Create connector.py

Author: raphaeljafriLB

labelpandas/connector.py

@@ -0,0 +1,24 @@
+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
+    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
+    Returns:
+        Dictinary 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"]}]}
+    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