Update to pins

1_train_model.R

@@ -1,6 +1,6 @@
 # https://blogs.rstudio.com/tensorflow/posts/2018-01-29-dl-for-cancer-immunotherapy/
 
-Sys.unsetenv("RETICULATE_PYTHON")
+# Sys.unsetenv("RETICULATE_PYTHON")
 
 suppressPackageStartupMessages({
   library(tidyverse)
@@ -144,4 +144,4 @@ results %>%
 
 # And this is the recommended way
 model %>%
-  save_model_tf("saved_models")
+  save_model_tf("saved_model")

2_publish_model.R

@@ -1,9 +1,17 @@
-library(rsconnect)
+library(pins)
+library(keras)
 
-rsconnect::deployTFModel(
-  modelDir = "saved_models",
-  # server = "{server}",     # <<- edit this line if necessary
-  # account = "{account}",   # <<- edit this line if necessary
-  appTitle = "immunotherapy",
-  forceUpdate = TRUE
+# Pin it on RStudio Connect
+pins::board_register_rsconnect()
+pins::pin(
+  "saved_model",
+  "peptide_model",
+  "Peptide Prediction Model",
+  "rsconnect",
+  zip = TRUE
 )
+
+mod_pinned <- pins::pin_get("peptide_model")
+utils::unzip(mod_pinned, exdir = fs::path_dir(mod_pinned))
+
+mod <- keras::load_model_tf(file.path(fs::path_dir(mod_pinned), "saved_model"))

plumber/plumber.R

@@ -10,29 +10,42 @@
 library(plumber)
 library(config)
 library(reticulate)
+library(PepTools)
 
-source("3_consume_tf_api.R")
+# Get pinned model from RStudio Connect
+pins::board_register_rsconnect(
+  server = config::get("rsc_url"),
+  key = config::get("rsc_api_key")
+)
+mod_pinned <- pins::pin_get("peptide_model")
+utils::unzip(mod_pinned[1], exdir = fs::path_dir(mod_pinned[1]))
+mod <- keras::load_model_tf(file.path(fs::path_dir(mod_pinned[1]), "saved_model"))
 
 #* @apiTitle Immunotherapy
 
 #* Predict peptide class
 #* @param peptide Character vector with a single peptide, eg. `"LLTDAQRIV"` or comma separated, e.g. `"LLTDAQRIV, LMAFYLYEV, VMSPITLPT, SLHLTNCFV, RQFTCMIAV"`
 #* @get /predict
-function(peptide, solo_url){
-  if (missing(solo_url) || is.null(solo_url)) {
-    solo_url <- config::get("solo_url_tensorflow") # TensorFlow API
-  }
+function(peptide){
+  # Peptide classes for prediction
+  peptide_classes <- c("NB", "WB", "SB")
 
   # split on commas and remove white space
   peptide <- trimws(strsplit(peptide, ",")[[1]])
 
-  predict_peptide_class_fun(peptide = peptide, solo_url = solo_url)
+  # transform input into flattened array
+  x_val <-
+    peptide %>%
+    pep_encode() %>%
+    array_reshape(dim = c(nrow(.), 9*20))
+
+  # Get predictions from models
+  preds <- mod %>%
+    keras::predict_classes(x_val)
+
+  # Return original peptides with predictions
+  tibble::tibble(
+    peptide = peptide,
+    peptide_classes = peptide_classes[preds]
+  )
 }
-
-
-
-# peptide <- "LLTDAQRIV"
-
-# source("3_consume_tf_api.R")
-# solo_url <- config::get("solo_url_tensorflow")
-# predict_peptide_class_fun(peptide = "LLTDAQRIV", solo_url = solo_url)

plumber/rsconnect/colorado.rstudio.com/alex.gold/immunotherapy_api.dcf

@@ -0,0 +1,12 @@
+name: immunotherapy_api
+title: Immunotherapy API
+username: alex.gold
+account: alex.gold
+server: colorado.rstudio.com
+hostUrl: https://colorado.rstudio.com/rsc/__api__
+appId: 7295
+bundleId: 40050
+url: https://colorado.rstudio.com/rsc/content/7295/
+when: 1598468216.99496
+asMultiple: FALSE
+asStatic: FALSE