临时保存1

src/flask_prompt_master/admin/views/analytics_admin.py

@@ -11,6 +11,7 @@ from datetime import datetime, timedelta
 import plotly.graph_objs as go
 import plotly.utils
 import json
+from src.flask_prompt_master.utils.performance_monitor import monitor_performance
 
 class AnalyticsAdminView(BaseView):
     """数据分析管理视图"""
@@ -39,50 +40,53 @@ class AnalyticsAdminView(BaseView):
         
         return self.render('admin/analytics_charts.html', charts_data=charts_data)
     
+    @monitor_performance
     def _get_analytics_data(self):
-        """获取分析数据"""
+        """获取分析数据 - 优化版本"""
         try:
-            # 用户统计
-            total_users = User.query.count()
-            active_users = User.query.filter_by(status=1).count()
-            new_users_today = User.query.filter(
-                User.created_time >= datetime.now().date()
-            ).count()
-            new_users_week = User.query.filter(
-                User.created_time >= datetime.now() - timedelta(days=7)
-            ).count()
+            today = datetime.now().date()
+            week_ago = datetime.now() - timedelta(days=7)
             
-            # 提示词统计
-            total_prompts = Prompt.query.count()
-            today_prompts = Prompt.query.filter(
-                Prompt.created_at >= datetime.now().date()
-            ).count()
-            week_prompts = Prompt.query.filter(
-                Prompt.created_at >= datetime.now() - timedelta(days=7)
-            ).count()
+            # 使用单个查询获取所有用户统计
+            user_stats = db.session.query(
+                func.count(User.uid).label('total_users'),
+                func.sum(func.case([(User.status == 1, 1)], else_=0)).label('active_users'),
+                func.sum(func.case([(User.created_time >= today, 1)], else_=0)).label('new_users_today'),
+                func.sum(func.case([(User.created_time >= week_ago, 1)], else_=0)).label('new_users_week')
+            ).first()
+            
+            # 使用单个查询获取所有提示词统计
+            prompt_stats = db.session.query(
+                func.count(Prompt.id).label('total_prompts'),
+                func.sum(func.case([(Prompt.created_at >= today, 1)], else_=0)).label('today_prompts'),
+                func.sum(func.case([(Prompt.created_at >= week_ago, 1)], else_=0)).label('week_prompts')
+            ).first()
             
             # 模板统计
-            total_templates = PromptTemplate.query.count()
-            default_templates = PromptTemplate.query.filter_by(is_default=True).count()
+            template_stats = db.session.query(
+                func.count(PromptTemplate.id).label('total_templates'),
+                func.sum(func.case([(PromptTemplate.is_default == True, 1)], else_=0)).label('default_templates')
+            ).first()
             
-            # 用户活跃度
-            active_users_week = db.session.query(func.count(func.distinct(Prompt.user_id))).filter(
-                Prompt.created_at >= datetime.now() - timedelta(days=7)
-            ).scalar()
+            # 用户活跃度 - 优化查询
+            active_users_week = db.session.query(
+                func.count(func.distinct(Prompt.user_id))
+            ).filter(Prompt.created_at >= week_ago).scalar() or 0
             
             return {
-                'total_users': total_users,
-                'active_users': active_users,
-                'new_users_today': new_users_today,
-                'new_users_week': new_users_week,
-                'total_prompts': total_prompts,
-                'today_prompts': today_prompts,
-                'week_prompts': week_prompts,
-                'total_templates': total_templates,
-                'default_templates': default_templates,
-                'active_users_week': active_users_week or 0
+                'total_users': user_stats.total_users or 0,
+                'active_users': user_stats.active_users or 0,
+                'new_users_today': user_stats.new_users_today or 0,
+                'new_users_week': user_stats.new_users_week or 0,
+                'total_prompts': prompt_stats.total_prompts or 0,
+                'today_prompts': prompt_stats.today_prompts or 0,
+                'week_prompts': prompt_stats.week_prompts or 0,
+                'total_templates': template_stats.total_templates or 0,
+                'default_templates': template_stats.default_templates or 0,
+                'active_users_week': active_users_week
             }
         except Exception as e:
+            print(f"获取分析数据失败: {str(e)}")
             return {
                 'total_users': 0,
                 'active_users': 0,

src/flask_prompt_master/admin/views/api_admin.py

@@ -110,18 +110,26 @@ class ApiAdminView(BaseView):
             # 成功率（模拟数据）
             success_rate = 98.5  # 百分比
             
-            # 最近7天的调用趋势
+            # 最近7天的调用趋势 - 优化版本
+            week_ago = datetime.now() - timedelta(days=7)
+            daily_calls_data = db.session.query(
+                func.date(Prompt.created_at).label('date'),
+                func.count(Prompt.id).label('count')
+            ).filter(
+                Prompt.created_at >= week_ago
+            ).group_by(
+                func.date(Prompt.created_at)
+            ).order_by(func.date(Prompt.created_at)).all()
+            
+            # 构建完整的7天数据
             daily_calls = []
             for i in range(7):
-                date = datetime.now() - timedelta(days=i)
-                count = Prompt.query.filter(
-                    func.date(Prompt.created_at) == date.date()
-                ).count()
+                date = (datetime.now() - timedelta(days=6-i)).date()
+                count = next((item.count for item in daily_calls_data if item.date == date), 0)
                 daily_calls.append({
                     'date': date.strftime('%Y-%m-%d'),
                     'count': count
                 })
-            daily_calls.reverse()
             
             return {
                 'today_calls': today_calls,

src/flask_prompt_master/templates/admin/analytics_dashboard.html

@@ -269,8 +269,11 @@
 
 <script>
 document.addEventListener('DOMContentLoaded', function() {
-    // 初始化图表
-    initCharts();
+    // 延迟初始化，避免阻塞页面渲染
+    setTimeout(function() {
+        // 初始化图表
+        initCharts();
+    }, 100);
     
