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index 3c24d14..5360f85 100644
--- a/materials/d1-03-performance/codealong-2.html
+++ b/materials/d1-03-performance/codealong-2.html
@@ -154,7 +154,8 @@
The project used for this particular exercise is hosted on Posit Cloud in this space. The project for this exercise is called Performance Code-along 2.
+
Requirements
The current version of our Shiny application contains a module for generating predictions of the number of LEGO parts in a set using the number of unique colors and number of unique part categories. The API is executed and processed using the {httr2} package. Here is the function wrapping the API execution:
diff --git a/search.json b/search.json
index bd93c1d..d3939f3 100644
--- a/search.json
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@@ -25,14 +25,14 @@
"href": "materials/d1-03-performance/ex-1.html",
"title": "Profile the LEGO Bricks App",
"section": "",
- "text": "The project used for this particular exercise is hosted on Posit Cloud in this space. The project for this exercise is called performance-exercise1."
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"section": "",
- "text": "The project used for this particular exercise is hosted on Posit Cloud in this space. The project for this exercise is called performance-exercise1."
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@@ -858,14 +858,21 @@
"href": "materials/d1-03-performance/codealong-2.html",
"title": "Asynchronous Processing of LEGO Model Prediction",
"section": "",
- "text": "The current version of our Shiny application contains a module for generating predictions of the number of LEGO parts in a set using the number of unique colors and number of unique part categories. The API is executed and processed using the {httr2} package. Here is the function wrapping the API execution:\n\n#' @importFrom httr2 request req_body_json req_perform resp_body_json\nrun_prediction <- function(df, endpoint_url, back_transform = TRUE, round_result = TRUE) {\n # create request object\n req <- request(endpoint_url)\n\n # perform request\n resp <- req |>\n req_body_json(df) |>\n req_perform()\n\n # extract predictions from response\n pred_values <- resp_body_json(resp)$.pred |> unlist()\n\n # back-transform log10 value of predicted number of parts if requested\n if (back_transform) {\n pred_values <- 10 ^ pred_values\n }\n\n # round result up to nearest integer if requested\n if (round_result) pred_values <- ceiling(pred_values)\n\n # append predictions to supplied data frame\n dplyr::mutate(df, predicted_num_parts = pred_values)\n}\n\nUnfortunately, the prediction API call takes a bit of time to execute due to some extremely sophisticated processing 😅. As a result, any interactions within the application will not be processed until the prediction call completes. Our goal is to convert the prediction processing from synchronous to asynchronous using {crew}"
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+ "section": "",
+ "text": "The project used for this particular exercise is hosted on Posit Cloud in this space. The project for this exercise is called Performance Code-along 2."
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- "section": "",
- "text": "The current version of our Shiny application contains a module for generating predictions of the number of LEGO parts in a set using the number of unique colors and number of unique part categories. The API is executed and processed using the {httr2} package. Here is the function wrapping the API execution:\n\n#' @importFrom httr2 request req_body_json req_perform resp_body_json\nrun_prediction <- function(df, endpoint_url, back_transform = TRUE, round_result = TRUE) {\n # create request object\n req <- request(endpoint_url)\n\n # perform request\n resp <- req |>\n req_body_json(df) |>\n req_perform()\n\n # extract predictions from response\n pred_values <- resp_body_json(resp)$.pred |> unlist()\n\n # back-transform log10 value of predicted number of parts if requested\n if (back_transform) {\n pred_values <- 10 ^ pred_values\n }\n\n # round result up to nearest integer if requested\n if (round_result) pred_values <- ceiling(pred_values)\n\n # append predictions to supplied data frame\n dplyr::mutate(df, predicted_num_parts = pred_values)\n}\n\nUnfortunately, the prediction API call takes a bit of time to execute due to some extremely sophisticated processing 😅. As a result, any interactions within the application will not be processed until the prediction call completes. Our goal is to convert the prediction processing from synchronous to asynchronous using {crew}"
+ "section": "Requirements",
+ "text": "Requirements\nThe current version of our Shiny application contains a module for generating predictions of the number of LEGO parts in a set using the number of unique colors and number of unique part categories. The API is executed and processed using the {httr2} package. Here is the function wrapping the API execution:\n\n#' @importFrom httr2 request req_body_json req_perform resp_body_json\nrun_prediction <- function(df, endpoint_url, back_transform = TRUE, round_result = TRUE) {\n # create request object\n req <- request(endpoint_url)\n\n # perform request\n resp <- req |>\n req_body_json(df) |>\n req_perform()\n\n # extract predictions from response\n pred_values <- resp_body_json(resp)$.pred |> unlist()\n\n # back-transform log10 value of predicted number of parts if requested\n if (back_transform) {\n pred_values <- 10 ^ pred_values\n }\n\n # round result up to nearest integer if requested\n if (round_result) pred_values <- ceiling(pred_values)\n\n # append predictions to supplied data frame\n dplyr::mutate(df, predicted_num_parts = pred_values)\n}\n\nUnfortunately, the prediction API call takes a bit of time to execute due to some extremely sophisticated processing 😅. As a result, any interactions within the application will not be processed until the prediction call completes. Our goal is to convert the prediction processing from synchronous to asynchronous using {crew}"
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