diff --git a/.gitignore b/.gitignore
index e75435c..1d90092 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,3 +47,6 @@ po/*~
 
 # RStudio Connect folder
 rsconnect/
+
+# api file
+api.R
\ No newline at end of file
diff --git a/forecasting_tool/functions.R b/forecasting_tool/functions.R
new file mode 100644
index 0000000..a91caaa
--- /dev/null
+++ b/forecasting_tool/functions.R
@@ -0,0 +1,132 @@
+#### mode ####
+getmode <- function(v, na = TRUE) {
+  if(na == TRUE){
+    v <- v[!is.na(v)]
+  }
+  uniqv <- unique(v)
+  uniqv[which.max(tabulate(match(v, uniqv)))]
+}
+
+#### Forecasting functions ####
+#### LSTM ####
+lstm_forecast <- function(ts_data, horizon) {
+  library(keras)
+  
+  # Normalize the data
+  normalized_data <- scale(ts_data)
+  
+  # Split data into train and test sets
+  train_data <- normalized_data[1:(length(normalized_data) - horizon)]
+  test_data <- normalized_data[(length(normalized_data) - horizon + 1):length(normalized_data)]
+  
+  # Prepare the training data
+  train_x <- train_y <- list()
+  for (i in 1:(length(train_data) - horizon)) {
+    train_x[[i]] <- matrix(train_data[i:(i + horizon - 1)], nrow = horizon, ncol = 1)
+    train_y[[i]] <- train_data[(i + horizon)]
+  }
+  
+  train_x <- array_reshape(train_x, c(length(train_x), horizon, 1))
+  train_y <- unlist(train_y)
+  
+  # Define the LSTM model architecture
+  model <- keras_model_sequential()
+  model %>%
+    layer_lstm(units = 50, input_shape = c(horizon, 1)) %>%
+    layer_dense(units = 1)
+  
+  # Compile the model
+  model %>% compile(
+    loss = "mean_squared_error",
+    optimizer = optimizer_adam()
+  )
+  
+  # Train the model
+  model %>% fit(
+    train_x, train_y,
+    epochs = 100,
+    batch_size = 32,
+    verbose = 0
+  )
+  
+  # Make predictions for the test set
+  test_x <- array_reshape(test_data, dim = c(length(test_data) / horizon, horizon, 1))
+  predicted_values <- model %>% predict(test_x)
+  
+  # Denormalize the predicted values
+  denormalized_values <- predicted_values * sd(ts_data) + mean(ts_data)
+  
+  # Create the forecast object
+  forecast_values <- ts(denormalized_values, frequency = 12)
+  
+  return(forecast_values)
+}
+
+#### AutoML ####
+automl_forecast <- function(ts_data, horizon) {
+  # Convert the time series data to a data frame
+  data_df <- data.frame(Date = as.numeric(time(ts_data)), Value = as.numeric(ts_data))
+  
+  # Initialize the H2O cluster
+  h2o.init()
+  
+  # Convert the data frame to an H2O frame
+  h2o_df <- as.h2o(data_df, destination_frame = "ts_data")
+  
+  # Set the target variable
+  target <- "Value"
+  
+  # Train AutoML model
+  aml <- h2o.automl(x = setdiff(colnames(h2o_df), target),
+                    y = target,
+                    training_frame = h2o_df,
+                    max_runtime_secs = 300,
+                    max_models = 10)
+  
+  # Generate predictions for the future horizon
+  forecast_df <- data.frame(Date = seq(time(ts_data)[length(ts_data)] + 1/12, by = 1/12, length.out = horizon))
+  forecast_h2o <- as.h2o(forecast_df, destination_frame = "forecast_data")
+  forecast_predictions <- h2o.predict(aml@leader, forecast_h2o)
+  
+  # Convert the predictions to a time series object
+  forecast_values <- ts(as.vector(forecast_predictions$predict), frequency = 12)
+  
+  # Shut down the H2O cluster
+  h2o.shutdown(prompt = FALSE)
+  
+  return(forecast_values)
+}
+
+#### ARFIMA Forecast ####
+arfima_forecast <- function(x, h){
+  {
+    arfima_fit <- auto.arima(x, seasonal = FALSE, stepwise = FALSE, 
+                             approximation = FALSE, allowdrift = FALSE)
+    
+    # Extract AR and MA orders from the fitted model
+    ar_order <- arimaorder(arfima_fit)[1]  # AR order
+    ma_order <- arimaorder(arfima_fit)[3]  # MA order
+    
+    # Create dynamic ARFIMA specification
+    spec <- arfimaspec(mean.model = list(armaOrder = c(ar_order, ma_order),
