Optimize model loading and prediction performance, implement the singleton patte…

…rn, and provide comprehensive error handling and error messages, along with confidence level display.

@@ -13,9 +13,83 @@ from model_pro.MHA import MultiHeadAttentionLayer
 from model_pro.classifier import FinalClassifier
 from model_pro.BERT_CTM import BERT_CTM_Model
 
-# 设置设备
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+class ModelManager:
+    _instance = None
+    _initialized = False
+    
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super(ModelManager, cls).__new__(cls)
+        return cls._instance
+    
+    def __init__(self):
+        if not self._initialized:
+            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            self.classifier_model = None
+            self.attention_model = None
+            self.bert_ctm_model = None
+            self._initialized = True
+    
+    def load_models(self, model_save_path, bert_model_path, ctm_tokenizer_path):
+        """加载所有需要的模型"""
+        try:
+            if self.classifier_model is None:
+                self.classifier_model = torch.load(model_save_path, map_location=self.device)
+                self.classifier_model.eval()
                 
+            if self.attention_model is None:
+                self.attention_model = MultiHeadAttentionLayer(embed_size=768, num_heads=8)
+                self.attention_model.to(self.device)
+                self.attention_model.eval()
+                
+            if self.bert_ctm_model is None:
+                self.bert_ctm_model = BERT_CTM_Model(
+                    bert_model_path=bert_model_path,
+                    ctm_tokenizer_path=ctm_tokenizer_path
+                )
+            return True
+        except Exception as e:
+            print(f"模型加载失败: {e}")
+            return False
+    
+    def predict_batch(self, texts, batch_size=32):
+        """批量预测文本情感"""
+        try:
+            all_predictions = []
+            all_probabilities = []
+            
+            # 分批处理文本
+            for i in range(0, len(texts), batch_size):
+                batch_texts = texts[i:i + batch_size]
+                
+                # 获取文本嵌入
+                embeddings = self.bert_ctm_model.get_bert_embeddings(batch_texts)
+                
+                # 转换为tensor
+                batch_x = torch.tensor(embeddings, dtype=torch.float32).to(self.device)
+                batch_x = torch.mean(batch_x, dim=1)
+                
+                with torch.no_grad():
+                    # 使用注意力机制
+                    attention_output = self.attention_model(batch_x, batch_x, batch_x)
+                    # 获取分类结果
+                    outputs = self.classifier_model(attention_output)
+                    outputs = torch.mean(outputs, dim=1)
+                    # 获取预测概率
+                    probabilities = torch.softmax(outputs, dim=1)
+                    # 获取预测标签
+                    _, predicted = torch.max(outputs, 1)
+                    
+                    all_predictions.extend(predicted.cpu().numpy())
+                    all_probabilities.extend(probabilities.cpu().numpy())
+                    
+            return all_predictions, all_probabilities
+        except Exception as e:
+            print(f"预测过程中出现错误: {e}")
+            return None, None
+
+# 创建全局的模型管理器实例
+model_manager = ModelManager()
 
 def detect_file_encoding(file_path, num_bytes=10000):
     """
@@ -59,12 +133,8 @@ def predict(model_save_path, input_data_path, output_path, bert_model_path, ctm_
     try:
         # 加载模型
         print("加载模型...")
-        classifier_model = torch.load(model_save_path, map_location=device)
-        classifier_model.eval()
-
-        attention_model = MultiHeadAttentionLayer(embed_size=768, num_heads=8)
-        attention_model.to(device)
-        attention_model.eval()
+        if not model_manager.load_models(model_save_path, bert_model_path, ctm_tokenizer_path):
+            return False
 
         # 检测文件编码
         encoding = detect_file_encoding(input_data_path)
@@ -88,14 +158,14 @@ def predict(model_save_path, input_data_path, output_path, bert_model_path, ctm_
         print("开始预测...")
         with torch.no_grad():
             for batch in tqdm(data_loader, desc="预测进度"):
-                batch_x = batch[0].to(device)
+                batch_x = batch[0].to(model_manager.device)
                 batch_x = torch.mean(batch_x, dim=1)
                 
