Update app.R

app.R

@@ -2,6 +2,7 @@ library(shiny)
 library(udpipe)
 library(stringr)
 library(ggplot2)
+library(reshape2)
 
 # Load the French bsd model (ensure it's downloaded and adjust path if necessary)
 model <- udpipe_load_model("french-gsd-ud-2.5-191206.udpipe")
@@ -17,7 +18,9 @@ ui <- fluidPage(
       textAreaInput("text", "Or enter French text directly:", value = "", 
                     placeholder = "Type or paste French text here", 
                     width = '100%', height = '200px', resize = "both"),
-      actionButton("analyze", "Analyze")
+      actionButton("analyze", "Analyze"),
+      tags$div(id = "progress", style = "display: none; margin-top: 10px;"),
+      tags$style("#progress {font-size: 14px; color: #337ab7;}")
     ),
     
     mainPanel(
@@ -96,11 +99,23 @@ server <- function(input, output, session) {
       unzip(input$corpus_zip$datapath, exdir = temp_dir)
       txt_files <- list.files(temp_dir, pattern = "\\.txt$", full.names = TRUE)
       
-      # Calculate metrics for each text file and store in a list
-      corpus_metrics <- lapply(txt_files, function(file) {
-        text <- readLines(file, warn = FALSE)
-        calculate_metrics(paste(text, collapse = " "))
-      })
+      corpus_metrics <- list()
+      n_files <- length(txt_files)
+      
+      # Show progress to the user
+      updateProgress <- function(value) {
+        session$sendCustomMessage("updateProgress", list(value = value))
+      }
+      updateProgress(0)
+      
+      # Calculate metrics for each text file
+      for (i in seq_along(txt_files)) {
+        text <- readLines(txt_files[i], warn = FALSE)
+        corpus_metrics[[i]] <- calculate_metrics(paste(text, collapse = " "))
+        
+        # Update progress
+        updateProgress(i / n_files * 100)
+      }
       
       # Combine metrics into a data frame
       corpus_metrics_df <- do.call(rbind, corpus_metrics)
@@ -115,14 +130,15 @@ server <- function(input, output, session) {
     }
   })
   
-  # Display box plots for corpus mode
+  # Display box plots for corpus mode, using facets for individual scales
   output$corpusPlots <- renderPlot({
     if (!is.null(results()) && results()$isCorpus) {
       corpus_metrics_df <- results()$data
-      melted_df <- reshape2::melt(corpus_metrics_df)
+      melted_df <- melt(corpus_metrics_df)
       
       ggplot(melted_df, aes(x = variable, y = value)) +
         geom_boxplot() +
+        facet_wrap(~ variable, scales = "free_y") +
         labs(x = "Metric", y = "Value", title = "Corpus Analysis - Readability and Cohesion Metrics") +
         theme_minimal() +
         theme(axis.text.x = element_text(angle = 45, hjust = 1))
@@ -134,7 +150,28 @@ server <- function(input, output, session) {
     !is.null(results()) && results()$isCorpus
   })
   outputOptions(output, "isCorpus", suspendWhenHidden = FALSE)
+  
+  # JavaScript for updating progress bar
+  session$sendCustomMessage("updateProgress", list(value = 0))
+  observe({
+    session$sendCustomMessage("addProgress", "")
+  })
+  
 }
 
+# JavaScript for updating progress bar
+js <- "
+Shiny.addCustomMessageHandler('updateProgress', function(message) {
+  if (message.value === 0) {
+    $('#progress').show().html('Processing... 0%');
+  } else if (message.value === 100) {
+    $('#progress').html('Processing complete!').delay(1000).fadeOut();
+  } else {
+    $('#progress').html('Processing... ' + Math.round(message.value) + '%');
+  }
+});
+"
+tags$head(tags$script(HTML(js)))
+
 # Run the application 
 shinyApp(ui = ui, server = server)