[SOLVED] CS320 Machine Project 5 Building a Data Website Java

CS320

Machine Project 5: Building a Data Website

Overview

In this machine project, you’ll build a website that shares a dataset — you get to pick the dataset (possible data sources are listed below). Your website will show the actual data on a standalone page, while the homepage will showcase different visualizations of the data that you choose.

You’ll use the flask framework for the website, which will have the following features:

1. Apage within the website that displays the data in JSON format.

2. Alink to a donation page that is optimized via A/B testing.

3. Asubscribe button that only accepts valid email address formats.

4. Multiple plots on the homepage that gives an overview of the data.

More information on these requirements will be detailed below.

Your .py file may be short, perhaps less than 100 lines, but it will probably take a fair bit of time to get those lines right.

Learning Objectives

During this machine project, students will:

Build a web application for sharing data/information on the internet using the Flask framework.

Practice data visualization methods to create informative SVG plots relating to a dataset that the student chooses.

Create regular expressions for basic email validation.

A/B test two different versions of a homepage to optimize donations.

Setup

Before you begin, follow the “starting a machine project” instructions in the git-workflows document to make sure that you are on the right branch and have the right files.

For this machine project, we will need some additional modules. You can install them by running the following line in your VM’s command line:

Testing

Run python3 tester.py inside of your mp5 directory (your program must be named main.py ) and work on fixing any issues.

Submission

Required Files

main.py : A Python module containing the code for your Flask web application.

main.csv : A CSV file that contains the dataset you have chosen for this machine project. If the dataset initially has a different name, please

rename it to main.csv , otherwise you will lose points when running the tester.

*.html : All HTML files needed to run your website. For example, if you just have an index.html file, that would be all you need to submit. If you have an index.html file, a donate.html file, and more .hmtl files, you would need to commit all of them.

dashboard1.svg : An SVG of your first data visualization.

dashboard1-query.svg : An SVG of your first data visualization using a query string (i.e. this one should change with a query string and appear different from dashboard1.svg ).

dashboard2.svg : An SVG of your second data visualization.

To submit the machine project, make sure that you have followed the instructions for “submitting a machine project” in the git-workflows document for the required file(s) above.

When following the submission instructions from above, the final output should look similar to this in GitLab:

If you do not know how to get to this screen, review the link above. If you are having issues, please come to office hours.

Important: make sure your program is named main.py and your dataset is named main.csv .

Important Notes:

1. Hardcoding of any kind or trying to “cheat” the autograder will be penalized heavily and can also result in 0 marks for all the projects. If you are confused about your code, please reach out to the teaching staff before submission.

Group Part (80%)

For this portion of the machine project, you may collaborate with your group members in any way (including looking at group members’ code). You may also seek help from CS 320 course staff (peer mentors, TAs, and the instructor). You may not seek or receive help from other CS 320 students (outside of your group) or anybody else outside of the course.

Part 1: Data Selection

You get to choose the dataset for this machine project. Find a CSV you like somewhere, then download it as a file named main.csv .

The file should have between 10 and 1000 rows and between 3 and 15 columns. Feel free to drop rows/columns from your original data source if necessary. **You will lose points if the size of your data is outside of these ranges.

Mandatory: leave a comment in your main.py about the source of your data.

If you’re looking for dataset ideas, here are a few places to look:

https://data-cityofmadison.opendata.arcgis.com

https://data.dhsgis.wi.gov/

https://www.kaggle.com/datasets

https://datasetsearch.research.google.com

Part 2: Building a Web Application

For part 2 of the machine project, we will do the majority of the work for this machine project in setting up the different pages that our web application will have.

Your web application should include three pages:

1. index.html : The homepage for our project.

2. browse.html : A page where people can view the raw data we chose.

3. donate.html : A page where we will ask for donations for our project.

To get started, consider creating a basic index.html file for our homepage:

<html>

<body>

<h1>Welcome!</h1>

<p>Enjoy the data.</p>

</body>

</html>

Then create a simple flask app in main.py with a route for the homepage that loads index.html :

import pandas as pd

from flask import Flask, request, jsonify

# TODO: Add a comment about your data source

app = Flask(__name__)

# df = pd.read_csv(“main.csv”)

@app.route(‘/’)

def home():

with open(“index.html”) as f:

html = f.read()

return html

if __name__ == ‘__main__’:

app.run(host=”0.0.0.0″, debug=True, threaded=False) # don’t change this line!

# NOTE: app.run never returns (it runs for ever, unless you kill the process)

# Thus, don’t define any functions after the app.run call, because it will

# never get that far.

After creating these files, try launching your application by running python3 main.py :

user@instance-1:~/cs320-projects-and-labs/mp5$ python3 main.py

* Serving Flask app “main” (lazy loading)

* Environment: production

WARNING: This is a development server. Do not use it in a production deployment.

Use a production WSGI server instead.

* Debug mode: off

* Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)

This program runs indefinitely, until you kill it with CTRL+C (meaning press CTRL and C at the same time). Open your web browser and go to http://your-ip:5000 to see your page (“your-ip” is the IP you use to SSH to your VM).

