I keep seeing 'Not Found' when trying to load my Shiny application, it seems to be that R cannot find the Shiny server, I have tried reinstalling all packages and still no luck.

The strange thing is that it works the first time I open R Studio, then it doesn't hereafter.. spooky

---
title: "Benchmarking Algorithms for Credit Card Fraud Detection"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    social: ["menu"]
    runtime: shiny
    #options: (shiny.maxRequestSize = 1500*1024^2)
---
```{r setup, include=FALSE}
suppressPackageStartupMessages({
library(shiny)
library(flexdashboard)
library(datasets)
library(caTools)
library(hydroGOF)
})

```

Column {.tabset}
-------------------------------------

### Linear Regression Model

```{r}
ui <- fluidPage(

  titlePanel("Upload Transaction Data Set"),

  sidebarLayout(

    sidebarPanel(

      fileInput("file1", "Choose CSV File",
                multiple = FALSE,
                accept = c("text/csv",
                           "text/comma-separated-values,text/plain",
                           ".csv")),

      p("The transaction data file must contain a 'Class' column that specifies the number 1 for fraudulent transactions, and 0 otherwise"),

      p("The 'Actual' graph exhibits the real calculations of the uploaded data, and the 'predicted' graph exhibits the predicted 'Class' column based on the other features of data included in your upload.")

    ),

    mainPanel(
    h1("Models"),
    plotOutput("actual"),
    plotOutput("prediction"),
    h1("Root Mean Square Error (RMSE) Accuracy %"),
    verbatimTextOutput("accuracy"),
    h1("Summary"),
    verbatimTextOutput("summary")
    
    )
  )
)

server = function(input,output){
  options(shiny.maxRequestSize=500*1024^2)
  thedata = reactive({
  req(input$file1)
  read.csv(file = input$file1$datapath)
  })

   output$prediction = renderPlot({
   req(thedata())

   set.seed(2)

   #Split data
   split <- sample.split(thedata(), SplitRatio=0.7)
   thedata <- subset(thedata(), split=TRUE)
   actual <- subset(thedata(), split=FALSE)

   #Create the model
    model <- lm(Class ~.,data = thedata())

   #Prediction
   prediction <- predict(model, actual)
   distPred <- predict(model, actual)

   #Comparing predicted vs actual model
   plot(prediction,type = "l",lty= 1.8,col = "blue")
   lines(prediction, type = "l", col = "blue")

   })

   output$actual = renderPlot({
   req(thedata())
   set.seed(2)

   split <- sample.split(thedata()$Class,0.7)
   
   thedata <- subset(thedata(),split)
   actual <- subset(thedata(),!split)

   model <- lm(Class ~.,data = thedata())

   prediction <- predict(model, actual)
   
   plot(actual$Class,type = "l",lty= 1.8,col = "blue")

output$accuracy <- renderPrint({
    rmse <- sqrt(mean(prediction-thedata$Class)^2)/diff(range(thedata$Class))
    rmse
  })
})
   
output$summary <- renderPrint({
    model <- lm(Class ~.,data = thedata())
    summary (model)
    })
}

shinyApp(ui, server)

```

-------------------------------------

### Logistic Regression Model

```{r}
ui <- fluidPage(

  titlePanel("Upload Transaction Data Set"),

  sidebarLayout(

    sidebarPanel(

      fileInput("file1", "Choose CSV File",
                multiple = FALSE,
                accept = c("text/csv",
                           "text/comma-separated-values,text/plain",
                           ".csv")),

  p("The transaction data file must contain a 'Class' column that specifies the number 1 for fraudulent transactions, and 0 otherwise"),

  p("The 'Actual' graph exhibits the real calculations of the uploaded data, and the 'predicted' graph exhibits the predicted 'Class' column based on the other features of data included in your upload.")

    ),

  mainPanel(
    h1("Models"),
    plotOutput("actual"),
    plotOutput("prediction"),
    h1("Confusion Matrix"),
    verbatimTextOutput("confMatrix"),
    h1("Accuracy (%)"),
    verbatimTextOutput("accuracy"),
    h1("Summary"),
    verbatimTextOutput("summary")
    
    )
  )
)

server = function(input,output){
  options(shiny.maxRequestSize=500*1024^2) #Max File Size
  thedata = reactive({
  req(input$file1)
  read.csv(file = input$file1$datapath)
  })

   output$prediction = renderPlot({
   req(thedata())

   #Split Data
   split <- sample.split(thedata(), SplitRatio=0.7)
   train <- subset(thedata(), split=TRUE)
   Actual <- subset(thedata(), split=FALSE)

   #Create Model
   mymodel <- glm(Class ~., data = thedata(), family = binomial, control = list(maxit = 50))

   #Prediction
   Prediction <- predict(mymodel, Actual, type="response")

   #Predicted Model
   plot(Prediction,type = "l",lty= 1.8,col = "blue")

  })

   output$actual = renderPlot({
   req(thedata())
   
   # Split Data
   split <- sample.split(thedata(), SplitRatio=0.7)
   train <- subset(thedata(), split=TRUE)
   Actual <- subset(thedata(), split=FALSE)

   #Create Model
   mymodel <- glm(Class ~., data = thedata(), family = binomial, control = list(maxit = 50))

   #Prediction
   Prediction <- predict(mymodel, Actual, type="response")

   #Actual Model
   plot(Actual$Class,type = "l",lty= 1.8,col = "blue")
   
  output$confMatrix <- renderPrint({
    confusionMatrix <- table(Actual_Value=train$Class, Predicted_Values=Prediction > 0.5)
    confusionMatrix
    })
   
  output$accuracy <- renderPrint({
    confusionMatrix <- table(Actual_Value=train$Class, Predicted_Values=Prediction > 0.5)
    (confusionMatrix[[1,1]] + confusionMatrix[[2,2]]) / sum(confusionMatrix) *100
    })
  })

   output$summary <- renderPrint({
    mymodel <- glm(Class ~., data = thedata(), family = binomial, control = list(maxit = 50))
    summary (mymodel)
    })
}

shinyApp(ui, server)

```   

-------------------------------------

### Bayesian Model

```{r}

```

Message:

Loading required package: shiny

Listening on http://127.0.0.1:4616