                 # 使用注意力机制
-                attention_output = attention_model(batch_x, batch_x, batch_x)
+                attention_output = model_manager.attention_model(batch_x, batch_x, batch_x)
                 
                 # 获取分类结果
-                outputs = classifier_model(attention_output)
+                outputs = model_manager.classifier_model(attention_output)
                 outputs = torch.mean(outputs, dim=1)
                 
                 # 获取预测概率

@@ -2,9 +2,7 @@ from utils.getPublicData import *  # Import utility functions for data retrieval
 from utils.mynlp import SnowNLP  # Import SnowNLP for sentiment analysis
 from collections import Counter  # Import Counter for counting occurrences
 import torch
-from model_pro.MHA import MultiHeadAttentionLayer
-from model_pro.classifier import FinalClassifier
-from model_pro.BERT_CTM import BERT_CTM_Model
+from BCAT_front.predict import model_manager
 
 articleList = getAllArticleData()  # Retrieve all article data
 commentList = getAllCommentsData()  # Retrieve all comment data
@@ -12,47 +10,27 @@ commentList = getAllCommentsData()  # Retrieve all comment data
 # 设置设备
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# 加载模型（全局变量，避免重复加载）
+# 设置模型路径
 model_save_path = 'model_pro/final_model.pt'
 bert_model_path = 'model_pro/bert_model'
 ctm_tokenizer_path = 'model_pro/sentence_bert_model'
 
+# 初始化模型
 try:
-    classifier_model = torch.load(model_save_path, map_location=device)
-    classifier_model.eval()
-    attention_model = MultiHeadAttentionLayer(embed_size=768, num_heads=8)
-    attention_model.to(device)
-    attention_model.eval()
-    bert_ctm_model = BERT_CTM_Model(
-        bert_model_path=bert_model_path,
-        ctm_tokenizer_path=ctm_tokenizer_path
-    )
+    model_manager.load_models(model_save_path, bert_model_path, ctm_tokenizer_path)
 except Exception as e:
     print(f"模型加载失败: {e}")
 
 def predict_sentiment(texts):
     """使用改进版模型预测情感"""
     try:
-        # 获取文本嵌入
-        embeddings = bert_ctm_model.get_bert_embeddings(texts)
-        
-        # 转换为tensor
-        batch_x = torch.tensor(embeddings, dtype=torch.float32).to(device)
-        batch_x = torch.mean(batch_x, dim=1)
-        
-        with torch.no_grad():
-            # 使用注意力机制
-            attention_output = attention_model(batch_x, batch_x, batch_x)
-            # 获取分类结果
-            outputs = classifier_model(attention_output)
-            outputs = torch.mean(outputs, dim=1)
-            # 获取预测标签
-            _, predicted = torch.max(outputs, 1)
-            
-        return predicted.cpu().numpy()
+        predictions, probabilities = model_manager.predict_batch(texts)
+        if predictions is not None:
+            return predictions, probabilities
+        return None, None
     except Exception as e:
         print(f"预测过程中出现错误: {e}")
-        return None
+        return None, None
 
 def getTypeList():
     # Return a list of unique article types
@@ -194,15 +172,23 @@ def getYuQingCharDataTwo(model_type='pro'):
                 article_sentiments.append('不良')
     else:
         # 使用改进模型
-        comment_predictions = predict_sentiment(comment_texts)
+        comment_predictions, comment_probs = predict_sentiment(comment_texts)
         if comment_predictions is not None:
-            comment_sentiments = ['良好' if pred == 0 else '不良' for pred in comment_predictions]
+            comment_sentiments = []
+            for pred, prob in zip(comment_predictions, comment_probs):
+                label = '良好' if pred == 0 else '不良'
+                confidence = prob[pred]
+                comment_sentiments.append(f"{label} ({confidence:.2%})")
         else:
             comment_sentiments = []
         
-        article_predictions = predict_sentiment(article_texts)
+        article_predictions, article_probs = predict_sentiment(article_texts)
         if article_predictions is not None:
-            article_sentiments = ['良好' if pred == 0 else '不良' for pred in article_predictions]
+            article_sentiments = []
+            for pred, prob in zip(article_predictions, article_probs):
+                label = '良好' if pred == 0 else '不良'
+                confidence = prob[pred]
+                article_sentiments.append(f"{label} ({confidence:.2%})")
         else:
             article_sentiments = []
     

