diff --git a/R/generate_model_assets.R b/R/generate_model_assets.R
index 7087f72..6a13762 100644
--- a/R/generate_model_assets.R
+++ b/R/generate_model_assets.R
@@ -43,24 +43,23 @@ generate_model_assets <- function(full_var_df, aws_path, model_code_link){
                                                                                                                                                                                                                                           "/variable=", full_var_df$variable[i],
                                                                                                                                                                                                                                           "/model_id=", m,
                                                                                                                                                                                                                                           "?endpoint_override=",config$endpoint,'"'),")\ndf <- all_results |> dplyr::collect()\n\n```
-       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n### Python\n\n```
-                             {python}\n# Use code below\n\nall_results\n\nimport ibis\n
-                             con = ibis.duckdbf.connect()\n\n
-                             con.raw_sql(f'''\n
-                             CREATE OR REPLACE SECRET secret (\n
-                             TYPE S3,\n
-                             ENDPOINT '",config$endpoint,"',\n
-                             URL_STYLE 'path'\n\n
-                             );/n
-                             '''\n\n
-                             path = ",
-                             paste0('"',"s3://",
-                                    aws_path,
-                                    "/project_id=", full_var_df$project_id[i],
-                                    "/duration=", full_var_df$duration[i],
-                                    "/variable=", full_var_df$variable[i],
-                                    "/model_id=", m,'"'),
-                             "\ncon.read_parquet(path + \"/**\")")
+       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n### Python\n\n```# Use code below\n\n\nimport ibis\n
+       con = ibis.duckdbf.connect()\n\n
+       con.raw_sql(f'''\n
+       CREATE OR REPLACE SECRET secret (\n
+       TYPE S3,\n
+       ENDPOINT '",config$endpoint,"',\n
+       URL_STYLE 'path'\n\n
+       );/n
+       '''\n\n
+       path = ",
+       paste0('"',"s3://",
+       aws_path,
+       "/project_id=", full_var_df$project_id[i],
+       "/duration=", full_var_df$duration[i],
+       "/variable=", full_var_df$variable[i],
+       "/model_id=", m,'"'),
+       "\ncon.read_parquet(path + \"/**\")")
     )
   )