Added inference function for the model

@@ -35,7 +35,7 @@ class BERT_CTM_Model:
             inputs = self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=80).to(self.device)
             inputs = self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=80).to(self.device)
             with torch.no_grad():
             with torch.no_grad():
                 outputs = self.model(**inputs)
                 outputs = self.model(**inputs)
-            embeddings.append(outputs.last_hidden_state.cpu().numpy())  # [batch_size, sequence_length, hidden_size]
+            embeddings.append(outputs.last_hidden_state[:, 0, :].cpu().numpy())  # [batch_size, hidden_size]
         return np.vstack(embeddings)
         return np.vstack(embeddings)
 
 
     def chinese_tokenize(self, text):
     def chinese_tokenize(self, text):
@@ -57,6 +57,20 @@ class BERT_CTM_Model:
         except Exception as e:
         except Exception as e:
             print(f"训练CTM模型时发生错误: {e}")
             print(f"训练CTM模型时发生错误: {e}")
 
 
+    def predict(self, texts):
+        """使用训练好的CTM模型预测新文本的主题分布"""
+        if not self.ctm_model:
+            raise ValueError("模型尚未训练或加载，无法进行预测")
+        
+        try:
+            bow_texts = [self.chinese_tokenize(text) for text in texts]
+            testing_dataset = self.tp.transform(text_for_contextual=texts, text_for_bow=bow_texts)
+            topic_distributions = self.ctm_model.get_doc_topic_distribution(testing_dataset)
+            return topic_distributions
+        except Exception as e:
+            print(f"预测主题时发生错误: {e}")
+            return None
+
     def save_model(self, path):
     def save_model(self, path):
         """保存训练后的CTM模型"""
         """保存训练后的CTM模型"""
         if self.ctm_model:
         if self.ctm_model:
@@ -92,3 +106,12 @@ if __name__ == "__main__":
 
 
     # 加载CTM模型
     # 加载CTM模型
     model.load_model('./trained_ctm_model')
     model.load_model('./trained_ctm_model')
+
+    # 预测新文本的主题分布
+    new_texts = ["这是一个新的文本", "另外一个新文本"]
+    topic_distributions = model.predict(new_texts)
+
+    # 输出预测结果
+    if topic_distributions is not None:
+        for idx, distribution in enumerate(topic_distributions):
+            print(f"文本 {idx+1} 的主题分布: {distribution}")