perf: offload heavy blocking I/O (matplotlib, pandas) to asyncio threads
This commit is contained in:
Xiaolan Bot
331
SubMind.py
331
SubMind.py
@@ -1,4 +1,5 @@
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import sqlite3
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import sqlite3
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import asyncio
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import os
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import os
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import html
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import html
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import requests
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import requests
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@@ -447,186 +448,208 @@ async def stats(update: Update, context: CallbackContext):
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user_id = update.effective_user.id
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user_id = update.effective_user.id
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await update.message.reply_text("正在为您生成更美观的统计图，请稍候...")
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await update.message.reply_text("正在为您生成更美观的统计图，请稍候...")
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font_prop = get_chinese_font()
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def generate_chart():
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main_currency = get_user_main_currency(user_id)
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font_prop = get_chinese_font()
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with get_db_connection() as conn:
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main_currency = get_user_main_currency(user_id)
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df = pd.read_sql_query("SELECT * FROM subscriptions WHERE user_id = ?", conn, params=(user_id,))
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with get_db_connection() as conn:
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if df.empty:
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df = pd.read_sql_query("SELECT * FROM subscriptions WHERE user_id = ?", conn, params=(user_id,))
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await update.message.reply_text("您还没有任何订阅数据。")
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cursor = conn.cursor()
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return
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cursor.execute("SELECT from_currency, to_currency, rate FROM exchange_rates WHERE to_currency = ?", (main_currency.upper(),))
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rate_cache = {(row['from_currency'], row['to_currency']): row['rate'] for row in cursor.fetchall()}
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df['converted_cost'] = df.apply(lambda row: convert_currency(row['cost'], row['currency'], main_currency), axis=1)
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unit_to_days = {'day': 1, 'week': 7, 'month': 30.4375, 'year': 365.25}
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def normalize_to_monthly(row):
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if pd.isna(row['frequency_unit']) or pd.isna(row['frequency_value']) or row['frequency_value'] == 0:
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return 0
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total_days = row['frequency_value'] * unit_to_days.get(row['frequency_unit'], 0)
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if total_days == 0:
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return 0
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return (row['converted_cost'] / total_days) * 30.4375
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df['monthly_cost'] = df.apply(normalize_to_monthly, axis=1)
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category_costs = df.groupby('category')['monthly_cost'].sum().sort_values(ascending=False)
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if category_costs.empty or category_costs.sum() == 0:
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await update.message.reply_text("您的订阅没有有效的费用信息。")
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return
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max_categories = 8
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if len(category_costs) > max_categories:
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top = category_costs.iloc[:max_categories]
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others_sum = category_costs.iloc[max_categories:].sum()
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if others_sum > 0:
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category_costs = pd.concat([top, pd.Series({'其他': others_sum})])
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else:
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category_costs = top
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total_monthly = category_costs.sum()
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currency_symbols = {'USD': '$', 'CNY': '¥', 'EUR': '€', 'GBP': '£', 'JPY': '¥'}
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symbol = currency_symbols.get(main_currency.upper(), f'{main_currency.upper()} ')
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def autopct_if_large(pct):
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if pct < 4:
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return ''
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value = total_monthly * pct / 100
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return f"{pct:.1f}%\\n{symbol}{value:.2f}"
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# Setup figure with a clean, modern background
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fig = plt.figure(figsize=(15, 8.5), facecolor='#FAFAFA')
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gs = fig.add_gridspec(1, 2, width_ratios=[1.1, 1], wspace=0.15)
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ax_pie = fig.add_subplot(gs[0, 0])
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ax_bar = fig.add_subplot(gs[0, 1])
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image_path = None
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try:
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# Modern color palette (Tailwind-inspired)
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theme_colors = ['#3B82F6', '#10B981', '#F59E0B', '#EF4444', '#8B5CF6', '#EC4899', '#14B8A6', '#F97316', '#6366F1', '#84CC16']
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if len(category_costs) > len(theme_colors):
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import matplotlib.pyplot as plt
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extra_colors = [matplotlib.colors.to_hex(c) for c in plt.get_cmap('tab20').colors]
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theme_colors.extend(extra_colors)
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color_map = {cat: theme_colors[i] for i, cat in enumerate(category_costs.index)}
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if df.empty:
