优化预测函数，增加执行时间和输入文本长度的打印，调整处理逻辑以提高可读性

blkeras.py

@@ -1,4 +1,5 @@
 import os
+from tracemalloc import start
 from huggingface_hub import login
 from huggingface_hub import hf_hub_download
 
@@ -98,6 +99,8 @@ def predict(text: str, stock_codes: list):
     from preprocess import get_document_vector, get_stock_info, preprocessing_entry, process_entities, process_pos_tags, processing_entry
 
     try:
+
+        start_time = datetime.now()
         input_text = text
         affected_stock_codes = stock_codes
 
@@ -110,7 +113,7 @@ def predict(text: str, stock_codes: list):
         processed_entry = processing_entry(input_text)
 
         # 解包 processed_entry 中的各个值
-        lemmatized_entry, pos_tag, ner, dependency_parsing, sentiment_score = processed_entry
+        lemmatized_entry, pos_tag, ner, _ , sentiment_score = processed_entry
 
         # 分别打印每个变量，便于调试
         #print("Lemmatized Entry:", lemmatized_entry)
@@ -403,6 +406,9 @@ def predict(text: str, stock_codes: list):
         print(f"predict() error: {e}")
         print(traceback_str)
         return {"predict() error": str(e), "traceback": traceback_str}
+    finally:
+        end_time = datetime.now()
+        print(f"predict() Text: {input_text[:200] if len(input_text) > 200 else input_text} \n execution time: {end_time - start_time}, Text Length: {len(input_text)} \n")
     
 
 def stock_fix_for_1118_model(score, predictions, last_prices, is_index=True):

preprocess.py

@@ -562,8 +562,9 @@ def processing_entry(entry):
     ner = named_entity_recognition(cleaned_text)
     # print(f"named_entity_recognition: {db_ner}")
 
-    dependency_parsed = dependency_parsing(cleaned_text)
+    # dependency_parsed = dependency_parsing(cleaned_text)
     # print(f"dependency_parsing: {db_dependency_parsing}")
+    dependency_parsed = None
 
     sentiment_score = get_sentiment_score(cleaned_text)
     # print(f"sentiment_score: {sentiment_score}")