     // 数字动画
     const statsNumbers = document.querySelectorAll('.stats-number');

src/flask_prompt_master/utils/cache_utils.py

@@ -0,0 +1,67 @@
+# -*- coding: utf-8 -*-
+"""
+缓存工具类
+用于缓存频繁查询的数据，提升页面加载性能
+"""
+import json
+import hashlib
+from datetime import datetime, timedelta
+from functools import wraps
+from flask import current_app
+
+# 简单的内存缓存实现
+_cache = {}
+_cache_expiry = {}
+
+def cache_key(*args, **kwargs):
+    """生成缓存键"""
+    key_str = json.dumps({'args': args, 'kwargs': kwargs}, sort_keys=True)
+    return hashlib.md5(key_str.encode()).hexdigest()
+
+def cached(expiry_seconds=300):
+    """
+    缓存装饰器
+    :param expiry_seconds: 缓存过期时间（秒）
+    """
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            key = f"{func.__name__}:{cache_key(*args, **kwargs)}"
+            
+            # 检查缓存是否有效
+            if key in _cache and key in _cache_expiry:
+                if datetime.now() < _cache_expiry[key]:
+                    return _cache[key]
+            
+            # 执行函数并缓存结果
+            result = func(*args, **kwargs)
+            _cache[key] = result
+            _cache_expiry[key] = datetime.now() + timedelta(seconds=expiry_seconds)
+            
+            return result
+        return wrapper
+    return decorator
+
+def clear_cache(pattern=None):
+    """
+    清除缓存
+    :param pattern: 缓存键模式，如果为None则清除所有缓存
+    """
+    global _cache, _cache_expiry
+    
+    if pattern is None:
+        _cache.clear()
+        _cache_expiry.clear()
+    else:
+        keys_to_remove = [key for key in _cache.keys() if pattern in key]
+        for key in keys_to_remove:
+            _cache.pop(key, None)
+            _cache_expiry.pop(key, None)
+
+def get_cache_stats():
+    """获取缓存统计信息"""
+    return {
+        'total_keys': len(_cache),
+        'expired_keys': len([k for k, v in _cache_expiry.items() if datetime.now() > v]),
+        'cache_size': sum(len(str(v)) for v in _cache.values())
+    }

src/flask_prompt_master/utils/performance_monitor.py

@@ -0,0 +1,88 @@
+# -*- coding: utf-8 -*-
+"""
+性能监控工具
+用于监控页面加载时间和数据库查询性能
+"""
+import time
+import functools
+from flask import request, g
+from sqlalchemy import event
+from sqlalchemy.engine import Engine
+import logging
+
+logger = logging.getLogger(__name__)
+
+# 查询时间统计
+query_times = []
+
+@event.listens_for(Engine, "before_cursor_execute")
+def receive_before_cursor_execute(conn, cursor, statement, parameters, context, executemany):
+    """记录SQL查询开始时间"""
+    context._query_start_time = time.time()
+
+@event.listens_for(Engine, "after_cursor_execute")
+def receive_after_cursor_execute(conn, cursor, statement, parameters, context, executemany):
+    """记录SQL查询结束时间"""
+    total = time.time() - context._query_start_time
+    query_times.append({
+        'statement': statement,
+        'duration': total,
+        'timestamp': time.time()
+    })
+    
+    # 记录慢查询
+    if total > 1.0:  # 超过1秒的查询
+        logger.warning(f"慢查询检测: {total:.2f}s - {statement[:100]}...")
+
+def monitor_performance(func):
+    """性能监控装饰器"""
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        start_time = time.time()
+        
+        # 清空查询时间记录
+        global query_times
+        query_times.clear()
+        
+        try:
+            result = func(*args, **kwargs)
+            return result
+        finally:
+            end_time = time.time()
+            duration = end_time - start_time
+            
+            # 记录性能数据
+            performance_data = {
+                'function': func.__name__,
+                'duration': duration,
+                'query_count': len(query_times),
+                'total_query_time': sum(q['duration'] for q in query_times),
+                'slow_queries': [q for q in query_times if q['duration'] > 0.5]
+            }
+            
+            # 记录到日志
+            logger.info(f"性能监控 - {func.__name__}: {duration:.2f}s, 查询数: {len(query_times)}")
+            
+            # 如果性能较差，记录详细信息
+            if duration > 2.0 or len(query_times) > 10:
+                logger.warning(f"性能警告 - {func.__name__}: {performance_data}")
+    
+    return wrapper
+
+def get_performance_stats():
+    """获取性能统计信息"""
+    if not query_times:
+        return {'message': '暂无查询数据'}
+    
+    total_queries = len(query_times)
+    total_time = sum(q['duration'] for q in query_times)
+    avg_time = total_time / total_queries if total_queries > 0 else 0
+    slow_queries = [q for q in query_times if q['duration'] > 0.5]
+    
+    return {
+        'total_queries': total_queries,
+        'total_time': round(total_time, 2),
+        'average_time': round(avg_time, 2),
+        'slow_queries_count': len(slow_queries),
+        'slow_queries': slow_queries[:5]  # 只返回前5个慢查询
+    }