修复预测函数中的股票和指数影响计算逻辑，添加零值处理以避免除零错误，并格式化输出影响值为百分比字符串

blkeras.py

@@ -231,10 +231,11 @@ def predict(text: str, stock_codes: list):
         last_index_dj_value = previous_stock_dj_index_history[0][-1][0]
         last_index_ixic_value = previous_stock_ixic_index_history[0][-1][0]
         last_index_ndx_value = previous_stock_ndx_index_history[0][-1][0]
+        last_stock_value = previous_stock_history[0][-1][0]
 
 
         # 针对 1012 模型的修复
-        stock_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), stock_predictions[0], previous_stock_history[0][-1][0])
+        stock_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), stock_predictions[0], last_stock_value)
         index_inx_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_inx_predictions[0], last_index_inx_value) 
         index_dj_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_dj_predictions[0], last_index_dj_value) 
         index_ixic_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_ixic_predictions[0], last_index_ixic_value) 
@@ -265,22 +266,30 @@ def predict(text: str, stock_codes: list):
         index_ndx_day_2 = index_ndx_predictions[1][0]
         index_ndx_day_3 = index_ndx_predictions[2][0]
 
+        stock_day_1 = stock_predictions[0][0]
+        stock_day_2 = stock_predictions[1][0]
+        stock_day_3 = stock_predictions[2][0]
+
         # 计算 impact_1_day, impact_2_day, impact_3_day
-        impact_inx_1_day = (index_inx_day_1 - last_index_inx_value) / last_index_inx_value
-        impact_inx_2_day = (index_inx_day_2 - index_inx_day_1) / index_inx_day_1
-        impact_inx_3_day = (index_inx_day_3 - index_inx_day_2) / index_inx_day_2
+        impact_inx_1_day = (index_inx_day_1 - last_index_inx_value) / last_index_inx_value if last_index_inx_value != 0 else 0
+        impact_inx_2_day = (index_inx_day_2 - index_inx_day_1) / index_inx_day_1 if index_inx_day_1 != 0 else 0
+        impact_inx_3_day = (index_inx_day_3 - index_inx_day_2) / index_inx_day_2 if index_inx_day_2 != 0 else 0
+
+        impact_dj_1_day = (index_dj_day_1 - last_index_dj_value) / last_index_dj_value if last_index_dj_value != 0 else 0
+        impact_dj_2_day = (index_dj_day_2 - index_dj_day_1) / index_dj_day_1 if index_dj_day_1 != 0 else 0
+        impact_dj_3_day = (index_dj_day_3 - index_dj_day_2) / index_dj_day_2 if index_dj_day_2 != 0 else 0
 
-        impact_dj_1_day = (index_dj_day_1 - last_index_dj_value) / last_index_dj_value
-        impact_dj_2_day = (index_dj_day_2 - index_dj_day_1) / index_dj_day_1
-        impact_dj_3_day = (index_dj_day_3 - index_dj_day_2) / index_dj_day_2
+        impact_ixic_1_day = (index_ixic_day_1 - last_index_ixic_value) / last_index_ixic_value if last_index_ixic_value != 0 else 0
+        impact_ixic_2_day = (index_ixic_day_2 - index_ixic_day_1) / index_ixic_day_1 if index_ixic_day_1 != 0 else 0
+        impact_ixic_3_day = (index_ixic_day_3 - index_ixic_day_2) / index_ixic_day_2 if index_ixic_day_2 != 0 else 0
 
-        impact_ixic_1_day = (index_ixic_day_1 - last_index_ixic_value) / last_index_ixic_value
-        impact_ixic_2_day = (index_ixic_day_2 - index_ixic_day_1) / index_ixic_day_1
-        impact_ixic_3_day = (index_ixic_day_3 - index_ixic_day_2) / index_ixic_day_2
+        impact_ndx_1_day = (index_ndx_day_1 - last_index_ndx_value) / last_index_ndx_value if last_index_ndx_value != 0 else 0
+        impact_ndx_2_day = (index_ndx_day_2 - index_ndx_day_1) / index_ndx_day_1 if index_ndx_day_1 != 0 else 0
+        impact_ndx_3_day = (index_ndx_day_3 - index_ndx_day_2) / index_ndx_day_2 if index_ndx_day_2 != 0 else 0
 
-        impact_ndx_1_day = (index_ndx_day_1 - last_index_ndx_value) / last_index_ndx_value
-        impact_ndx_2_day = (index_ndx_day_2 - index_ndx_day_1) / index_ndx_day_1
-        impact_ndx_3_day = (index_ndx_day_3 - index_ndx_day_2) / index_ndx_day_2
+        impact_stock_1_day = (stock_day_1 - last_stock_value) / last_stock_value if last_stock_value != 0 else 0
+        impact_stock_2_day = (stock_day_2 - stock_day_1) / stock_day_1 if stock_day_1 != 0 else 0
+        impact_stock_3_day = (stock_day_3 - stock_day_2) / stock_day_2 if stock_day_2 != 0 else 0
 
         # 将 impact 值转换为百分比字符串
         impact_inx_1_day_str = f"{impact_inx_1_day:.2%}"
@@ -299,6 +308,10 @@ def predict(text: str, stock_codes: list):
         impact_ndx_2_day_str = f"{impact_ndx_2_day:.2%}"
         impact_ndx_3_day_str = f"{impact_ndx_3_day:.2%}"
 
+        impact_stock_1_day_str = f"{impact_stock_1_day:.2%}"
+        impact_stock_2_day_str = f"{impact_stock_2_day:.2%}"
+        impact_stock_3_day_str = f"{impact_stock_3_day:.2%}"
+
 
         # 如果需要返回原始预测数据进行调试，可以直接将其放到响应中
         if len(affected_stock_codes) > 5:
@@ -334,6 +347,9 @@ def predict(text: str, stock_codes: list):
             "impact_ndx_1_day": impact_ndx_1_day_str,                # 计算并格式化 impact_1_day
             "impact_ndx_2_day": impact_ndx_2_day_str,                # 计算并格式化 impact_2_day
             "impact_ndx_3_day": impact_ndx_3_day_str,  
+            "impact_stock_1_day": impact_stock_1_day_str,                # 计算并格式化 impact_1_day
+            "impact_stock_2_day": impact_stock_2_day_str,                # 计算并格式化 impact_2_day
+            "impact_stock_3_day": impact_stock_3_day_str,  
             "affected_stock_codes": affected_stock_codes_str,  # 动态生成受影响的股票代码
             "accuracy": float(fake_accuracy),
             "impact_on_stock": stock_predictions,     # 第一个预测值是股票影响