优化预测函数的错误处理逻辑，返回空列表以替代错误信息；增加对空文本和股票代码的处理，确保输入有效性

app.py

@@ -76,7 +76,7 @@ async def predict(request: PredictRequest):
         )
         return result
     except Exception as e:
-        return {"error": str(e)}
+        return []
 
 @app.get("/")
 async def root():

blkeras.py

@@ -99,7 +99,11 @@ def predict(text: str, stock_codes: list):
     from preprocess import get_document_vector, get_stock_info, process_entities, process_pos_tags, processing_entry
 
     try:
-
+        if text.strip() == "" and not stock_codes:
+            return []
+        if stock_codes and text.strip() == "":
+            text = "EMPTY_TEXT"
+            
         print(f"Input Text Length: {len(text)}, Start with: {text[:200] if len(text) > 200 else text}")
         print("Input stock codes:", stock_codes)
 
@@ -406,7 +410,7 @@ def predict(text: str, stock_codes: list):
         traceback_str = traceback.print_exc()
         print(f"predict() error: {e}")
         print(traceback_str)
-        return {"predict() error": str(e), "traceback": traceback_str}
+        return []
     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")

preprocess.py

@@ -266,6 +266,10 @@ def process_long_text(text, tokenizer, max_length=512):
     return segments
 
 def get_sentiment_score(text):
+    if text and text.strip() == "EMPTY_TEXT":
+        return 0.0
+    
+    
     try:
         import torch