+                                         include.mean = TRUE, arfima = TRUE))
+    
+    result <- try({
+      # Fit the model using the dynamic specification
+      garch_fit <- arfimafit(spec = spec, data = x)
+      
+      # Generate forecasts directly using arfimaforecast
+      forecast_values <- arfimaforecast(garch_fit, n.ahead = h)
+    }, silent = TRUE)
+    
+    if (inherits(result, "try-error")) {
+      forecast_vector <- rep(0, times = h)
+    } else {
+      # Extract the forecasted values as a vector
+      forecast_vector <- as.vector(forecast_values@forecast$seriesFor)
+    }
+    
+    
+    return(forecast_vector)
+  }
+}
\ No newline at end of file
diff --git a/forecasting_tool/global.R b/forecasting_tool/global.R
index 3530432..eabd0a6 100644
--- a/forecasting_tool/global.R
+++ b/forecasting_tool/global.R
@@ -46,6 +46,9 @@ library(tsfgrnn)
 library(fontawesome)
 
 source("helper.R")
+source("theme.R")
+source("api.R")
+source("functions.R")
 
 # js scroll code ####
 jscode_1 <- '
@@ -55,220 +58,6 @@ jscode_1 <- '
       }
     '
 
-# API ####
-openai_api_key <<- ""
-gemini_api_key <<- ""
-claude_api_key <<-
-  ""
-hugging_api_key <<- ""
-
 #### data edit ####
 data_edit <<- data.frame(row = NA, col = NA, value = NA)
 
-#### dashboard sidebar theme ####
-dashboard_sidebar_theme <- create_theme( 
-  adminlte_sidebar(
-    # dark_bg = "#006666",
-    # dark_hover_bg = "#202020"
-  )
-)
-
-#### dashboard theme ####
-dashboard_body_theme <- create_theme(
-  theme = "cosmo",
-  bs_vars_button(
-    default_color = "#FFF",
-    default_bg = "#0066cc",
-    # default_border = "white",
-    border_radius_base = "10px"
-  ),
-  bs_vars_tabs(
-    border_color = "black",
-    active_link_hover_bg = "#CCE5FF"
-  ),
-  output_file = NULL
-)
-
-#### user panel theme ####
-myDashboardUser <- function (..., name = NULL, image = NULL, title = NULL, subtitle = NULL, 
-                             footer = NULL) 
-{
-  if (!is.null(title)) {
-    line_1 <- paste0(name, " - ", title)
-  }
-  else {
-    line_1 <- name
-  }
-  if (!is.null(subtitle)) {
-    user_text <- shiny::tags$p(line_1, shiny::tags$small(subtitle))
-    user_header_height <- NULL
-  }
-  else {
-    user_text <- shiny::tags$p(line_1)
-    user_header_height <- shiny::tags$script(
-      shiny::HTML("$(\".user-header\").css(\"height\", \"145px\")")
-    )
-  }
-  userTag <- shiny::tagList(
-    shiny::tags$head(
-      shiny::tags$script("$(function() {\n
-                           $('.dashboard-user').on('click', function(e){\n
-                           e.stopPropagation();\n
-                           });\n
-                           });\n
-                           ")),
-    # we need to add an id and the class `action-button` to this link
-    shiny::tags$a(id = "user_dropdown",
-                  href = "#",
-                  class = "dropdown-toggle action-button",
-                  `data-toggle` = "dropdown",
-                  shiny::tags$img(src = image,
-                                  class = "user-image",
-                                  alt = "User Image"),
-                  shiny::tags$span(class = "hidden-xs",
-                                   name)
-    ),
-    shiny::tags$ul(class = "dropdown-menu dashboard-user", 
-                   shiny::tags$li(class = "user-header",
-                                  if (!is.null(user_header_height)) user_header_height,
-                                  shiny::tags$img(src = image, 
-                                                  class = "img-circle",
-                                                  alt = "User Image",
-                                                  style="border:red"),
-                                  user_text,
-                                  style="background:#0066cc"), 
-                   if (length(list(...)) > 0) 
-                     shiny::tags$li(class = "user-body", ...),
-                   if (!is.null(footer)) 
-                     shiny::tags$li(class = "user-footer", footer)
-    )
-  )
-  userTag
-}
-
-
-
-#### mode ####
-getmode <- function(v, na = TRUE) {
-  if(na == TRUE){
-    v <- v[!is.na(v)]
-  }
-  uniqv <- unique(v)
-  uniqv[which.max(tabulate(match(v, uniqv)))]
-}
-
-#### Forecasting functions ####
-#### LSTM ####
-lstm_forecast <- function(ts_data, horizon) {
-  library(keras)
-  
-  # Normalize the data
-  normalized_data <- scale(ts_data)
-  
-  # Split data into train and test sets
-  train_data <- normalized_data[1:(length(normalized_data) - horizon)]
-  test_data <- normalized_data[(length(normalized_data) - horizon + 1):length(normalized_data)]
-  
-  # Prepare the training data
-  train_x <- train_y <- list()
-  for (i in 1:(length(train_data) - horizon)) {
-    train_x[[i]] <- matrix(train_data[i:(i + horizon - 1)], nrow = horizon, ncol = 1)
-    train_y[[i]] <- train_data[(i + horizon)]
-  }
-  
-  train_x <- array_reshape(train_x, c(length(train_x), horizon, 1))
-  train_y <- unlist(train_y)
-  
-  # Define the LSTM model architecture
-  model <- keras_model_sequential()
-  model %>%
-    layer_lstm(units = 50, input_shape = c(horizon, 1)) %>%
-    layer_dense(units = 1)
-  
-  # Compile the model
-  model %>% compile(
-    loss = "mean_squared_error",
-    optimizer = optimizer_adam()
-  )
-  
-  # Train the model
-  model %>% fit(
-    train_x, train_y,
-    epochs = 100,
-    batch_size = 32,
-    verbose = 0
-  )
-  
-  # Make predictions for the test set
-  test_x <- array_reshape(test_data, dim = c(length(test_data) / horizon, horizon, 1))
-  predicted_values <- model %>% predict(test_x)
-  
-  # Denormalize the predicted values
-  denormalized_values <- predicted_values * sd(ts_data) + mean(ts_data)
-  
-  # Create the forecast object
-  forecast_values <- ts(denormalized_values, frequency = 12)
-  
-  return(forecast_values)
-}
-
-#### AutoML ####
-automl_forecast <- function(ts_data, horizon) {
-  # Convert the time series data to a data frame
-  data_df <- data.frame(Date = as.numeric(time(ts_data)), Value = as.numeric(ts_data))
-  
-  # Initialize the H2O cluster
-  h2o.init()
-  
-  # Convert the data frame to an H2O frame
-  h2o_df <- as.h2o(data_df, destination_frame = "ts_data")
-  
-  # Set the target variable
-  target <- "Value"
-  
-  # Train AutoML model
-  aml <- h2o.automl(x = setdiff(colnames(h2o_df), target),
-                    y = target,
-                    training_frame = h2o_df,
-                    max_runtime_secs = 300,
-                    max_models = 10)
-  
-  # Generate predictions for the future horizon
-  forecast_df <- data.frame(Date = seq(time(ts_data)[length(ts_data)] + 1/12, by = 1/12, length.out = horizon))
-  forecast_h2o <- as.h2o(forecast_df, destination_frame = "forecast_data")
-  forecast_predictions <- h2o.predict(aml@leader, forecast_h2o)
-  
-  # Convert the predictions to a time series object
-  forecast_values <- ts(as.vector(forecast_predictions$predict), frequency = 12)
-  
-  # Shut down the H2O cluster
-  h2o.shutdown(prompt = FALSE)
-  
-  return(forecast_values)
-}
-
-#### ARFIMA FOrecast ####
-arfima_forecast <- function(x, h){
-  {
-    arfima_fit <- auto.arima(x, seasonal = FALSE, stepwise = FALSE, 
-                             approximation = FALSE, allowdrift = FALSE)
-    
-    # Extract AR and MA orders from the fitted model
-    ar_order <- arimaorder(arfima_fit)[1]  # AR order
-    ma_order <- arimaorder(arfima_fit)[3]  # MA order
-    
-    # Create dynamic ARFIMA specification
-    spec <- arfimaspec(mean.model = list(armaOrder = c(ar_order, ma_order),
-                                         include.mean = TRUE, arfima = TRUE))
-    
-    # Fit the model using the dynamic specification
-    garch_fit <- arfimafit(spec = spec, data = x)
-    
-    # Generate forecasts directly using arfimaforecast
-    forecast_values <- arfimaforecast(garch_fit, n.ahead = h)
-    
-    # Extract the forecasted values as a vector
-    forecast_vector <- as.vector(forecast_values@forecast$seriesFor)
-    return(forecast_vector)
-  }
-}
\ No newline at end of file
diff --git a/forecasting_tool/helper.R b/forecasting_tool/helper.R
index 08dd73b..729453f 100644
--- a/forecasting_tool/helper.R
+++ b/forecasting_tool/helper.R
@@ -26,6 +26,38 @@ chat <- function(user_message,
   return(openai_chat_response$choices$message$content)
 }
 
+# Nvidia chat ####
+chat_nvidia <- function(user_message,
+                        history = NULL,
+                        api_key, 
+                        model_llm, 
+                        temp = 0.2, 
+                        topp = 0.7, 
+                        max_token = 1024) {
+  user_prompt <-  list(list(role = "user", content = user_message))
+  prompt   <- c(history, user_prompt) |> purrr::compact()
+  
+  base_url <- "https://integrate.api.nvidia.com/v1"
+  body     <- list(model = model_llm, messages = prompt, temperature = temp, top_p = topp,
+                   max_tokens = max_token)
+  req <-
+    resp <-
+    request(base_url) |>
+    req_url_path_append("chat/completions") |>
+    req_auth_bearer_token(token = api_key) |>
+    req_headers("Content-Type" = "application/json") |>
+    req_user_agent("Soumyadipta Das") |>
+    req_body_json(body) |>
+    req_retry(max_tries = 4) |>
+    req_throttle(rate = 15) |>
+    req_perform()
+  
+  openai_chat_response <-
+    resp |> resp_body_json(simplifyVector = TRUE)
+  
+  return(openai_chat_response$choices$message$content)
+}
+
 # Google Gemini ####
 gemini <- function(prompt,
                    temperature = 0.7,
diff --git a/forecasting_tool/server.R b/forecasting_tool/server.R
index 775c020..a88a4db 100644
--- a/forecasting_tool/server.R
+++ b/forecasting_tool/server.R
@@ -443,17 +443,7 @@ server <- function(input, output, session) {
           } else if (input$model == "State Space ARIMA") {
             forecast_values <- c(model$y, model$forecast)
           } else if (input$model == "ARFIMA") {
-            
-            result <- try({
-              df <- model
-            }, silent = TRUE)
-            
-            if (inherits(result, "try-error")) {
-              forecast_values <- c(tsData(), rep(0, times = input$horizon))
-            } else {
               forecast_values <- c(tsData(), model)
-            }
-            
           } else {
             forecast_values <- forecast(model, h = input$horizon)
             forecast_values <- c(fitted(model),forecast_values$mean)
@@ -826,14 +816,15 @@ server <- function(input, output, session) {
             token = hugging_api_key,
             max_new_tokens = 1000
           )[[1]][[1]]
-      } else if (input$model_gen == "microsoft-Phi-3-mini") {
+      } else if (input$model_gen == "Phi-3.5-mini") {
         response <-
-          create_completion_huggingface(
-            model = "microsoft/Phi-3-mini-4k-instruct",
+          chat_nvidia(
+            prompt,
             history = rv$chat_history,
-            prompt = prompt,
-            token = hugging_api_key
-          )[[1]][[1]]
+            temp = input$temperature,
+            api_key = nv_api_key,
+            model_llm = "microsoft/phi-3.5-mini-instruct"
+          )
       } else if (input$model_gen == "Yi-1.5") {
         response <-
           create_completion_huggingface(
@@ -843,6 +834,15 @@ server <- function(input, output, session) {
             token = hugging_api_key,
             max_new_tokens = 1000
           )[[1]][[1]]
+      } else if (input$model_gen == "Meta-Llama-3.1") {
+        response <-
+          chat_nvidia(
+            prompt,
+            history = rv$chat_history,
+            temp = input$temperature,
+            api_key = nv_api_key,
+            model_llm = "meta/llama-3.1-405b-instruct"
+          )
       }
     
     
diff --git a/forecasting_tool/theme.R b/forecasting_tool/theme.R
new file mode 100644
index 0000000..3f9e4c6
