The old emotion recognition model has been replaced with the new model_pro, and the results have been integrated into the project.

utils/getEchartsData.py

@@ -1,10 +1,59 @@
 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
 
 articleList = getAllArticleData()  # Retrieve all article data
 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）
+model_save_path = 'model_pro/final_model.pt'
+bert_model_path = 'model_pro/bert_model'
+ctm_tokenizer_path = 'model_pro/sentence_bert_model'
+
+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
+    )
+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()
+    except Exception as e:
+        print(f"预测过程中出现错误: {e}")
+        return None
+
 def getTypeList():
     # Return a list of unique article types
     return list(set([x[8] for x in articleList]))
@@ -119,32 +168,32 @@ def getYuQingCharDataOne():
     return X, Y, biedata
 
 def getYuQingCharDataTwo():
-    # Analyze sentiment of comments and articles
-    comment_sentiments = []
-    for comment in commentList:
-        emotionValue = SnowNLP(comment[4]).sentiments
-        if emotionValue > 0.4:
-            comment_sentiments.append('正面')
-        elif emotionValue < 0.2:
-            comment_sentiments.append('负面')
-        else:
-            comment_sentiments.append('中性')
-    comment_counts = Counter(comment_sentiments)
+    # 分析评论和文章的情感
+    comment_texts = [comment[4] for comment in commentList]
+    article_texts = [article[5] for article in articleList]
     
-    article_sentiments = []
-    for article in articleList:
-        emotionValue = SnowNLP(article[5]).sentiments
-        if emotionValue > 0.4:
-            article_sentiments.append('正面')
-        elif emotionValue < 0.2:
-            article_sentiments.append('负面')
-        else:
-            article_sentiments.append('中性')
+    # 预测评论情感
+    comment_predictions = 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]
+    else:
+        comment_sentiments = []
+    
+    # 预测文章情感
+    article_predictions = 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]
+    else:
+        article_sentiments = []
+    
+    # 统计结果
+    comment_counts = Counter(comment_sentiments)
     article_counts = Counter(article_sentiments)
     
-    X = ['正面', '中性', '负面']
+    X = ['良好', '不良']
     biedata1 = [{'name': x, 'value': comment_counts.get(x, 0)} for x in X]
     biedata2 = [{'name': x, 'value': article_counts.get(x, 0)} for x in X]
+    
     return biedata1, biedata2
 
 def getYuQingCharDataThree():