diff --git a/ebay_api.py b/ebay_api.py
index a9857df..a1031b3 100644
--- a/ebay_api.py
+++ b/ebay_api.py
@@ -45,12 +45,13 @@ class FindingApi:
         try:# TODO run pdb here to see how to extract itemId before update_df
             training = pd.read_csv('training.csv')
             for item in data['findItemsByCategoryResponse'][0]['searchResult'][0]['item']:
-                if item not in training.values:
+                if item not in training.values not in itemid_results_list:# TODO need to figure out syntax to check if item_id is present in both list and training set. (list to avoid repeat results)
                     itemid_results_list.append(item['itemId'][0]) # itemId
                     # values are in lists for some reason
         except (pd.errors.EmptyDataError, FileNotFoundError):
             for item in data['findItemsByCategoryResponse'][0]['searchResult'][0]['item']:
-                itemid_results_list.append(item['itemId'][0])
+                if item not in itemid_results_list:
+                    itemid_results_list.append(item['itemId'][0])
         item_id_results = [','.join(itemid_results_list[n:n+20]) for n in list(range(0,
             len(itemid_results_list), 20))]
 
@@ -87,17 +88,17 @@ class CurateData:
         '''
         Extracts itemIds and name-value list , creates new dict and appends df
         '''
-        names = []
-        values = []
-        nvl = data['Item'][0]['ItemSpecifics']['NameValueList'][0]# TODO is this only for one item?
+        for item in data:
+            names = []
+            values = []
+            nvl = data['Item'][0]['ItemSpecifics']['NameValueList']
+            for nvl_dict in nvl:
+                names.append(nvl_dict['Name'])
+                values.append(nvl_dict['Value'])
+            # TODO Also append itemId and value to the dictionary somewhere
 
-        for nvl_dict in nvl:
-            names.append(nvl_dict['Name'])
-            values.append(nvl_dict['Value'])
-        # TODO Also append itemId and value to the dictionary
-
-        nvl_dict = dict(zip(names, values))
-        data.update(nvl_dict)
+            nvl_dict = dict(zip(names, values))
+            data.update(nvl_dict)
         df = pd.json_normalize(data)
         df.to_csv('training.csv', mode='a')