I’m trying to follow the docs for using Vetiver to get a ML model deployed to a cloud resource but I’m having issues running my Vetiver API.

I’m using the following code but when I run the server using pr_run(port = 8080) I don’t get the full interactive endpoint that’s discussed in the example described here

pacman::p_load(tidymodels, vetiver, googleCloudRunner, ranger, pins, plumber)

# Example data
data(iris)

# Split the data
set.seed(123)
split <- initial_split(iris, prop = 0.8)
train_data <- training(split)
test_data <- testing(split)

# Define the model
model_spec <- rand_forest() %>%
  set_mode("classification") %>%
  set_engine("ranger")

# Define the workflow
workflow <- workflow() %>%
  add_model(model_spec) %>%
  add_formula(Species ~ .)

# Train the model
rf_fit <- workflow %>%
  fit(data = train_data)

# Version the model
v <- vetiver_model(rf_fit, "iris_rf")

model_board <- board_temp()

model_board %>%
  vetiver_pin_write(v)

pr() %>%
  vetiver_api(v) %>% 
  pr_run(port = 8080)

I get the following window but there’s option for interactivity. Any suggestions for resolving this would be greatly appreciated.