Edited output

app.py

@@ -13,6 +13,7 @@ model_cat = BertForSequenceClassification.from_pretrained('warleagle/service_nam
 tokenizer_spec = BertTokenizer.from_pretrained("warleagle/specialists_categorizer_model",
                                           token=HF_TOKEN)
 model_spec = BertForSequenceClassification.from_pretrained('warleagle/specialists_categorizer_model',token=HF_TOKEN)
+
 def categoriser_predict(input_text):
     clf = pipeline("text-classification", model=model_cat, tokenizer=tokenizer_cat)
     predictions = clf(input_text)

service_dops_api/dops_classifier.py

@@ -15,13 +15,16 @@ class DopsClassifier:
             result[self.config.dops_default_values[dop_name]] = 0
         return result
     
-    def convert_search_to_human(self,dict_from_search):
-        return [key for key, value in dict_from_search.items() if value == 1]
-    
+    def convert_search_to_human(self,dict_from_search,dop_name):
+        result = {}
+        result['dop_name'] = dop_name
+        result['values'] = [key for key, value in dict_from_search.items() if value == 1]
+        result['default_values'] = list(dict_from_search.keys())
+        return result
     def run_all_dops(self,text):
         all_dops = self.config.option_patterns_dict.keys()
-        result_dict = {}
+        result_list = []
         for dop in all_dops:
             temp_dop = self.run_regular_search(text,dop)
-            result_dict[dop] = self.convert_search_to_human(temp_dop)
-        return result_dict
+            result_list.append(self.convert_search_to_human(temp_dop,dop))
+        return result_list