#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#

library(shiny)
library(shinyjs)
library(shinydashboard)
#library("wesanderson") # ayyyy lmao hipster af
library(dplyr)
library(DT)
library(ggplot2)
library(grid) # for the info box
library(plotly)
library(shinycssloaders)
library(NGLVieweR)
library(httr)
library(readr)
library(RCurl)
# make shiny non-stupid
options(shiny.launch.browser = FALSE) # i am a big girl and can tie my own laces
options(shiny.port = 8000) # don't change the port every time
options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces"
options(DT.options = list(scrollX = TRUE))


# An "application token" is required here. I generated this one like this:
# curl -H "Content-Type: application/json" -d '{"name":"r-data"}' -u <username>:<password> https://git.tunstall.in/api/v1/users/sethp/tokens

# Gitea access token
access_token = read_lines("~/secret-token")

gene_url = "https://git.tunstall.in/api/v1/repos/tanu/fellowship_dcdf/raw/tb_data_fc/dashboard/list_unique_missense_genes.csv"
missense_gene_url = "https://git.tunstall.in/api/v1/repos/tanu/fellowship_dcdf/raw/tb_data_fc/dashboard/missense_genes_params.csv"
alphafold_url = "https://alphafold.ebi.ac.uk/files/"

genes=read_csv(paste0(gene_url,"?token=",access_token))
unique_missense_genes = read_delim(paste0(missense_gene_url,"?token=",access_token))
#genes = c("Gene 1", "Gene 2", "Gene 3", "Gene 4", "Gene 5")

# Define UI for application that draws a histogram
ui=dashboardPage(skin="purple",
                 dashboardHeader(title="Tuberculosis Host"),
                 dashboardSidebar(
                   radioButtons("gene",
                                label="Gene",
                                choices = genes$Gene,
                                selected="alr" # "alr" is a value
                   ),
                   actionButton("clear_ngl",
                                "Reset Structures"),
                   actionButton("all_mutations",
                                "All Mutations")
                 ),
                 dashboardBody(
                   useShinyjs(),
                   fluidRow(
                     box(
                       title="Crystallised Structure",
                       width=5,
                       column(
                         NGLVieweROutput("structure"),
                         width=12
                       ) 
                     ),
                     box(
                       title="AlphaFold Structure",
                       width=5,
                       column(
                         NGLVieweROutput("af_structure"),
                         width=12
                       )
                     ),
                     box(
                       title="Mutation Information",
                       width=2,
                       column(
                         htmlOutput("information"),
                         width=12
                       )
                     )
                   ),
                   fluidRow(
                     column(
                       DT::dataTableOutput('table'),
                       width=12
                     )
                   ),
                   verbatimTextOutput("debug")
                 )
)

# Define server logic required to draw a histogram
server <- function(input, output) {
  
  output$distPlot <- renderPlot({
    # generate bins based on input$bins from ui.R
    x    <- faithful[, 2]
    bins <- seq(min(x), max(x), length.out = input$bins + 1)
    
    # draw the histogram with the specified number of bins
    hist(x, breaks = bins, col = 'darkgray', border = 'white',
         xlab = 'Waiting time to next eruption (in mins)',
         main = 'Histogram of waiting times')
  })
  ### NGLViewer ####
  # Structure Viewer WebGL/NGLViewR window
  output$structure <- renderNGLVieweR({
    selected_gene=input$gene
    ngl_gene=as.character(genes[genes$Gene==selected_gene,"PDB"])
    NGLVieweR(ngl_gene) %>%
      addRepresentation("cartoon",
                        param = list(name = "cartoon",
                                     color="tan",
                                     #colorScheme = "bfactor",
                                     opacity = 1
                        )
      ) %>%
      stageParameters(backgroundColor = "white") %>%
      setQuality("high") %>%
      setFocus(0) %>%
      setSpin(FALSE)
  })
  output$af_structure <- renderNGLVieweR({
    selected_af_gene=input$gene
    ngl_af_gene=as.character(genes[genes$Gene==selected_af_gene,"AF_PDB"])
    
    #af_pdb=cat(content(GET(paste0(alphafold_url,"AF-",ngl_af_gene,"-F1-model_v4.pdb")), as="text"), "\n")
    af_pdb=content(GET(paste0(alphafold_url,"AF-",ngl_af_gene,"-F1-model_v4.pdb")), as="text")
    #print(af_pdb)
    #ngl_pdb_file=paste0(load_dir, "Data/", ngl_drug, '/output/depth/', ngl_gene, '_complex.pdb')
    NGLVieweR(af_pdb, format="pdb") %>%
      addRepresentation("cartoon",
                        param = list(name = "cartoon",
                                     #color="tan"
                                     colorScheme = "bfactor",
                                     opacity = 1
                        )
      ) %>%
      stageParameters(backgroundColor = "white") %>%
      setQuality("high") %>%
      setFocus(0) %>%
      setSpin(FALSE)
  })
  
  # output$table <- DT::renderDataTable({
  #   selected_gene=input$gene
  #   gene=as.character(genes[genes$Gene==selected_gene,"Gene"])
  #   #unique_missense_genes[unique_missense_genes$gene_name==gene,]
  #   unique_missense_genes
  # },
  # selection = "single",
  # search = list(search="alr"))
  output$table <- DT::renderDataTable(
    unique_missense_genes,
    selection = "single",
    options=list(
      search = list(search=as.character(genes[genes$Gene==input$gene,"Gene"]))
    )
  )
  
  observeEvent(input$table_rows_selected,{
    #req(length(input$table_row_selected) > 0)
    mutation = as.character(unique_missense_genes[input$table_rows_selected,"hgvd_p"])
    chain = as.character(unique_missense_genes[input$table_rows_selected,"chain"])
    # what the absolute FUCK is this mess? I JUST WANT THE MATCH ASDLASJASDASHFKLJASDFK
    # coming down from the trees was a mistake, and abandoning Perl doubly so.
    clicked_position = regmatches(mutation, regexpr("[0-9]+", mutation, perl=TRUE))
    