-from flask import Flask, session, render_template, redirect, Blueprint, request
+from flask import Flask, session, render_template, redirect, Blueprint, request, jsonify
 from utils.mynlp import SnowNLP
 from utils.getHomePageData import *
 from utils.getHotWordPageData import *
@@ -9,9 +9,7 @@ from utils.getTopicPageData import *
 from utils.yuqingpredict import *
 from utils.logger import app_logger as logging
 import torch
-from model_pro.MHA import MultiHeadAttentionLayer
-from model_pro.classifier import FinalClassifier
-from model_pro.BERT_CTM import BERT_CTM_Model
+from BCAT_front.predict import model_manager
 
 pb = Blueprint('page',
                __name__,
@@ -21,47 +19,26 @@ pb = Blueprint('page',
 # 设置设备
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# 加载模型（全局变量，避免重复加载）
+# 设置模型路径
 model_save_path = 'model_pro/final_model.pt'
 bert_model_path = 'model_pro/bert_model'
 ctm_tokenizer_path = 'model_pro/sentence_bert_model'
 
+# 初始化模型
 try:
-    classifier_model = torch.load(model_save_path, map_location=device)
-    classifier_model.eval()
-    attention_model = MultiHeadAttentionLayer(embed_size=768, num_heads=8)
-    attention_model.to(device)
-    attention_model.eval()
-    bert_ctm_model = BERT_CTM_Model(
-        bert_model_path=bert_model_path,
-        ctm_tokenizer_path=ctm_tokenizer_path
-    )
+    model_manager.load_models(model_save_path, bert_model_path, ctm_tokenizer_path)
 except Exception as e:
-    print(f"模型加载失败: {e}")
+    logging.error(f"模型加载失败: {e}")
 
 def predict_sentiment(text):
     """使用改进版模型预测单个文本的情感"""
     try:
-        # 获取文本嵌入
-        embeddings = bert_ctm_model.get_bert_embeddings([text])
-        
-        # 转换为tensor
-        batch_x = torch.tensor(embeddings, dtype=torch.float32).to(device)
-        batch_x = torch.mean(batch_x, dim=1)
-        
-        with torch.no_grad():
-            # 使用注意力机制
-            attention_output = attention_model(batch_x, batch_x, batch_x)
-            # 获取分类结果
-            outputs = classifier_model(attention_output)
-            outputs = torch.mean(outputs, dim=1)
-            # 获取预测标签和概率
-            probabilities = torch.softmax(outputs, dim=1)
-            _, predicted = torch.max(outputs, 1)
-            
-        return predicted.item(), probabilities[0][predicted.item()].item()
+        predictions, probabilities = model_manager.predict_batch([text])
+        if predictions is not None and len(predictions) > 0:
+            return predictions[0], probabilities[0][predictions[0]]
+        return None, None
     except Exception as e:
-        print(f"预测过程中出现错误: {e}")
+        logging.error(f"预测过程中出现错误: {e}")
         return None, None
 
 @pb.route('/home')
@@ -218,6 +195,7 @@ def yuqingChar():
 
 @pb.route('/yuqingpredict')
 def yuqingpredict():
+    try:
         username = session.get('username')
         TopicList = getAllTopicData()
         defaultTopic = TopicList[0][0]
@@ -243,9 +221,10 @@ def yuqingpredict():
             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:.2f})"
+                sentences = f"{sentences} (置信度: {confidence:.2%})"
             else:
-            sentences = '预测失败'
+                sentences = '预测失败，请稍后重试'
+                logging.error(f"预测失败，话题: {defaultTopic}")
         
         comments = getCommentFilterDataTopic(defaultTopic)
         return render_template('yuqingpredict.html',
@@ -258,6 +237,9 @@ def yuqingpredict():
                                yData=Y,
                                comments=comments,
                                model_type=model_type)
+    except Exception as e:
+        logging.error(f"舆情预测页面渲染失败: {e}")
+        return render_template('error.html', error_message="加载舆情预测页面失败，请稍后重试")
 
 
 @pb.route('/articleCloud')