Add neural net report feature to EDA module

R/app_server.R

@@ -1,7 +1,7 @@
 ##########################################################
 # RTutor.AI | A Shiny app for chatting with your data.
 # Author: Xijin Ge | ge@orditus.com
-# © 2024 Orditus LLC
+# © 2025 Orditus LLC
 # No warranty & not for commercial use without a license.
 ##########################################################
 

R/app_ui.R

@@ -1,7 +1,7 @@
 ##########################################################
 # RTutor.AI | A Shiny app for chatting with your data.
 # Author: Xijin Ge | ge@orditus.com
-# © 2024 Orditus LLC
+# © 2025 Orditus LLC
 # No warranty & not for commercial use without a license.
 ##########################################################
 
@@ -25,7 +25,7 @@ app_ui <- function(request) {
     tags$footer(
       style = "position: fixed;bottom: 0;width: 100%;background-color: #F6FFF5;
         padding: 10px;text-align: center;z-index: 99;",
-      span("© 2024 Orditus LLC"),
+      span("© 2025 Orditus LLC"),
       HTML("&nbsp;"),  # Adds space
       actionLink(inputId = "ppolicy", "Privacy Policy"),
       HTML("&nbsp;"),

R/mod_10_eda.R

@@ -135,6 +135,16 @@ mod_10_eda_ui <- function(id) {
           ),
           uiOutput(ns("eda_report_ui"))
         )
+      ),
+      tabPanel(
+        title = "Neural Net Reports",
+        div(style = "margin-left: 20px;",
+          hr(class = "custom-hr-thick"),
+          h4(strong("Neural Net Analysis"),
+            style = "font-size: 24px;"
+          ),
+          uiOutput(ns("nnet_report_ui"))
+        )
       )
     )
   )
@@ -876,5 +886,228 @@ mod_10_eda_serv <- function(id, selected_dataset_name, use_python,
       }
     )
 
