Update connector.py

Author: raphaeljafriLB

labelpandas/connector.py

@@ -1,24 +1,34 @@
-def create_data_rows(local_files, lb_client, base_client, row, row_data_col, global_key_col, external_id_col, metadata_name_key_to_schema, metadata_index, divider):
-    """ Function to-be-multithreaded to create data row dictionaries from a table
+def create_data_rows(
+    local_files, 
+    lb_client, 
+    row, 
+    row_data_col, 
+    global_key_col, 
+    external_id_col, 
+    metadata_index, 
+    metadata_name_key_to_schema, 
+    metadata_schema_to_name_key,    
+    divider):
+    """ Function to-be-multithreaded to create data row dictionaries from a Pandas table
     Args:
-        local_files     :   Required (bool) - If True, will create urls for local files / If False, treats the values in `row_data_col` as urls
-        lb_client       :   Required (labelbox.client.Client) - Labelbox Client object
-        base_client     :   Required (labelbase.client.Client) - Labelbase Client object
-        row             :   Required (pandas.core.series.Series) - Pandas row object
-        row_data_col    :   Required (str) - Column name where the data row row data URL is located
-        global_key_col  :   Required (str) - Column name where the data row global key is located - defaults to the row_data column
-        external_id_col :   Required (str) - Column name where the data row external ID is located - defaults to the row_data column
-        metadata_index  :   Required (dict) - Dictionary where {key=column_name : value=metadata_type} - metadata_type must be one of "enum", "string", "datetime" or "number"
-        divider         :   Optional (str) - String delimiter to separate metadata field names from their metadata answer options in your metadata_name_key_to_schema dictionary
+        local_files                 :   Required (bool) - If True, will create urls for local files / If False, treats the values in `row_data_col` as urls
+        lb_client                   :   Required (labelbox.client.Client) - Labelbox Client object
+        row                         :   Required (pandas.core.series.Series) - Pandas row object
+        row_data_col                :   Required (str) - Column name where the data row row data URL is located
+        global_key_col              :   Required (str) - Column name where the data row global key is located - defaults to the row_data column
+        external_id_col             :   Required (str) - Column name where the data row external ID is located - defaults to the row_data column
+        metadata_index              :   Required (dict) - Dictionary where {key=column_name : value=metadata_type} - metadata_type must be one of "enum", "string", "datetime" or "number"
+        metadata_name_key_to_schema :   Required (dict) - Dictionary where {key=metadata_field_name_key : value=metadata_schema_id}
+        metadata_schema_to_name_key :   Required (dict) - Inverse of metadata_name_key_to_schema
+        divider                     :   Optional (str) - String delimiter to separate metadata field names from their metadata answer options in your metadata_name_key_to_schema dictionary
     Returns:
-        Dictinary with "row_data", "global_key", "external_id" and "metadata_fields" keys
+        Two items - the global_key, and a dictionary with "row_data", "global_key", "external_id" and "metadata_fields" keys
     """
     row_data = lb_client.upload_file(str(row[row_data_col])) if local_files else str(row[row_data_col])
-    global_key = str(row[global_key_col])
-    data_row_dict = {"row_data" : row_data, "global_key" : global_key, "external_id" : row[external_id_col], "metadata_fields" : [{"schema_id" : metadata_name_key_to_schema['lb_integration_source', "value" : "Pandas"]}]}
+    data_row_dict = {"row_data" : row_data, "global_key" : str(row[global_key_col]), "external_id" : row[external_id_col], "metadata_fields" : [{"schema_id" : metadata_name_key_to_schema['lb_integration_source', "value" : "Pandas"]}]}
     if metadata_index:
         for metadata_field_name in metadata_index.keys():
             name_key = f"{metadata_field_name}{divider}{row[metadata_field_name]}"
             value = row[metadata_field_name] if name_key not in metadata_name_key_to_schema.keys() else metadata_name_key_to_schema[name_key]
             data_row_dict['metadata_fields'].append({"schema_id" : metadata_schema_to_name_key[metadata_field_name], "value" : value})
-    return global_key, data_row_dict
+    return data_row_dict