Update app.R

app.R

@@ -1,58 +1,66 @@
 library(shiny)
-library(bslib)
-library(dplyr)
-library(ggplot2)
+library(udpipe)
 
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
+# 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")
 
-ui <- page_sidebar(
-  theme = bs_theme(bootswatch = "minty"),
-  title = "Penguins explorer",
-  sidebar = sidebar(
-    varSelectInput("xvar", "X variable", df_num, selected = "Bill Length (mm)"),
-    varSelectInput("yvar", "Y variable", df_num, selected = "Bill Depth (mm)"),
-    checkboxGroupInput("species", "Filter by species",
-      choices = unique(df$Species), selected = unique(df$Species)
+# Define UI for the application
+ui <- fluidPage(
+  titlePanel("French Readability Analyzer with UDPipe"),
+  
+  sidebarLayout(
+    sidebarPanel(
+      textInput("text", "Enter French text:", value = "", placeholder = "Type or paste French text here"),
+      actionButton("analyze", "Analyze")
     ),
-    hr(), # Add a horizontal rule
-    checkboxInput("by_species", "Show species", TRUE),
-    checkboxInput("show_margins", "Show marginal plots", TRUE),
-    checkboxInput("smooth", "Add smoother"),
-  ),
-  plotOutput("scatter")
+    
+    mainPanel(
+      h3("Readability Features"),
+      tableOutput("results")
+    )
+  )
 )
 
-server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
+# Define server logic
+server <- function(input, output) {
+  
+  analyze_text <- eventReactive(input$analyze, {
+    text <- input$text
+    
+    # Annotate text using udpipe with the French model
+    annotated <- udpipe_annotate(model, x = text)
+    annotated_df <- as.data.frame(annotated)
+    
+    # Calculate readability metrics
+    word_count <- nrow(annotated_df[annotated_df$upos == "NOUN" | annotated_df$upos == "VERB" | 
+                                      annotated_df$upos == "ADJ" | annotated_df$upos == "ADV", ])
+    
+    sentence_count <- length(unique(annotated_df$sentence_id))
+    
+    # Syllable count - count vowels in each token as an approximation
+    syllable_count <- sum(sapply(gregexpr("[aeiouyAEIOUY]", annotated_df$token), function(x) max(0, length(x))))
+    
+    # Average sentence length (words per sentence)
+    avg_sentence_length <- ifelse(sentence_count > 0, word_count / sentence_count, 0)
+    
+    # Average syllables per word
+    avg_syllables_per_word <- ifelse(word_count > 0, syllable_count / word_count, 0)
+    
+    # Compile results into a data frame
+    results <- data.frame(
+      Metric = c("Word Count", "Sentence Count", "Syllable Count", 
+                 "Average Sentence Length", "Average Syllables per Word"),
+      Value = c(word_count, sentence_count, syllable_count, 
+                round(avg_sentence_length, 2), round(avg_syllables_per_word, 2))
+    )
+    
+    return(results)
+  })
+  
+  output$results <- renderTable({
+    analyze_text()
   })
-
-  output$scatter <- renderPlot(
-    {
-      p <- ggplot(subsetted(), aes(!!input$xvar, !!input$yvar)) +
-        theme_light() +
-        list(
-          theme(legend.position = "bottom"),
-          if (input$by_species) aes(color = Species),
-          geom_point(),
-          if (input$smooth) geom_smooth()
-        )
-
-      if (input$show_margins) {
-        margin_type <- if (input$by_species) "density" else "histogram"
-        p <- p |> ggExtra::ggMarginal(
-          type = margin_type, margins = "both",
-          size = 8, groupColour = input$by_species, groupFill = input$by_species
-        )
-      }
-
-      p
-    },
-    res = 100
-  )
 }
 
-shinyApp(ui, server)
+# Run the application 
+shinyApp(ui = ui, server = server)