize=(15, 8.5), facecolor='#FAFAFA')
+        gs = fig.add_gridspec(1, 2, width_ratios=[1.1, 1], wspace=0.15)
+        ax_pie = fig.add_subplot(gs[0, 0])
+        ax_bar = fig.add_subplot(gs[0, 1])
+        image_path = None
 
-        # Enhanced Donut Chart
-        wedges, texts, autotexts = ax_pie.pie(
-            category_costs.values,
-            labels=category_costs.index,
-            autopct=autopct_if_large,
-            startangle=140,
-            counterclock=False,
-            pctdistance=0.75,
-            labeldistance=1.1,
-            colors=pie_colors,
-            wedgeprops={'width': 0.35, 'edgecolor': '#FAFAFA', 'linewidth': 2.5}
-        )
+        try:
+            theme_colors = ['#3B82F6', '#10B981', '#F59E0B', '#EF4444', '#8B5CF6', '#EC4899', '#14B8A6', '#F97316', '#6366F1', '#84CC16']
+            if len(category_costs) > len(theme_colors):
+                import matplotlib.pyplot as plt
+                extra_colors = [matplotlib.colors.to_hex(c) for c in plt.get_cmap('tab20').colors]
+                theme_colors.extend(extra_colors)
                 
-        for t in texts:
-            t.set_fontproperties(font_prop)
-            t.set_fontsize(13)
-            t.set_color('#374151')
-        for t in autotexts:
-            t.set_fontproperties(font_prop)
-            t.set_fontsize(10)
-            t.set_color('#FFFFFF')
-            t.set_weight('bold')
+            color_map = {cat: theme_colors[i] for i, cat in enumerate(category_costs.index)}
+            pie_colors = [color_map[cat] for cat in category_costs.index]
 
-        # Center text for donut
-        ax_pie.text(
-            0, 0,
-            f"月总支出\n{symbol}{total_monthly:.2f}",
-            ha='center', va='center',
-            fontproperties=font_prop,
-            fontsize=18,
-            color='#1F2937',
-            weight='bold'
-        )
-        ax_pie.set_title('支出占比结构', fontproperties=font_prop, fontsize=18, pad=20, color='#111827', weight='bold')
-        ax_pie.axis('equal')
-
-        # Enhanced Bar Chart
-        bar_series = category_costs.sort_values(ascending=True)
-        bar_colors = [color_map[cat] for cat in bar_series.index]
-        
-        bars = ax_bar.barh(bar_series.index, bar_series.values, color=bar_colors, height=0.6, alpha=0.95, edgecolor='none')
-        
-        ax_bar.set_title('各类别月支出对比', fontproperties=font_prop, fontsize=18, pad=20, color='#111827', weight='bold')
-        ax_bar.set_xlabel(f'金额（{main_currency.upper()}）', fontproperties=font_prop, fontsize=12, color='#6B7280', labelpad=10)
-        
-        # Clean up axes
-        ax_bar.spines['top'].set_visible(False)
-        ax_bar.spines['right'].set_visible(False)
-        ax_bar.spines['left'].set_visible(False)
-        ax_bar.spines['bottom'].set_color('#E5E7EB')
-        
-        ax_bar.tick_params(axis='x', colors='#6B7280', labelsize=11)
-        ax_bar.tick_params(axis='y', length=0, pad=10) # Hide y ticks but keep labels
-        
-        ax_bar.grid(axis='x', color='#F3F4F6', linestyle='-', linewidth=1.5, alpha=1)
-        ax_bar.set_axisbelow(True)
-
-        for label in ax_bar.get_yticklabels():
-            label.set_fontproperties(font_prop)
-            label.set_fontsize(13)
-            label.set_color('#374151')
-
-        # Bar value labels
-        max_val = bar_series.max() if len(bar_series) else 0
-        offset = max_val * 0.02 if max_val > 0 else 0.1
-        for rect, value in zip(bars, bar_series.values):
-            ax_bar.text(
-                rect.get_width() + offset,
-                rect.get_y() + rect.get_height() / 2,
-                f"{symbol}{value:.2f}",
-                va='center',
-                ha='left',
-                fontproperties=font_prop,
-                fontsize=11,
-                color='#4B5563',
-                weight='bold'
+            wedges, texts, autotexts = ax_pie.pie(
+                category_costs.values,
+                labels=category_costs.index,
+                autopct=autopct_if_large,
+                startangle=140,
+                counterclock=False,
+                pctdistance=0.75,
+                labeldistance=1.1,
+                colors=pie_colors,
+                wedgeprops={'width': 0.35, 'edgecolor': '#FAFAFA', 'linewidth': 2.5}
             )
 
-        fig.suptitle('📊 您的订阅支出洞察', fontproperties=font_prop, fontsize=24, color='#0F172A', y=1.02, weight='bold')
-        fig.tight_layout(rect=[0, 0, 1, 0.95])
+            for t in texts:
+                t.set_fontproperties(font_prop)
+                t.set_fontsize(13)
+                t.set_color('#374151')
+            for t in autotexts:
+                t.set_fontproperties(font_prop)
+                t.set_fontsize(10)
+                t.set_color('#FFFFFF')
+                t.set_weight('bold')
 