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pie_colors = [color_map[cat] for cat in category_costs.index]
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return False, "您还没有任何订阅数据。"
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# Enhanced Donut Chart
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def fast_convert(amount, from_curr, to_curr):
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wedges, texts, autotexts = ax_pie.pie(
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if from_curr.upper() == to_curr.upper():
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category_costs.values,
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return amount
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labels=category_costs.index,
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cached_rate = rate_cache.get((from_curr.upper(), to_curr.upper()))
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autopct=autopct_if_large,
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if cached_rate is not None:
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startangle=140,
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return amount * cached_rate
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counterclock=False,
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return convert_currency(amount, from_curr, to_curr)
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pctdistance=0.75,
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labeldistance=1.1,
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colors=pie_colors,
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wedgeprops={'width': 0.35, 'edgecolor': '#FAFAFA', 'linewidth': 2.5}
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)
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for t in texts:
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df['converted_cost'] = df.apply(lambda row: fast_convert(row['cost'], row['currency'], main_currency), axis=1)
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t.set_fontproperties(font_prop)
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unit_to_days = {'day': 1, 'week': 7, 'month': 30.4375, 'year': 365.25}
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t.set_fontsize(13)
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t.set_color('#374151')
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for t in autotexts:
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t.set_fontproperties(font_prop)
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t.set_fontsize(10)
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t.set_color('#FFFFFF')
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t.set_weight('bold')
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# Center text for donut
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def normalize_to_monthly(row):
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ax_pie.text(
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if pd.isna(row['frequency_unit']) or pd.isna(row['frequency_value']) or row['frequency_value'] == 0:
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0, 0,
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return 0
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f"月总支出\n{symbol}{total_monthly:.2f}",
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total_days = row['frequency_value'] * unit_to_days.get(row['frequency_unit'], 0)
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ha='center', va='center',
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if total_days == 0:
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fontproperties=font_prop,
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return 0
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fontsize=18,
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return (row['converted_cost'] / total_days) * 30.4375
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color='#1F2937',
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weight='bold'
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)
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ax_pie.set_title('支出占比结构', fontproperties=font_prop, fontsize=18, pad=20, color='#111827', weight='bold')
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ax_pie.axis('equal')
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# Enhanced Bar Chart
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df['monthly_cost'] = df.apply(normalize_to_monthly, axis=1)
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bar_series = category_costs.sort_values(ascending=True)
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category_costs = df.groupby('category')['monthly_cost'].sum().sort_values(ascending=False)
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bar_colors = [color_map[cat] for cat in bar_series.index]
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bars = ax_bar.barh(bar_series.index, bar_series.values, color=bar_colors, height=0.6, alpha=0.95, edgecolor='none')
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ax_bar.set_title('各类别月支出对比', fontproperties=font_prop, fontsize=18, pad=20, color='#111827', weight='bold')
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ax_bar.set_xlabel(f'金额（{main_currency.upper()}）', fontproperties=font_prop, fontsize=12, color='#6B7280', labelpad=10)
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# Clean up axes
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ax_bar.spines['top'].set_visible(False)
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ax_bar.spines['right'].set_visible(False)
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ax_bar.spines['left'].set_visible(False)
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ax_bar.spines['bottom'].set_color('#E5E7EB')
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ax_bar.tick_params(axis='x', colors='#6B7280', labelsize=11)
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ax_bar.tick_params(axis='y', length=0, pad=10) # Hide y ticks but keep labels
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ax_bar.grid(axis='x', color='#F3F4F6', linestyle='-', linewidth=1.5, alpha=1)
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ax_bar.set_axisbelow(True)
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for label in ax_bar.get_yticklabels():
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if category_costs.empty or category_costs.sum() == 0:
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label.set_fontproperties(font_prop)
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return False, "您的订阅没有有效的费用信息。"
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label.set_fontsize(13)
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label.set_color('#374151')
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# Bar value labels
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max_categories = 8
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max_val = bar_series.max() if len(bar_series) else 0
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if len(category_costs) > max_categories:
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offset = max_val * 0.02 if max_val > 0 else 0.1
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top = category_costs.iloc[:max_categories]
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for rect, value in zip(bars, bar_series.values):
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others_sum = category_costs.iloc[max_categories:].sum()
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ax_bar.text(
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if others_sum > 0:
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rect.get_width() + offset,
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category_costs = pd.concat([top, pd.Series({'其他': others_sum})])
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rect.get_y() + rect.get_height() / 2,
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else:
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f"{symbol}{value:.2f}",
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category_costs = top
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va='center',
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ha='left',
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total_monthly = category_costs.sum()
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fontproperties=font_prop,
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currency_symbols = {'USD': '$', 'CNY': '¥', 'EUR': '€', 'GBP': '£', 'JPY': '¥'}
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fontsize=11,
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symbol = currency_symbols.get(main_currency.upper(), f'{main_currency.upper()} ')
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color='#4B5563',
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weight='bold'
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def autopct_if_large(pct):
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if pct < 4:
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return ''
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value = total_monthly * pct / 100
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return f"{pct:.1f}%\n{symbol}{value:.2f}"
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fig = plt.figure(figsize=(15, 8.5), facecolor='#FAFAFA')
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gs = fig.add_gridspec(1, 2, width_ratios=[1.1, 1], wspace=0.15)
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ax_pie = fig.add_subplot(gs[0, 0])
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ax_bar = fig.add_subplot(gs[0, 1])
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image_path = None
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try:
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theme_colors = ['#3B82F6', '#10B981', '#F59E0B', '#EF4444', '#8B5CF6', '#EC4899', '#14B8A6', '#F97316', '#6366F1', '#84CC16']
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if len(category_costs) > len(theme_colors):
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import matplotlib.pyplot as plt
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extra_colors = [matplotlib.colors.to_hex(c) for c in plt.get_cmap('tab20').colors]
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theme_colors.extend(extra_colors)
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color_map = {cat: theme_colors[i] for i, cat in enumerate(category_costs.index)}
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pie_colors = [color_map[cat] for cat in category_costs.index]
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wedges, texts, autotexts = ax_pie.pie(
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category_costs.values,
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labels=category_costs.index,
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autopct=autopct_if_large,
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startangle=140,
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counterclock=False,
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pctdistance=0.75,
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labeldistance=1.1,
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colors=pie_colors,
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wedgeprops={'width': 0.35, 'edgecolor': '#FAFAFA', 'linewidth': 2.5}
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)
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)
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fig.suptitle('📊 您的订阅支出洞察', fontproperties=font_prop, fontsize=24, color='#0F172A', y=1.02, weight='bold')
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for t in texts:
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fig.tight_layout(rect=[0, 0, 1, 0.95])
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t.set_fontproperties(font_prop)
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t.set_fontsize(13)
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t.set_color('#374151')
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for t in autotexts:
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t.set_fontproperties(font_prop)
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t.set_fontsize(10)
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t.set_color('#FFFFFF')
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t.set_weight('bold')
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with tempfile.NamedTemporaryFile(prefix=f'stats_{user_id}_', suffix='.png', delete=False) as tmp:
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ax_pie.text(
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image_path = tmp.name
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0, 0,
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f"月总支出\n{symbol}{total_monthly:.2f}",
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ha='center', va='center',
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fontproperties=font_prop,
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fontsize=18,
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color='#1F2937',
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weight='bold'
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)
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ax_pie.set_title('支出占比结构', fontproperties=font_prop, fontsize=18, pad=20, color='#111827', weight='bold')
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ax_pie.axis('equal')
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plt.savefig(image_path, dpi=250, bbox_inches='tight', facecolor=fig.get_facecolor())
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bar_series = category_costs.sort_values(ascending=True)
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bar_colors = [color_map[cat] for cat in bar_series.index]
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bars = ax_bar.barh(bar_series.index, bar_series.values, color=bar_colors, height=0.6, alpha=0.95, edgecolor='none')
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ax_bar.set_title('各类别月支出对比', fontproperties=font_prop, fontsize=18, pad=20, color='#111827', weight='bold')
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ax_bar.set_xlabel(f'金额（{main_currency.upper()}）', fontproperties=font_prop, fontsize=12, color='#6B7280', labelpad=10)
