优化

predict/txt_predict.py

@@ -18,7 +18,6 @@ class TxtPredict(BasePredictor):
 
     def pre_deal(self, li: list):
         data = []
-        resp = {}
         for row_index, i in enumerate(li):
             raw_items = re.split(r'(?:，|,|;|\s)\s*', i)
             no_null_raw_items = [j.strip() for j in raw_items if j.strip()]
@@ -26,22 +25,13 @@ class TxtPredict(BasePredictor):
                 'qty': None,
                 'brand': None,
                 'gn_or_param': None,
-                'statu_code': 1
             }
             for item in no_null_raw_items:
                 if self.isnum(item):
                     resp_data['qty'] = item
                     no_null_raw_items.remove(item)
                     break
-            else:
-                resp_data = {'message': '未检测到数量', 'statu_code': 0}
-                resp[row_index] = resp_data
-                data.append((row_index, resp_data, no_null_raw_items))
-                continue
 
-            if not no_null_raw_items:
-                resp_data = {'message': '未检测到参数/型号', 'statu_code': 0}
-                resp[row_index] = resp_data
             data.append((row_index, resp_data, no_null_raw_items))
         return data
 
@@ -50,11 +40,9 @@ class TxtPredict(BasePredictor):
         data = self.pre_deal(li)
         resp = {}
         for row_index, resp_data, nonull_nonum_items in data:
-            if resp_data['statu_code'] == 0:
-                resp[row_index] = resp_data
-                continue
-
-            if len(nonull_nonum_items) == 1:
+            if not nonull_nonum_items:
+                pass
+            elif len(nonull_nonum_items) == 1:
                 resp_data['gn_or_param'] = nonull_nonum_items[0]
             elif len(nonull_nonum_items) >= 2:
                 result_1, prab = self.get_single_predict(nonull_nonum_items[-1], BasePredictor.model, BasePredictor.extractor)