Implement a two-level caching system (memory + disk) to optimize topic switch re…

…sponse speed, support asynchronous writing, and automatically clean up expired data.

+import json
+import os
+import time
+from datetime import datetime, timedelta
+import threading
+import queue
+
+class PredictionCache:
+    _instance = None
+    _lock = threading.Lock()
+    
+    def __new__(cls):
+        with cls._lock:
+            if cls._instance is None:
+                cls._instance = super(PredictionCache, cls).__new__(cls)
+            return cls._instance
+    
+    def __init__(self):
+        if not hasattr(self, 'initialized'):
+            self.cache_dir = 'cache/predictions'
+            self.cache_duration = timedelta(hours=24)  # 缓存24小时
+            self.cache = {}
+            self.cache_queue = queue.Queue()
+            self.initialized = True
+            
+            # 确保缓存目录存在
+            os.makedirs(self.cache_dir, exist_ok=True)
+            
+            # 启动缓存清理线程
+            self.cleanup_thread = threading.Thread(target=self._cleanup_old_cache, daemon=True)
+            self.cleanup_thread.start()
+            
+            # 加载现有缓存
+            self._load_cache()
+    
+    def _load_cache(self):
+        """加载磁盘上的缓存文件"""
+        try:
+            for filename in os.listdir(self.cache_dir):
+                if filename.endswith('.json'):
+                    filepath = os.path.join(self.cache_dir, filename)
+                    with open(filepath, 'r', encoding='utf-8') as f:
+                        cache_data = json.load(f)
+                        # 检查缓存是否过期
+                        if self._is_cache_valid(cache_data['timestamp']):
+                            topic = filename[:-5]  # 移除.json后缀
+                            self.cache[topic] = cache_data
+                        else:
+                            # 删除过期缓存文件
+                            os.remove(filepath)
+        except Exception as e:
+            print(f"加载缓存失败: {e}")
+    
+    def _cleanup_old_cache(self):
+        """定期清理过期缓存的后台线程"""
+        while True:
+            try:
+                # 检查并清理内存缓存
+                current_time = datetime.now()
+                expired_topics = []
+                
+                for topic, cache_data in self.cache.items():
+                    if not self._is_cache_valid(cache_data['timestamp']):
+                        expired_topics.append(topic)
+                        
+                # 删除过期缓存
+                for topic in expired_topics:
+                    del self.cache[topic]
+                    cache_file = os.path.join(self.cache_dir, f"{topic}.json")
+                    if os.path.exists(cache_file):
+                        os.remove(cache_file)
+                
+                # 休眠1小时后再次检查
+                time.sleep(3600)
+            except Exception as e:
+                print(f"清理缓存时出错: {e}")
+                time.sleep(3600)  # 发生错误时也等待1小时
+    
+    def _is_cache_valid(self, timestamp):
+        """检查缓存是否有效"""
+        cache_time = datetime.fromtimestamp(timestamp)
+        return datetime.now() - cache_time < self.cache_duration
+    
+    def get(self, topic):
+        """获取话题的预测缓存"""
+        if topic in self.cache and self._is_cache_valid(self.cache[topic]['timestamp']):
+            return self.cache[topic]['prediction']
+        return None
+    
+    def set(self, topic, prediction):
+        """设置话题的预测缓存"""
+        cache_data = {
+            'prediction': prediction,
+            'timestamp': datetime.now().timestamp()
+        }
+        
+        # 更新内存缓存
+        self.cache[topic] = cache_data
+        
+        # 异步保存到磁盘
+        self.cache_queue.put((topic, cache_data))
+        threading.Thread(target=self._save_cache_to_disk, daemon=True).start()
+    
+    def _save_cache_to_disk(self):
+        """异步保存缓存到磁盘"""
+        try:
+            while not self.cache_queue.empty():
+                topic, cache_data = self.cache_queue.get()
+                cache_file = os.path.join(self.cache_dir, f"{topic}.json")
+                with open(cache_file, 'w', encoding='utf-8') as f:
+                    json.dump(cache_data, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            print(f"保存缓存到磁盘失败: {e}")
+
+# 创建全局缓存实例
+prediction_cache = PredictionCache() 
\ No newline at end of file

@@ -8,6 +8,7 @@ from utils.getEchartsData import *
 from utils.getTopicPageData import *
 from utils.yuqingpredict import *
 from utils.logger import app_logger as logging
+from utils.cache_manager import prediction_cache
 import torch
 from BCAT_front.predict import model_manager
 
@@ -207,24 +208,34 @@ def yuqingpredict():
         # 获取模型选择参数
         model_type = request.args.get('model', 'pro')  # 默认使用改进模型
         
-        if model_type == 'basic':
-            # 使用基础模型（SnowNLP）
-            value = SnowNLP(defaultTopic).sentiments
-            if value == 0.5:
-                sentences = '中性'
-            elif value > 0.5:
-                sentences = '正面'
-            elif value < 0.5:
-                sentences = '负面'
+        # 尝试从缓存获取预测结果
+        cache_key = f"{defaultTopic}_{model_type}"
+        cached_result = prediction_cache.get(cache_key)
+        
+        if cached_result is not None:
+            sentences = cached_result
         else:
-            # 使用改进模型
-            predicted_label, confidence = predict_sentiment(defaultTopic)
-            if predicted_label is not None:
-                sentences = '良好' if predicted_label == 0 else '不良'
-                sentences = f"{sentences} (置信度: {confidence:.2%})"
+            if model_type == 'basic':
+                # 使用基础模型（SnowNLP）
+                value = SnowNLP(defaultTopic).sentiments
+                if value == 0.5:
+                    sentences = '中性'
+                elif value > 0.5:
+                    sentences = '正面'
+                elif value < 0.5:
+                    sentences = '负面'
             else:
-                sentences = '预测失败，请稍后重试'
-                logging.error(f"预测失败，话题: {defaultTopic}")
+                # 使用改进模型
+                predicted_label, confidence = predict_sentiment(defaultTopic)
+                if predicted_label is not None:
+                    sentences = '良好' if predicted_label == 0 else '不良'
+                    sentences = f"{sentences} (置信度: {confidence:.2%})"
+                else:
+                    sentences = '预测失败，请稍后重试'
+                    logging.error(f"预测失败，话题: {defaultTopic}")
+            
+            # 将结果存入缓存
+            prediction_cache.set(cache_key, sentences)
         
         comments = getCommentFilterDataTopic(defaultTopic)
         return render_template('yuqingpredict.html',