Update app.R

app.R

@@ -43,10 +43,10 @@ ui <- fluidPage(
         tabPanel("About", 
                  HTML("
                  <h5> The following model was part of the the research article: </h5>
+                 <h4>Developing an Interpretable Machine Learning Model for the Detection of Mimosa Grazing in Goats</h4>
 
 		<p><p> <h5> You can test the app using an example dataset available <a href='https://github.com/harpomaxx/goat-behavior-model/blob/881ed7251a58a55b05d5eb3a3bc40225ba6694cb/data/split/dataset_a.tsv'  > here </a></h5>
-
-                 <h4>Developing an Interpretable Machine Learning Model for the Detection of Mimosa Grazing in Goats</h4>
+        <p><p> <h5> A dataset is already preloaded in the app for demostration purposes </a></h5>
                  
                  <em>In the last years, several machine learning approaches for detecting animal behaviors have been proposed. 
                  However, despite their successful application,  their complexity and lack of explainability have difficulty in their 
@@ -263,11 +263,13 @@ server <- function(input, output) {
   
   output$SHAPSummary <- renderPlot({
     
-    if (is.null(input$file1))
-      return(NULL)
+    file_path <- if (is.null(input$file1)) {
+      default_file_path
+    } else {
+      input$file1$datapath
+    }
     
-    inFile <- input$file1
-    dataset <- readr::read_delim(inFile$datapath,delim='\t')
+    dataset <- readr::read_delim(file_path,delim='\t')
     predictions <- predict(model, dataset)
     selected_variables <-
       readr::read_delim(
@@ -286,11 +288,13 @@ server <- function(input, output) {
   
   output$SHAPSummaryperclass <- renderPlot({
     
-    if (is.null(input$file1))
-      return(NULL)
+    file_path <- if (is.null(input$file1)) {
+      default_file_path
+    } else {
+      input$file1$datapath
+    }
     
-    inFile <- input$file1
-    dataset <- readr::read_delim(inFile$datapath,delim='\t')
+    dataset <- readr::read_delim(file_path,delim='\t')
     predictions <- predict(model, dataset)
     selected_variables <-
       readr::read_delim(
@@ -316,11 +320,12 @@ server <- function(input, output) {
   })
   output$SHAPDependency <- renderPlot({
     
-    if (is.null(input$file1))
-      return(NULL)
-    
-    inFile <- input$file1
-    dataset <- readr::read_delim(inFile$datapath,delim='\t')
+     file_path <- if (is.null(input$file1)) {
+      default_file_path
+    } else {
+      input$file1$datapath
+    }
+    dataset <- readr::read_delim(file_path,delim='\t')
     predictions <- predict(model, dataset)
     selected_variables <-
       readr::read_delim(