Predictor.R

# load RDS and libraries 
load("20170724.RData")

# this is function to predict only

library("tidyverse")
library("stringr") 
library("textclean") 
library("lexicon")
library("magrittr")
library(shiny)
library(stringi)
 

# Server ------ ============================================

server <- function(input, output, session) { 
  
  # Function to expand contractions and convert numbers to "num" ----- 
  expand_contraction = function (text) { 
    text %<>% str_replace_all(pattern = "[`''']",replacement = "'" ) 
    text %<>% tolower()
    text %<>% str_replace_all(pattern = "\\b\\w*\\d,*\\.*\\w*,*\\.*\\b", replacement = "num") # the real regex is \b\w*\d,*\.*\w*,*\.*\b
    text %<>% str_replace_all(pattern = "\\b(num)\\W*\\S*(num)\\b", replacement = "num")
    {for (i in (seq_along(contrac_repl)))
      text %<>% str_replace_all(pattern = tolower(names(contrac_repl))[i] ,replacement = tolower(contrac_repl[i]))
    }
    # after the contractions, replace the any 's after the word 
    text %<>% str_replace_all("'s","")
    return(text)
  }
  
  # word predict function ------
  
  wordpredict <- function (phrase) {
    # pass by the dictionary and contraction
    phrase <- expand_contraction(phrase)
    #splittext <- str_split(phrase," ",simplify = T)
    splittext <- stri_split_boundaries(phrase, type="word",tokens_only = T,skip_word_none=TRUE, simplify = T)
    splittext <- ifelse(splittext %in% dictionary$word, splittext, "UNKWORD")
    splittext <- ifelse(splittext %in% badwords$word, "badword", splittext)
    lengthtext = length(splittext)
    backoff_index = ifelse(lengthtext>4,4,lengthtext)
    # pick at maximum four words
    if (lengthtext >1) {word_search_last = splittext[lengthtext]}
    if (lengthtext >2) {word_search_last_minus_one = splittext[lengthtext-1]} 
    if (lengthtext >3) {word_search_last_minus_two = splittext[lengthtext-2]} 
    if (lengthtext >4) {word_search_last_minus_three = splittext[lengthtext-3]}                    
    
    if (lengthtext >4) {fivechance = fivegrams %>% filter(third_next_word==word_search_last, sec_next_word == word_search_last_minus_one, next_word == word_search_last_minus_two, word == word_search_last_minus_three) %>% top_n(5,wt = fiveprop) %>% mutate(chance = fiveprop*0.4^(backoff_index-4), origin="fivegrams") %>% ungroup() %>% select(selection = fourth_next_word, chance, origin)}
    
    if (lengthtext >3) {fourchance = fourgrams %>% filter(sec_next_word==word_search_last, next_word==word_search_last_minus_one, word==word_search_last_minus_two) %>% top_n(5, wt=fourprop) %>% mutate(chance = fourprop*0.4^(backoff_index-3), origin = "fourgrams") %>% ungroup() %>% select(selection = third_next_word, chance, origin)}
    
    if (lengthtext >2) {threechance = trigrams %>% filter(next_word==word_search_last,word==word_search_last_minus_one) %>% top_n(5, wt=triprop) %>% mutate(chance = triprop*0.4^(backoff_index-2), origin = "trigrams") %>% ungroup() %>% select(selection = sec_next_word, chance, origin)}
    
    if (lengthtext >1) {bichance = bigrams %>% filter(word==word_search_last) %>% top_n(5, wt=biprop) %>% mutate(chance = biprop*0.4^(backoff_index-1), origin = "bigrams") %>% ungroup() %>% select(selection = next_word, chance, origin)}
    
    if (lengthtext <= 1) {unichance = unigrams %>% top_n(5, uniprop) %>% mutate(chance = uniprop*0.4^(backoff_index-1), origin = "unigram") %>% ungroup() %>% select(selection = word, chance, origin)}
    
    
    chance_final <- rbind(if(exists("fivechance")){fivechance},
                          if(exists("fourchance")){fourchance},
                          if(exists("threechance")){threechance},
                          if(exists("bichance")){bichance},
                          if(exists("unichance")){unichance})
    
    if(exists("fivechance")){rm(fivechance)} 
    if(exists("fourchance")){rm(fourchance)}
    if(exists("threechance")){rm(threechance)}
    if(exists("bichance")){rm(bichance)}
    if(exists("unichance")){rm(unichance)}
    
    ifelse(exists("chance_final"), 
               chance_final2 <- chance_final %>% group_by(selection) %>% arrange(desc(chance)) %>% top_n(1),
               chance_final2 <- unigrams %>% top_n(5, wt=uniprop))
    return(list(splittext,chance_final2,lengthtext,backoff_index))
  }
  
  results <- reactive(wordpredict(input$text_input))
  # remeber that you need to call like this 'results()'
  output$raw <- renderText(input$text_input)
  output$all <- renderPrint(results())
  output$predict <- renderTable(results()[[2]])
  output$converted <- renderText(results()[[1]])
  
  
  } #the server

# User interface ---- ================================
  
ui <- basicPage(h1("Simple predictor"),
                textInput("text_input", label = "Type here") ,
                tags$strong("user input"),
                textOutput("raw"),
                tags$strong("words converted"),
                textOutput("converted"),
                #verbatimTextOutput("all"),
                tags$strong("suggested words"),
                tableOutput("predict")
                              ) # the user interface


shinyApp(ui = ui, server = server) # this launches your app