library(shiny)
library(caret)

test_data <- readRDS('./breast_cancer_test_data.rds')
gbmFit <- readRDS('./gbm_model.rds')
preProcessor <- readRDS('./preProcessor.rds')
test_data_transformed <- predict(preProcessor, test_data)
prediction <- predict(gbmFit, newdata = test_data_transformed[,2:10], type = "prob")

inputs1 <- c("Clump Thickness" = "Cl.thickness",
            "Uniformity of Cell Size" = "Cell.size",
            "Uniformity of Cell Shape" = "Cell.shape",
            "Marginal Adhesion" = "Marg.adhesion",
            "Single Epithelial Cell Size" = "Epith.c.size",
            "Bare Nuclei" = "Bare.nuclei",
            "Bland Chromatin" = "Bl.cromatin",
            "Normal Nucleoli" = "Normal.nucleoli",
            "Mitoses" = "Mitoses")

inputs2 <- c("Uniformity of Cell Size" = "Cell.size",
             "Clump Thickness" = "Cl.thickness",
             "Uniformity of Cell Shape" = "Cell.shape",
             "Marginal Adhesion" = "Marg.adhesion",
             "Single Epithelial Cell Size" = "Epith.c.size",
             "Bare Nuclei" = "Bare.nuclei",
             "Bland Chromatin" = "Bl.cromatin",
             "Normal Nucleoli" = "Normal.nucleoli",
             "Mitoses" = "Mitoses")


# Define UI for miles per gallon app ----
ui <- fluidPage(
    
    # App title ----
    titlePanel("Breast Cancer"),
    
    # Sidebar layout with input and output definitions ----
    sidebarLayout(
        
        # Sidebar panel for inputs ----
        sidebarPanel(
            # Input: Decimal interval with step value ----
            sliderInput("threshold", "Probability Threshold:",
                        min = 0, max = 1,
                        value = 0.5, step = 0.01),
            
            # Input: Selector for variable to plot on x axis ----
            selectInput("variable_x", "Variable on X:",
                        inputs1),
            
            # Input: Selector for variable to plot on y axis ----
            selectInput("variable_y", "Variable on Y:",
                        inputs2),
        ),
        
        # Main panel for displaying outputs ----
        mainPanel(
            
            # Output: Formatted text for caption ----
            h3(textOutput("caption")),
            
            # Output: prediction outcome
            tableOutput("predictions"),
            
            # Output: Verbatim text for data summary ----
            verbatimTextOutput("summary"),
            
            # Output: Formatted text for formula ----
            h3(textOutput("formula")),
            
            # Output: Plot of the data ----
            # was  click = "plot_click"
            plotOutput("scatterPlot", brush = "plot_brush"),
            
            # Output: present click info
            tableOutput("info")
            
        )
    )
)

# Define server logic to plot various variables against mpg ----
server <- function(input, output) {
    
    # Compute the formula text ----
    # This is in a reactive expression since it is shared by the
    # output$caption and output$mpgPlot functions
    formulaText <- reactive({
        paste(input$variable_y, "~", input$variable_x)
    })

    # Compute the formula text ----
    # This is in a reactive expression since it is shared by the
    # output$caption and output$mpgPlot functions
    total_count <- reactive({
        data.frame(Class = colnames(prediction),
                   Count = c(sum(prediction$malignant<input$threshold),
                             sum(prediction$malignant>=input$threshold)))
    })
    
    # Compute the formula text ----
    # This is in a reactive expression since it is shared by the
    # output$caption and output$mpgPlot functions
    threshold_proba <- reactive({
        cbind(Prediction = ifelse(prediction$malignant>=input$threshold, 
                                  "malignant", "benign"),
              test_data)
    })
    
    # return prediction summary
    output$predictions <- renderTable({
        total_count()
    })
        
    # Return the formula text for printing as a caption ----
    output$caption <- renderText({
        "Breast cancer test data summary"
    })
    
    # Generate a summary of the dataset ----
    # The output$summary depends on the datasetInput reactive
    # expression, so will be re-executed whenever datasetInput is
    # invalidated, i.e. whenever the input$dataset changes
    output$summary <- renderPrint({
        summary(test_data)
    })
    
    # Return the formula text for printing as a caption ----
    output$formula <- renderText({
        formulaText()
    })
    
    # Generate a plot of the requested variable against mpg ----
    # and only exclude outliers if requested
    output$scatterPlot <- renderPlot({
        plot(as.formula(formulaText()), data = threshold_proba())
        #ggplot(test_data, aes(x=input$variable_x, y=input$variable_y)) + geom_point()
    })
    
    output$info <- renderTable({
        # With base graphics, need to tell it what the x and y variables are.
        #nearPoints(test_data, input$plot_click, 
        #           xvar = "Cl.thickness", yvar = "Epith.c.size")
        # nearPoints() also works with hover and dblclick events
        brushedPoints(threshold_proba(), input$plot_brush, 
                      xvar = input$variable_x, yvar = input$variable_y)
    })
    
}

# Create Shiny app ----
shinyApp(ui, server)