    # Now update the 3D structure to highlight the clicked thing and then zoom in.
    
    NGLVieweR_proxy("structure") %>%
      #addSelection('ball+stick'
      addSelection('spacefill'
                   , param = list(
                     name = "Pos"
                     , sele = trimws(paste0(clicked_position,':', chain))
                     , color = "red"
                     #, colorValue="00ff00"
                     #, colorScheme="element"
                   )
      )
    NGLVieweR_proxy("af_structure") %>%
      #addSelection('ball+stick'
      addSelection('spacefill'
                   , param = list(
                     name = "Pos"
                     , sele = clicked_position
                     , color = "red"
                     #, colorValue="00ff00"
                     #, colorScheme="element"
                   )
      )
    
    NGLVieweR_proxy("af_structure") %>% updateZoomMove(
      center = clicked_position,
      zoom = clicked_position,
      duration = 1000, # animation time in ms
      z_offSet = -1
    )
    NGLVieweR_proxy("structure") %>% updateZoomMove(
      center = trimws(paste0(clicked_position,':', chain)),
      zoom = trimws(paste0(clicked_position,':', chain)),
      duration = 1000, # animation time in ms
      z_offSet = -1
    )
    output$information <- renderUI({
      selected_gene=input$gene
      HTML(paste0("<strong>Mutation: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mutationinformation"]),"<br/>",
                  "<strong>PDB ID: </strong>", as.character(genes[genes$Gene==selected_gene,"PDB"]),"<br/>",
                  "<strong>AlphaFold ID: </strong>", as.character(genes[genes$Gene==selected_gene,"AF_PDB"]), "<br/>",
                  "<strong>Ligand Distance: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ligand_distance"]), "<br/>",
                  "<strong>mCSM Lig: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mcsm_lig"]), "<br/>",
                  "<strong>mmCSM Lig: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mmcsm_lig"]), "<br/>",
                  "<strong>mCSM DUET: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_duet"]), "<br/>",
                  "<strong>FoldX: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_foldx"]), "<br/>",
                  "<strong>DeepDDG: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_deepddg"]), "<br/>",
                  "<strong>Dynamut2: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_dynamut2"]), "<br/>",
                  "<strong>mCSM PPI2: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mcsm_ppi2_affinity"]), "<br/>",
                  "<strong>Interface Distance: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"interface_dist"]), "<br/>",
                  "<strong>mCSM NA: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mcsm_na_affinity"]), "<br/>",
                  "<strong>Consurf: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"consurf_score"]), "<br/>",
                  #"<strong>SNAP2: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"snap2_score"]), "<br/>",
                  "<strong>SNAP2 Outcome: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"snap2_outcome"]), "<br/>",
                  #"<strong>PROVEAN: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"provean_score"]), "<br/>",
                  "<strong>PROVEAN Outcome: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"provean_outcome"]), "<br/>",
                  "<strong>RSA: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"rsa"]), "<br/>",
                  "<strong>Residue Depth: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"rd_values"]), "<br/>"
      )
      )
      
    })
    
    # output$debug <- renderPrint({
    #   print(c(mutation, clicked_position))
    # })
  })
  observeEvent(
    {
      input$clear_ngl
    }, {
      NGLVieweR_proxy("structure") %>%
        removeSelection("Pos") %>%
        removeSelection("all_mutations_surface") %>%
        updateVisibility("cartoon", TRUE) %>%
        removeSelection("all_mutations")
      NGLVieweR_proxy("af_structure") %>%
        removeSelection("Pos") %>%
        removeSelection("all_mutations_surface") %>%
        updateVisibility("cartoon", TRUE) %>%
        removeSelection("all_mutations")
    })
  # add a surface representation and highlight all mutations on it
  observeEvent(
    {
      input$all_mutations
    }, {
      gene = input$gene
      mutations = paste0(":",
                         as.matrix(unique_missense_genes[unique_missense_genes$gene_name == gene,c("chain")])[1],
                         " and (",
                         paste0(
                           apply(
                             unique_missense_genes[unique_missense_genes$gene_name == gene,c("position","chain")],
                             1,
                             function(x){
                               paste0(trimws(x[1])
                               )}
                           ),
                           collapse=", "
                         ), ")"
      )
      print(mutations)
      
      
      NGLVieweR_proxy("structure") %>%
        updateVisibility("cartoon", FALSE) %>%
        addSelection(type="surface",
                     param = list(name = "all_mutations_surface",
                                  sele = "all",
                                  color="tan",
                                  opacity = 0.2
                     )
        ) %>%
        addSelection(type="surface",
                     param = list(name = "all_mutations",
                                  color="red",
                                  sele = mutations
                                  #colorScheme = "bfactor",
                                  #opacity = 1
                     )
        )
      NGLVieweR_proxy("af_structure") %>%
        updateVisibility("cartoon", FALSE) %>%
        addSelection(type="surface",
                     param = list(name = "all_mutations_surface",
                                  sele = "all",
                                  color="tan",
                                  opacity = 0.2
                     )
        ) %>%
        addSelection(type="surface",
                     param = list(name = "all_mutations",
                                  color="red",
                                  sele = mutations
                                  #colorScheme = "bfactor",
                                  #opacity = 1
                     )
        )
    })
}

# Run the application 
shinyApp(ui = ui, server = server)