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Update app.R
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app.R
CHANGED
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@@ -125,7 +125,7 @@ ui <- fluidPage(
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selected = unique(sm$application)[1]),
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selectInput("model", "Model",
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choices = unique(sm$optimizeImageRep),
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selected = "clip"),
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########################################################################
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# Use our named vector 'metric_choices' directly in selectInput
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@@ -265,7 +265,7 @@ server <- function(input, output) {
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if (input$compareToBest) {
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z <- grouped_data$improvement
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main_title <- paste(input$application, "-", chosen_metric_label, "
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} else {
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z <- grouped_data$mean_metric
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main_title <- paste(input$application, "-", chosen_metric_label)
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@@ -275,6 +275,7 @@ server <- function(input, output) {
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y <- grouped_data$MaxImageDimsRight
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zlim <- range(z, na.rm = TRUE)
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customPalette <- colorRampPalette(c("blue", "white", "red"))(50)
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heatMap(
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x = x,
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selected = unique(sm$application)[1]),
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selectInput("model", "Model",
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choices = unique(sm$optimizeImageRep),
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selected = "clip-rsicd"),
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########################################################################
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# Use our named vector 'metric_choices' directly in selectInput
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if (input$compareToBest) {
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z <- grouped_data$improvement
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main_title <- paste(input$application, "-", chosen_metric_label, "\n Improvement Over Best Single Scale")
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} else {
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z <- grouped_data$mean_metric
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main_title <- paste(input$application, "-", chosen_metric_label)
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y <- grouped_data$MaxImageDimsRight
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zlim <- range(z, na.rm = TRUE)
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par(mar=c(5,5,5,1))
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customPalette <- colorRampPalette(c("blue", "white", "red"))(50)
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heatMap(
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x = x,
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