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ax_bar.spines['top'].set_visible(False)
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ax_bar.spines['right'].set_visible(False)
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ax_bar.spines['left'].set_visible(False)
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ax_bar.spines['bottom'].set_color('#E5E7EB')
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ax_bar.tick_params(axis='x', colors='#6B7280', labelsize=11)
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ax_bar.tick_params(axis='y', length=0, pad=10)
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ax_bar.grid(axis='x', color='#F3F4F6', linestyle='-', linewidth=1.5, alpha=1)
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ax_bar.set_axisbelow(True)
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with open(image_path, 'rb') as photo:
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for label in ax_bar.get_yticklabels():
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await update.message.reply_photo(photo, caption="✨ 已为您生成全新的精美订阅统计图！")
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label.set_fontproperties(font_prop)
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finally:
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label.set_fontsize(13)
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plt.close(fig)
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label.set_color('#374151')
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if image_path and os.path.exists(image_path):
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os.remove(image_path)
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max_val = bar_series.max() if len(bar_series) else 0
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offset = max_val * 0.02 if max_val > 0 else 0.1
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for rect, value in zip(bars, bar_series.values):
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ax_bar.text(
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rect.get_width() + offset,
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rect.get_y() + rect.get_height() / 2,
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f"{symbol}{value:.2f}",
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va='center',
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ha='left',
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fontproperties=font_prop,
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fontsize=11,
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color='#4B5563',
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weight='bold'
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)
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fig.suptitle('📊 您的订阅支出洞察', fontproperties=font_prop, fontsize=24, color='#0F172A', y=1.02, weight='bold')
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fig.tight_layout(rect=[0, 0, 1, 0.95])
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with tempfile.NamedTemporaryFile(prefix=f'stats_{user_id}_', suffix='.png', delete=False) as tmp:
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image_path = tmp.name
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plt.savefig(image_path, dpi=250, bbox_inches='tight', facecolor=fig.get_facecolor())
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return True, image_path
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finally:
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plt.close(fig)
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success, result = await asyncio.to_thread(generate_chart)
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if success:
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try:
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with open(result, 'rb') as photo:
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await update.message.reply_photo(photo, caption="✨ 已为您生成全新的精美订阅统计图！")
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finally:
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if os.path.exists(result):
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os.remove(result)
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else:
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await update.message.reply_text(result)
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# --- Import/Export Commands ---
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# --- Import/Export Commands ---
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async def export_command(update: Update, context: CallbackContext):
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async def export_command(update: Update, context: CallbackContext):
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user_id = update.effective_user.id
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user_id = update.effective_user.id
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with get_db_connection() as conn:
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df = pd.read_sql_query(
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# 将重度 I/O 和 CPU 绑定的 pandas 导出操作放入后台线程
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"SELECT name, cost, currency, category, next_due, frequency_unit, frequency_value, renewal_type, notes FROM subscriptions WHERE user_id = ?",
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def process_export():
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conn, params=(user_id,))
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with get_db_connection() as conn:
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if df.empty:
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df = pd.read_sql_query(
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"SELECT name, cost, currency, category, next_due, frequency_unit, frequency_value, renewal_type, notes FROM subscriptions WHERE user_id = ?",
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conn, params=(user_id,))
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if df.empty:
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return False, None
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||||||
|
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。")
|
await update.message.reply_text("您还没有任何订阅数据，无法导出。")
|
||||||
return
|
return
|
||||||
|
|
||||||
with tempfile.NamedTemporaryFile(prefix=f'export_{user_id}_', suffix='.csv', delete=False) as tmp:
|
|
||||||
export_path = tmp.name
|
|
||||||
|
|
||||||
try:
|
try:
|
||||||
df.to_csv(export_path, index=False, encoding='utf-8-sig')
|
|
||||||
|
|
||||||
with open(export_path, 'rb') as file:
|
with open(export_path, 'rb') as file:
|
||||||
await update.message.reply_document(document=file, filename='subscriptions.csv',
|
await update.message.reply_document(document=file, filename='subscriptions.csv',
|
||||||
caption="您的订阅数据已导出为 CSV 文件。")
|
caption="您的订阅数据已导出为 CSV 文件。")
|
||||||
finally:
|
finally:
|
||||||
if os.path.exists(export_path):
|
if export_path and os.path.exists(export_path):
|
||||||
os.remove(export_path)
|
os.remove(export_path)
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user