-        with tempfile.NamedTemporaryFile(prefix=f'stats_{user_id}_', suffix='.png', delete=False) as tmp:
-            image_path = tmp.name
+            ax_pie.text(
+                0, 0,
+                f"月总支出\n{symbol}{total_monthly:.2f}",
+                ha='center', va='center',
+                fontproperties=font_prop,
+                fontsize=18,
+                color='#1F2937',
+                weight='bold'
+            )
+            ax_pie.set_title('支出占比结构', fontproperties=font_prop, fontsize=18, pad=20, color='#111827', weight='bold')
+            ax_pie.axis('equal')
 
-        plt.savefig(image_path, dpi=250, bbox_inches='tight', facecolor=fig.get_facecolor())
+            bar_series = category_costs.sort_values(ascending=True)
+            bar_colors = [color_map[cat] for cat in bar_series.index]
             
-        with open(image_path, 'rb') as photo:
-            await update.message.reply_photo(photo, caption="✨ 已为您生成全新的精美订阅统计图！")
-    finally:
-        plt.close(fig)
-        if image_path and os.path.exists(image_path):
-            os.remove(image_path)
+            bars = ax_bar.barh(bar_series.index, bar_series.values, color=bar_colors, height=0.6, alpha=0.95, edgecolor='none')
+            
+            ax_bar.set_title('各类别月支出对比', fontproperties=font_prop, fontsize=18, pad=20, color='#111827', weight='bold')
+            ax_bar.set_xlabel(f'金额（{main_currency.upper()}）', fontproperties=font_prop, fontsize=12, color='#6B7280', labelpad=10)
+            
+            ax_bar.spines['top'].set_visible(False)
+            ax_bar.spines['right'].set_visible(False)
+            ax_bar.spines['left'].set_visible(False)
+            ax_bar.spines['bottom'].set_color('#E5E7EB')
+            
+            ax_bar.tick_params(axis='x', colors='#6B7280', labelsize=11)
+            ax_bar.tick_params(axis='y', length=0, pad=10) 
+            
+            ax_bar.grid(axis='x', color='#F3F4F6', linestyle='-', linewidth=1.5, alpha=1)
+            ax_bar.set_axisbelow(True)
+
+            for label in ax_bar.get_yticklabels():
+                label.set_fontproperties(font_prop)
+                label.set_fontsize(13)
+                label.set_color('#374151')
+
+            max_val = bar_series.max() if len(bar_series) else 0
+            offset = max_val * 0.02 if max_val > 0 else 0.1
+            for rect, value in zip(bars, bar_series.values):
+                ax_bar.text(
+                    rect.get_width() + offset,
+                    rect.get_y() + rect.get_height() / 2,
+                    f"{symbol}{value:.2f}",
+                    va='center',
+                    ha='left',
+                    fontproperties=font_prop,
+                    fontsize=11,
+                    color='#4B5563',
+                    weight='bold'
+                )
+
+            fig.suptitle('📊 您的订阅支出洞察', fontproperties=font_prop, fontsize=24, color='#0F172A', y=1.02, weight='bold')
+            fig.tight_layout(rect=[0, 0, 1, 0.95])
+
+            with tempfile.NamedTemporaryFile(prefix=f'stats_{user_id}_', suffix='.png', delete=False) as tmp:
+                image_path = tmp.name
+
+            plt.savefig(image_path, dpi=250, bbox_inches='tight', facecolor=fig.get_facecolor())
+            return True, image_path
+        finally:
+            plt.close(fig)
+
+    success, result = await asyncio.to_thread(generate_chart)
+    
+    if success:
+        try:
+            with open(result, 'rb') as photo:
+                await update.message.reply_photo(photo, caption="✨ 已为您生成全新的精美订阅统计图！")
+        finally:
+            if os.path.exists(result):
+                os.remove(result)
+    else:
+        await update.message.reply_text(result)
 
 
 
 # --- Import/Export Commands ---
 async def export_command(update: Update, context: CallbackContext):
     user_id = update.effective_user.id
-    with get_db_connection() as conn:
-        df = pd.read_sql_query(
-            "SELECT name, cost, currency, category, next_due, frequency_unit, frequency_value, renewal_type, notes FROM subscriptions WHERE user_id = ?",
-            conn, params=(user_id,))
-    if df.empty:
+    
+    # 将重度 I/O 和 CPU 绑定的 pandas 导出操作放入后台线程
+    def process_export():
+        with get_db_connection() as conn:
+            df = pd.read_sql_query(
+                "SELECT name, cost, currency, category, next_due, frequency_unit, frequency_value, renewal_type, notes FROM subscriptions WHERE user_id = ?",
+                conn, params=(user_id,))
+        if df.empty:
+            return False, None
+        
+        tmp = tempfile.NamedTemporaryFile(prefix=f'export_{user_id}_', suffix='.csv', delete=False)
+        export_path = tmp.name
+        tmp.close()
+        
+        df.to_csv(export_path, index=False, encoding='utf-8-sig')
+        return True, export_path
+
+    success, export_path = await asyncio.to_thread(process_export)
+    
+    if not success:
         await update.message.reply_text("您还没有任何订阅数据，无法导出。")
         return
 
-    with tempfile.NamedTemporaryFile(prefix=f'export_{user_id}_', suffix='.csv', delete=False) as tmp:
-        export_path = tmp.name
-
     try:
-        df.to_csv(export_path, index=False, encoding='utf-8-sig')
-
         with open(export_path, 'rb') as file:
             await update.message.reply_document(document=file, filename='subscriptions.csv',
                                                 caption="您的订阅数据已导出为 CSV 文件。")
     finally:
-        if os.path.exists(export_path):
+        if export_path and os.path.exists(export_path):
             os.remove(export_path)