diff --git a/ebay_api.py b/ebay_api.py
index b3d6aa0..0423a9d 100644
--- a/ebay_api.py
+++ b/ebay_api.py
@@ -278,12 +278,12 @@ class CurateData:
 
         expanded_dropd = self.extract_df(expanded_dropd) # convert lists to values
 
-        dict_pics_list = list(set(expanded_class.PictureURL.to_list())) # prolly need to create set long before df... immediately after Shopping or trading call
+        pics_source_list = list(set(expanded_class.PictureURL.to_list())) # prolly need to create set long before df... immediately after Shopping or trading call
         destination = 'your target folder' # decide whether or not you want to set a default folder to have the user define it as input every time. or have this only 
         # defined in the download function
 
 #        '''Will use temp_dict_pics for changing the training set at preprocessing'''
-#        temp_dict_pics = {k:destination+re.search(r'[^/]+(?=/\$_|.jpg)', k, re.IGNORECASE).group()+'.jpg' for k in dict_pics_list}
+#        temp_dict_pics = {k:destination+re.search(r'[^/]+(?=/\$_|.jpg)', k, re.IGNORECASE).group()+'.jpg' for k in pics_source_list}
 #        # TODO decide if the above is necesssary at this point or if it should
 #        # be created at preprocessing or download
 #
@@ -296,37 +296,46 @@ class CurateData:
 #            except ValueError:
 #                json.dump(temp_dict_pics, f)
 
-        with open('dict_pics_list.txt', 'a+') as f: # Temp iterable for use w/executor
+        with open('pics_source_list.txt', 'a+') as f: # Temp iterable for use w/executor
             try:
-                dict_pics_list = json.load(f)
-                dict_pics_list.append(dict_pics_list)
-                json.dump(dict_pics_list, f)
-            
+                pics_source_list = json.load(f)
+                pics_source_list.append(pics_source_list)
+                json.dump(pics_source_list, f)
             except ValueError:
-                json.dump(dict_pics_list, f)
+                json.dump(pics_source_list, f)
 
         return expanded_class, expanded_dropd
 
     def dl_pictures(self):
         '''
-        Downloads pictures from api to local storage using custom master dict
+        Downloads pictures from api to local storage using pics_source_list
+        and creates custom {source:target} dictionary as dict_pics
         '''
 
         with open('target_dirs.txt', 'a+') as f: # TODO you can add option to change directory here, too. Look up how to have optional arguments
             try:
                 target_dir = json.load(f)
+
             except ValueError:
-                target_dir = input('No default directory found. Create One? [y] or [n]:')
+                target_dir = input('No target directory found. Create One? [y] or [n]:')
+                if target_dir == 'y':
+                    target_dir = input('Please provide full URL to destination folder')
+                else:
+                    print('Creating default folder in current directory')
+                    target_dir = os.getcwd()
+                    json.dump(target_dir, f)
+
         with open('dict_pics.txt') as jf:
             dict_pics = json.load(jf)
 
-        with open('dict_pics_list.txt') as f:
-            dict_pics_list = json.load(f)
+        with open('pics_source_list.txt') as f:
+            pics_source_list = json.load(f)
 
         def dl_pic(pic):
 
             if os.path.exists(dict_pics[pic]):
                 pass
+
             else:
                 r = requests.get(pic, stream=True)
                 r.raw.decode_content = True
@@ -334,14 +343,10 @@ class CurateData:
                     shutil.copyfileobj(r.raw, f)
 
         with concurrent.futures.ThreadPoolExecutor() as executor:
-            for future in executor.map(dl_pic, dict_pics_list):
+            for future in executor.map(dl_pic, pics_source_list):
                 future
 
-        with open('dict_pics_list.txt','w') as f:
-            dict_pics_list = []
-            json.dump(dict_pics_list, f)
-
-        temp_dict_pics = {k:target_dir+re.search(r'[^/]+(?=/\$_|.jpg)', k, re.IGNORECASE).group()+'.jpg' for k in dict_pics_list}
+        temp_dict_pics = {k:target_dir+re.search(r'[^/]+(?=/\$_|.jpg)', k, re.IGNORECASE).group()+'.jpg' for k in pics_source_list}
         # TODO decide if the above is necesssary at this point or if it should
         # be created at preprocessing or download
 
@@ -354,25 +359,15 @@ class CurateData:
             except ValueError:
                 json.dump(temp_dict_pics, f)
 
+        with open('pics_source_list.txt','w') as f:
+            pics_source_list = []
+            json.dump(pics_source_list, f)
+
         # TODO pipeline gameplan: 5 files: master img download dict,raw_json.txt, raw_json.csv, master_class_training.csv, master_nvl_training.csv
         # cont... open raw_json.txt and append, same with csv --> process new data --> pull out image source+dest and expand new dfs for the additional pictures
         # if not exists and append to master img download dict
         # --> concat m_class_training df and m_nvl_training dfs with new data. Need to add inclusion tests for all files when opened and appended/concatted
 
-    def update_df(self, data): # TODO save raw df as csv file
-        '''
-        Creates training instances for dataset. picture_url_list expanded to
-        max available pictures with each picture url corresponding to features
-        in common with same listing (i.e., because there are multiple pictures
-        per listing, each picture will be its own training instance.
-        '''
-        pass
-
-    # TODO You will have to mess around more with pandas df to find a better solution to creating your csv file: i.e., create dataframe from from instances, run through process to customize your df
-    # for final training set for your ml model training. Contemplate on the future... you want ability to update main csv AND training csv; one for updating raw data instances from search queries, and 
-    # the other for updating your training set. 
-
-
 def main():
     '''
     Main program creates/updates a csv file to use for ML training from live