# packages & defs ####
library(tidyverse)
library(tidymodels)
library(flexdashboard)
library(shinyWidgets)
library(bsicons)
library(fontawesome)
library(shiny)
library(bslib)
library(DT)
library(shapviz)
library(gh)
library(thematic)
library(xgboost)
library(shinyBS)

light <- bs_theme(bootswatch = "flatly")
dark <- bs_theme(bootswatch = "darkly")

thematic_shiny()
# load scripts & data ####
source("create_patient_module.R")
source("about.R")
source("funcs.R")

dummy_df <- read_csv("dummy_df.csv",show_col_types = FALSE)

# load git data ####
git_token <- Sys.getenv("GITHUB_PAT")
git_url <- Sys.getenv("GIT_URL")
tmp = tempfile()
gh(paste0('GET ', git_url), .destfile = tmp, .overwrite = TRUE, .token = git_token)
model <- xgb.load(tmp)

# ui ####
ui <- page_navbar(
  title = "POP-Pe",
  selected = "Risk Calculator",
  collapsible = TRUE,
  fillable_mobile = TRUE,
  theme = light,
  nav_panel(
    title = "About",
    about_card
  ),
  nav_panel(
    title = "Risk Calculator",
    sidebarLayout(
      sidebarPanel(
        create_patient_UI("inputs"),
        card_footer(
          materialSwitch(
            inputId = "dark_mode",
            label = "Dark mode", 
            value = FALSE,
            status = "primary"
          ),
          actionButton(
            inputId = "refresh",
            label = "Regenerate simulated patients",
            icon = icon("arrows-rotate")
          )
        )
      ),
      mainPanel(
        layout_column_wrap(
          width = 1/2,
          heights_equal = "row",
          uiOutput("risk_vb"),uiOutput("avg_vb") %>% tooltip("Estimated based on the original cohort")
        ),
        navset_card_underline(
          title = "Plots",
        #  height = "800px",
          nav_panel("Effect of variables on prediction", plotOutput("shap_plot")),
          nav_panel("Simulated patients", plotOutput("syn_plot"))
        )
      )
    )
  )
)

# server ####
server <- function(input, output, session) {
  
  shiny::observe(session$setCurrentTheme(
    if (isTRUE(input$dark_mode)) dark else light
  ))

  inputData <- create_patient_Server("inputs")
  
  output$risk_vb <- renderUI({
    p <- generate_model(inputData(), model)
    theme_zone <- case_when(
      p < 1.7 ~ "success",
      p < 10 ~ "warning",
      p >= 10 ~ "danger"
    )
    app_icon <- case_when(
      p < 1.7 ~ "emoji-smile",
      p < 10 ~ "emoji-neutral",
      p >= 10 ~ "emoji-frown"
    )
    bslib::value_box(
      title = "Risk for sustained use",
      value = paste(p,"%"),
      theme = theme_zone,
      showcase = bsicons::bs_icon(app_icon)
    ) 
  })
  
  output$avg_vb <- renderUI({
    bslib::value_box(
      title = "Average risk for cohort",
      value = "1.7%",
      theme = "light",
      showcase = bsicons::bs_icon("person-arms-up")
    ) 
  })
  
  output$shap_plot <- renderPlot({
    sv_waterfall(shapviz(model, X_pred = as.matrix(inputData())), 
                         row_id = 1,
                         fill_colors = c( "#a52c60","#f7d13d"),
                 annotation_size = 5) +
      scale_y_discrete(labels = vars_label) +
      theme_minimal(base_size = 20) +
      theme(axis.text.y = element_text(size = 15),
            legend.position = "none")
  })
  
  output$syn_plot <- renderPlot({
    syn_data <- syn_df(dummy_df, inputData(), model)
    plot_close_side(syn_data)
  })
  
  observeEvent(reactiveValuesToList(input), {
    syn_data <- syn_df(dummy_df, inputData(), model)
    output$syn_plot <- renderPlot({
      plot_close_side(syn_data)
    })
  })
  
}

shinyApp(ui, server)