+
+    ##### RTutor NNet Report #####
+    # UI for Neural Network Tab
+    output$nnet_report_ui <- renderUI({
+      req(selected_dataset_name() != no_data)
+      req(!use_python())
+      req(!is.null(current_data()))
+      df <- ggpairs_data()
+
+      # Calculate the number of levels for the selected target variable
+      # (required info for binary classification)
+      num_levels <- 0
+      target_levels <- character(0)
+      if (!is.null(input$nnet_target_variable) && input$nnet_target_variable %in% colnames(df)) {
+        selected_col <- df[[input$nnet_target_variable]]
+
+        if (is.factor(selected_col)) {   # if target is a factor
+          num_levels <- length(levels(selected_col))  # number of levels
+          if (num_levels == 2) {   # if target is a binary factor
+            target_levels <- levels(selected_col)  # vector of target levels
+          }
+        }
+      }
+
+      tagList(
+        br(),
+        fluidRow(
+          column(
+            width = 3,
+            actionButton(
+              inputId = ns("render_nnet_report_rtutor"),
+              label = strong("Render Report"),
+              class = "custom-action-button"
+            )
+          )
+        ),
+        br(),
+        fluidRow(
+          column(
+            width = 6,
+            selectInput(
+              inputId = ns("nnet_target_variable"),
+              label = "Select a target variable:",
+              choices = colnames(df),
+              selected = input$nnet_target_variable,
+              multiple = FALSE
+            )
+          ),
+          if (num_levels == 2) {   # if target variable is binary factor
+            column(
+              width = 6,
+              selectInput(
+                inputId = ns("nnet_positive_class"),
+                label = "Select the positive class:",
+                choices = target_levels,
+                selected = if (length(target_levels) > 0) target_levels[1] else NULL  # Default to the first level, or handle empty list
+              )
+            )
+          }
+        ),
+        br(),
+        checkboxGroupInput(
+          inputId = ns("nnet_variables"),
+          label = "Select up to 20 predictor variables:",
+          choices = colnames(df),
+          selected = colnames(df)
+        ),
+        br(),
+        selectInput(
+          inputId = ns("nnet_size_value"),
+          label = "Select number of neurons:",
+          choices = c(2:30),
+          selected = 6,
+          multiple = FALSE
+        )
+      )
+    })
+
+    # when user uploads a file and has more than 20 columns, only the first 20 is selected by nnet_variables.
+    observeEvent(input$user_file, {
+      req(selected_dataset_name() != no_data)
+      req(input$select_data == uploaded_data)
+      req(!use_python())
+      req(!is.null(ggpairs_data()))
+      df <- ggpairs_data()
+      if(ncol(df) > max_eda_var) {
+        updateCheckboxGroupInput(
+          session = session,
+          inputId = ns("nnet_variables"),
+          label = "Deselect variables to ignore (optional):",
+          choices = colnames(df),
+          selected = colnames(df)[1:max_eda_var]
+        )
+      }
+    })
+
+    # if user selects more than 20 columns for the nnet_variables, only the first 20 is selected by nnet_variables. Show a warning.
+    observeEvent(c(input$nnet_variables, input$nnet_target_variable), {
+      req(!use_python())
+      req(!is.null(ggpairs_data()))
+
+      selected_var <- input$nnet_variables
+      update_selection <- FALSE
+      # if the selected target variable is not included in the nnet_variables, add it to the top of the list.
+      if (!(input$nnet_target_variable %in% selected_var)) {
+        selected_var <- c(input$nnet_target_variable, selected_var)
+        update_selection <- TRUE
+      }
+
+      if(length(selected_var) > max_eda_var) {
+        selected_var <- selected_var[1:max_eda_var]
+
+        showNotification(
+          ui = paste("Only the first 20 variables are selected for EDA.
+          Please deselect some variables to continue."),
+          id = "nnet_variables_warning",
+          duration = 5,
+          type = "error"
+        )
+        update_selection <- TRUE
+      }
+
+      # if target variable is selected, add to it; if too many, only keep the first 20
+      if(update_selection){
+        updateCheckboxGroupInput(
+          session = session,
+          inputId = ns("nnet_variables"),
+          label = "Deselect variables to ignore (optional):",
+          choices = colnames(ggpairs_data()),
+          selected = selected_var
+        )
+      }
+    })
+
+    nnet_file <- reactiveVal(NULL)
+
+    observeEvent(input$render_nnet_report_rtutor, {
+      req(selected_dataset_name() != no_data)
+      req(!use_python())
+      req(!is.null(current_data()))
+
+
+      withProgress(message = "Generating Report (5 minutes)", {
+        incProgress(0.2)
+        # Copy the report file to a temporary directory before processing it, in
+        # case we don't have write permissions to the current working dir (which
+        # can happen when deployed).
+        tempReport <- file.path(tempdir(), "RTutor_NNet.Rmd")
+        # tempReport
+        tempReport <- gsub("\\", "/", tempReport, fixed = TRUE)
+        output_file <- gsub("Rmd$", "html", tempReport)
+        # This should retrieve the project location on your device:
+        # "C:/Users/bdere/Documents/GitHub/idepGolem"
+        #wd <- getwd()
+
+        markdown_location <- app_sys("app/www/nnet.Rmd")
+        file.copy(from = markdown_location, to = tempReport, overwrite = TRUE)
+
+
+        # Extract negative class for binary classification
+        negative_class <- if (!is.null(input$nnet_positive_class)) {
+          setdiff(levels(ggpairs_data()[[input$nnet_target_variable]]), input$nnet_positive_class)
+        } else {
+          NULL
+        }
+
+        # Set up parameters to pass to Rmd document
+        params <- list(
+          df = ggpairs_data()[, input$nnet_variables],
+          target = input$nnet_target_variable,
+          predictors = input$nnet_variables,
+          size = input$nnet_size_value,
+          positive_class_variable = input$nnet_positive_class,
+          negative_class_variable = negative_class
+        )
+        req(params)
+        # Knit the document, passing in the `params` list, and eval it in a
+        # child of the global environment (this isolates the code in the document
+        # from the code in this app).
+        tryCatch({
+          rmarkdown::render(
+            input = tempReport, # markdown_location,
+            output_file = output_file,
+            params = params,
+            envir = new.env(parent = globalenv())
+          )
+        },
+          error = function(e) {
+            showNotification(
+              ui = paste("Error when generating the report. Please try again."),
+              id = "nnet_report_error",
+              duration = 5,
+              type = "error"
+            )
+        },
+          finally = {
+            nnet_file(output_file)
+            # show modal with download button
+            showModal(modalDialog(
+              title = "Successfully rendered the report!",
+              downloadButton(
+                outputId = ns("nnet_report_rtutor"),
+                label = "Download"
+              ),
+              easyClose = TRUE
+            ))
+          }
+        )
+      })
+    })
+
+    # Markdown report
+    output$nnet_report_rtutor <- downloadHandler(
+      # For PDF output, change this to "report.pdf"
+      filename = "RTutor_NNet.html",
+      content = function(file) {
+        validate(
+          need(!is.null(nnet_file()), "File not found.")
+        )
+        file.copy(from = nnet_file(), to = file, overwrite = TRUE)
+      }
+    )
+
   })
 }

R/mod_12_about.R

@@ -104,8 +104,8 @@ mod_12_about_ui <- function(id) {
     ),
     p(
       "Explore our other AI tools at ",
-      a("Chatlize.ai",
-        href = "https://chatlize.ai/",
+      a("Datably.ai",
+        href = "https://datably.ai/",
         target = "_blank"
       ),
       style = "font-size: 23px;padding-left: 20px;padding-right: 20px;"

R/mod_14_first_time_user_ui.r

@@ -43,8 +43,8 @@ mod_14_first_time_user_ui <- function(id) {
         ),
         h4("Also try ",
           a(
-            "Chatlize.ai,",
-            href = "https://chatlize.ai",
+            "Datably.ai,",
+            href = "https://datably.ai",
             target = "_blank"
           ),
           " a more general platform for analyzing data through chats.
@@ -97,7 +97,7 @@ mod_14_first_time_user_ui <- function(id) {
           tags$li(
             "RTutor can only analyze traditional statistics data, where
             rows are observations and columns are variables. For complex
-            data, try https://chatlize.ai."
+            data, try https://datably.ai."
           ),
           tags$li(
             "Once uploaded, your data is automatically loaded into R as a