fix 代码整理

app/api/api_outfit_matcher.py

@@ -29,41 +29,41 @@ def outfit_matcher(request_item: OutfitMatcher):
     service = OutfitMaterTypeAware()
     all_items = request_item["query"] + request_item["database"]
     prepared_feature = {}
+    have_features_data = []
+    no_have_features_data = []
+    temp_data = deepcopy(all_items)
 
     # 连接milvus
     client = MilvusClient(uri=MILVUS_URL, token="root:Milvus", db_name="mixi")
-    search_data = client.get(collection_name='mixi_outfit', ids=[item['item_name'] for item in all_items])
+    try:
+        search_data = client.get(collection_name='mixi_outfit', ids=[item['item_name'] for item in all_items])
+        # 查询数据库，分成两批 需要过模型推理的和不需要的
+        for td in temp_data:
+            for sd in search_data:
+                if td['item_name'] == sd['item_name']:
+                    td['features'] = sd['features']
+            if "features" not in td.keys():
+                no_have_features_data.append(td)
+            else:
+                have_features_data.append(td)
 
-    # 查询数据库，分成两批 需要过模型推理的和不需要的
+        if len(no_have_features_data) > 0:
-    have_features_data = []
+            extracted_features = backbone_service.get_result(no_have_features_data)
-    no_have_features_data = []
 
-    temp_data = deepcopy(all_items)
+            # 准备数据
-    for td in temp_data:
+            data = deepcopy(no_have_features_data)  # 做深拷贝 , all_items 是list 可变数组
-        for sd in search_data:
+            for i, feature in enumerate(extracted_features):
-            if td['item_name'] == sd['item_name']:
+                data[i]['features'] = feature
-                td['features'] = sd['features']
+                if 'mapped_cate' in data[i].keys():
-        if "features" not in td.keys():
+                    del data[i]['mapped_cate']
-            no_have_features_data.append(td)
-        else:
-            have_features_data.append(td)
 
-    if len(no_have_features_data) > 0:
+            # 存入数据
-        extracted_features = backbone_service.get_result(no_have_features_data)
+            res = client.insert(collection_name="mixi_outfit", data=data)
-
+            # 断开连接
-        # 准备数据
+            for d in data:
-        data = deepcopy(no_have_features_data)  # 做深拷贝 , all_items 是list 可变数组
+                prepared_feature[d['item_name']] = d['features']
-        for i, feature in enumerate(extracted_features):
+    finally:
-            data[i]['features'] = feature
-            if 'mapped_cate' in data[i].keys():
-                del data[i]['mapped_cate']
-
-        # 存入数据
-        res = client.insert(collection_name="mixi_outfit", data=data)
-        # 断开连接
         client.close()
-        for d in data:
-            prepared_feature[d['item_name']] = d['features']
 
     for hfd in have_features_data:
         prepared_feature[hfd['item_name']] = hfd['features']

app/service/similar_match/service.py

@@ -86,7 +86,6 @@ class SimilarMatch:
 
     @RunTime
     def match_features(self):
-        # 连接milvus
         # 连接milvus
         client = MilvusClient(uri=MILVUS_URL, db_name="mixi")
         try: