CSV uploader app

In this tutorial, we'll learn to build a simple app for exploratory data analysis. The app will let us upload CSV files and visualize the data distribution in a histogram, and we'll interact through selectors to choose the CSV file and column to be plotted. Take a look at the final app:

The full code and some sample CSV files can be downloaded from this GitHub repository.

Setup and app structure

We'll use GenieFramework.jl, DataFrames.jl and CSV.jl to build the app. To install them, use the builtin package manager, or launch a REPL and enter Pkg mode with ] and type in the following command:

pkg> add GenieFramework DataFrames CSV

The app's code will have a main file app.jl for the logic, ui.jl for the UI, and an uploads folder to store the datasets:

├── app.jl
├── ui.jl
└── uploads/

We will not use the app.jl.html that is created by default.


Let's start by opening the app.jl file and adding the following skeleton code:

using GenieFramework, DataFrames, CSV

# Logic goes here

@page("/", "ui.jl")
Server.isrunning() || Server.up()

The @genietools macro sets up the Genie environment and the @page macro renders the app's UI, whereas the logic will be implemented in between. For the ui.jl file this will be the skeleton code:

    # UI definition goes here
] |> string

The app has four components: an upload box, two selectors, and a histogram plot. To implement each component, we'll need to:

  • Define the necessary variables and expose them to the UI.
  • Implement component's behavior.
  • Define component's UI in ui.jl.

Upload box

The upload box lets us select a .csv file and store it on the server side. The file is transmitted via an HTTP POST request, with the file attached in the header. To enable this request, add the following configuration lines to app.jl:

Genie.config.cors_headers["Access-Control-Allow-Origin"]  =  "*"
Genie.config.cors_headers["Access-Control-Allow-Headers"] = "Content-Type"
Genie.config.cors_headers["Access-Control-Allow-Methods"] = "GET,POST,PUT,DELETE,OPTIONS"
Genie.config.cors_allowed_origins = ["*"]

Next, we'll create the storage folder if it doesn't already exist, and give our app page a title.

const FILE_PATH = "upload"
@out title = "CSV Analysis"

The @out macro makes it so the code in the UI can access the value of a variable. Before adding more logic, let us proceed with the UI.

The UI is structured via Julia functions that implement various HTML and Javascript components. To add the page title and upload box, write in ui.jl the following:

        cell(class="col-md-12", [
            uploader(label="Upload Dataset", accpt=".csv", multiple=true, method="POST", url="http://localhost:8000/", field__name="csv_file")
] |> string

While heading, row and cell simply generate the HTML code for headers and dividers, the uploader function generates the Javascript allowing us to pick and upload a file. The url argument points to the address where the POST request will be submitted, and so we'll require a route on the server that handles the request. For this, add the following code to app.jl:

route("/", method = POST) do
  files = Genie.Requests.filespayload()
  for f in files
      write(joinpath(FILE_PATH, f[2].name), f[2].data)
  if length(files) == 0
      @info "No file uploaded"
  return "Upload finished"

The route function sets up a listener function at a path that will be executed for incoming requests. Instead of defining a standalone function that stores the file in the request header, we've used Julia's do end syntax which builds an anonymous function with the code in the block and passes it to route.

The upload box is now finished and working. Give it a try by launching a preview in a new browser tab.

File and column selectors

Once we have the .csv file uploaded, we want to access its contents and choose one column for plotting. We'll do the choosing through two selectors, which rely on several variables to store:

  • The selected file, and the list of uploaded files.
  • The selected column in a file, and the list of columns in the file.

Let's add to app.jl the variables with default values as

@in selected_file = "iris.csv"
@in selected_column = "petal.length"
@out upfiles = readdir(FILE_PATH)
@out columns = ["petal.length", "petal.width", "sepal.length", "sepal.width", "variety"]

Note that we tag the selection variables with @in as they'll be storing values sent from the UI.

Now, we define the UI with

cell(class="st-module", [
  Stipple.select(:selected_file; options=:upfiles)
cell(class="st-module", [
  Stipple.select(:selected_column; options=:columns)

The selectors are generated by Stipple.select, which takes as first argument the symbol name of the variable where the selected value will be stored.

As of now, when the user makes a selection the chosen values are stored in the corresponding variables. All we need to do now is use these values to generate the histogram plot.


Since all the interactive elements are already implemented, we'll add to app.jl the reactive code that

  1. Loads the CSV file into a DataFrame.
  2. Refreshes the column selector choices with the column names of the selected file.
  3. Plots the histogram of the selected column.

For the plotting, we'll use a PlotData struct that can be parameterized to different plot types such as scatter plots or histograms. We'll define the variable storing the plot as

@out irisplot = PlotData()
This tutorial uses PlotData objects for plotting. The new recommended way is to directly use PlotlyJS traces as explained here.

In Genie, reactive code is implemented within a handler defined with the @handlers macro. In it, we use the @onchange macro to act whenever a variable changes in the UI as follows:

@handlers begin
    @onchange isready, selected_file, selected_column begin
        upfiles = readdir(FILE_PATH)
        data = CSV.read(joinpath(FILE_PATH, selected_file), DataFrame)
        columns = names(data)
        if selected_column in names(data)
            irisplot = PlotData(x=data[!, selected_column], plot=StipplePlotly.Charts.PLOT_TYPE_HISTOGRAM)

Notice the isready variable, which is provided by Genie to indicate when the browser has finished loading the UI. Finally, the code for the UI element to display the plot is

    cell(class="st-module", [

That's it, we're done! Now you can run the full app and add some more functionality if you'd like. Here are some ideas:

  • Add a scatter plot.
  • Add other interactive elements such as sliders to change the parameters of the plot.
  • Add the clustering algorithm from the Iris tutorial and try it on different datasets.