--- /dev/null
+++ b/forecasting_tool/theme.R
@@ -0,0 +1,80 @@
+#### dashboard sidebar theme ####
+dashboard_sidebar_theme <- create_theme( 
+  adminlte_sidebar(
+    # dark_bg = "#006666",
+    # dark_hover_bg = "#202020"
+  )
+)
+
+#### dashboard theme ####
+dashboard_body_theme <- create_theme(
+  theme = "cosmo",
+  bs_vars_button(
+    default_color = "#FFF",
+    default_bg = "#0066cc",
+    # default_border = "white",
+    border_radius_base = "10px"
+  ),
+  bs_vars_tabs(
+    border_color = "black",
+    active_link_hover_bg = "#CCE5FF"
+  ),
+  output_file = NULL
+)
+
+#### user panel theme ####
+myDashboardUser <- function (..., name = NULL, image = NULL, title = NULL, subtitle = NULL, 
+                             footer = NULL) 
+{
+  if (!is.null(title)) {
+    line_1 <- paste0(name, " - ", title)
+  }
+  else {
+    line_1 <- name
+  }
+  if (!is.null(subtitle)) {
+    user_text <- shiny::tags$p(line_1, shiny::tags$small(subtitle))
+    user_header_height <- NULL
+  }
+  else {
+    user_text <- shiny::tags$p(line_1)
+    user_header_height <- shiny::tags$script(
+      shiny::HTML("$(\".user-header\").css(\"height\", \"145px\")")
+    )
+  }
+  userTag <- shiny::tagList(
+    shiny::tags$head(
+      shiny::tags$script("$(function() {\n
+                           $('.dashboard-user').on('click', function(e){\n
+                           e.stopPropagation();\n
+                           });\n
+                           });\n
+                           ")),
+    # we need to add an id and the class `action-button` to this link
+    shiny::tags$a(id = "user_dropdown",
+                  href = "#",
+                  class = "dropdown-toggle action-button",
+                  `data-toggle` = "dropdown",
+                  shiny::tags$img(src = image,
+                                  class = "user-image",
+                                  alt = "User Image"),
+                  shiny::tags$span(class = "hidden-xs",
+                                   name)
+    ),
+    shiny::tags$ul(class = "dropdown-menu dashboard-user", 
+                   shiny::tags$li(class = "user-header",
+                                  if (!is.null(user_header_height)) user_header_height,
+                                  shiny::tags$img(src = image, 
+                                                  class = "img-circle",
+                                                  alt = "User Image",
+                                                  style="border:red"),
+                                  user_text,
+                                  style="background:#0066cc"), 
+                   if (length(list(...)) > 0) 
+                     shiny::tags$li(class = "user-body", ...),
+                   if (!is.null(footer)) 
+                     shiny::tags$li(class = "user-footer", footer)
+    )
+  )
+  userTag
+}
\ No newline at end of file
diff --git a/forecasting_tool/ui.R b/forecasting_tool/ui.R
index a6bfece..a9eb314 100644
--- a/forecasting_tool/ui.R
+++ b/forecasting_tool/ui.R
@@ -385,10 +385,11 @@ ui <- shinydashboardPlus::dashboardPage(
             "model_gen",
             "AI Model",
             choices = c(
+              "Meta-Llama-3.1",
               "Meta-Llama-3",
               # "gpt-3.5-turbo",
               "gemini-pro",
-              "microsoft-Phi-3-mini",
+              "Phi-3.5-mini",
               # "claude-2.1",
               # "claude-instant",
               "google-gemma-7b-it",
@@ -396,11 +397,11 @@ ui <- shinydashboardPlus::dashboardPage(
               "Mistral-v0.3",
               "Yi-1.5"
             ),
-            selected = "Meta-Llama-3-8B-Instruct"
+            selected = "Meta-Llama-3.1"
           ), 
           sliderInput(
             "temperature",
-            "Temperature (gemini & gpt only)",
+            "Temperature (gemini, llama-3.1 & Phi only)",
             min = 0,
             max = 1,
             value = 0.5,