If something doesn’t seem to be working correctly, there is a chance that you have a different program (maybe from MP5) running out of port 5000. To stop whatever is running in port 5000, you can run lsof -i tcp:5000 in terminal. Then, find the process ID and run kill #### .

Requirements for Pages

While there is quite a bit of creative freedom for this machine project and these pages, we do have some requirements for what is on the pages.

Going to http://YOUR-VM-IP:port/browse.html should return the content for browse.html , and similarly for the other pages.

The index.html page should have hyperlinks to all the other pages. Be sure to not include your IP here! A relative path is necessary to pass our tests.

You should put whatever content you think makes sense on the pages. Just make sure that they all start with an <h1> heading, giving the page a title.

Requirement: browse.html page

The browse.html page should show an HTML table with all the data from main.csv . Don’t truncate the table on the page; we want to see all the rows from main.csv on the screen (it is OK to delete rows in main.csv if you want a shorter file, but make sure that this is done outside of main.py ). Don’t have any other tables on this page, so as not to confuse our tester.

The page might look something like this:

Hint 1: you don’t necessarily need to have an actual browse.html file just because there’s a browse.html page. The handler just needs to output a string in html format, meaning we can use python to build this string of html in many different ways. For example, here’s a hi.html page without a corresponding hi.html file:

@app.route(‘/hi.html’)

def hi_handler():

return “howdy!”

For browsing, instead of returning a hardcoded string, you’ll need to generate a string containing HTML code for the table, then return that string.

For example, “<html>{}<html>”.format(“hello”) would insert “hello” into the middle of a string containing HTML code.

Hint 2: look into pandas.to_html() . If you have floats in your table, make sure they are not truncated! By default, to_html() truncates floats, for example 1.286957141491255 -> 1.286957 .

Requirement: browse.json page

What if other people want to easily download our dataset from our website? They could use something like Selenium or pandas.read_html() , but that takes extra effort on their part. Oftentimes, developers will provide access to structured data over a network connection using JSON files. We will do the same thing, and give other developers a place to go to download our data.

Add a resource at https://your-ip:port/browse.json that displays the same information as browse.html , but in JSON format (represent the DataFrame. as a list of dicts, such that each dict corresponds to one row).

Rate Limiting

Adding this resource for other developers was helpful, but what if someone wanted to take down our web application? One way that they could try to do this is by repeatedly sending requests to our application, which may cause it to crash.

One way that we can try to fight this is with rate limiting, which will only allow a user to access our data one time every minute.

Add rate limiting capabilities to the browse.json resource. If the specific user has not requested the data at this resource in the past 60 seconds, we will allow them to get the data as normal. However, if they have requested the data at this resource in the past 60 seconds, we should return a 429 error code, as well as the proper Retry-After header.

Check the client IP with request.remote_addr . Do not allow more than one request per minute from any one IP address.

Hint 1: consider combining Flask’s jsonify with Pandas to_dict : https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_dict.html

Hint 2: we cover rate limiting in the future lecture.

Requirement: visitors.json

Now add a resource at http://your-ip:5000/visitors.json that returns a list of the IP addresses that have visited your browse.json resource. We could think of this resource as a list of who has downloaded or viewed our browse.json resource.

Hint 1: use the client IPs stored in previous exercise (rate limiting).

Requirement: donate.html page

On your donations page, write some text, making your best plea for funding. This can be as simple as adding a paragraph tag with a little information about why someone should donate to your project.

Part 3: A/B Testing

Now that we have a donations page, we will want to optimize our web application’s homepage so that it directs users to donate to our project (without donations, a real web application may struggle to stay afloat due to hosting fees and other costs).

We’ll do an A/B test. Create two versions of the homepage, say, A and B. They should differ in some way, perhaps somewhat trivially (e.g., maybe the link to donations is blue in version A and in red in version B), or in a more complicated way. (Note: we could have two separate HTML files, but it might be easier to utilize the .replace(…) method for strings to make this happen).

For the first 10 times your homepage is visited, alternate between version A and B each time. After that, pick the best version (the one where people click to donate most often), and keep showing it for all future visits to the page.

Hint 1: consider having a global counter in main.py to keep track of how many times the homepage has been visited. Consider whether this number is 10 or less and whether it is even/odd when deciding between showing version A or B for alternations.

Hint 2: when somebody visits donate.html , we need to know if they took a link from version A or B of the homepage. The easiest way is using query strings. On version A of the homepage, instead of having a regular link to donate.html , link to “donate.html?from=A”, and in the link on version B to donate.html , use “donate.html?from=B”. Then the handler for the donate.html route can keep count of how much people are using the links on both versions of the home page. However, be sure to still allow for your donate page to be accessible without a query string as well. For example, if someone were to just type YOUR-IP:5000/donate.html directly into their browser.

Hint 3: You don’t necessarily need to have two different versions of your homepage to make this work. You could use the templating approach: once you read your index.html file into your program, you can replace pieces of it. At that point it should be a string, so you could add something to it or replace something in it.

Part 4: Emails

There should be a button on your site that allows people to share their email address with you to get updates about changes to the data:

When the button is clicked, some JavaScript. code will run that does the following:

1. Pops up a box asking the user for their email address

2. Sends the email to your flask application

3. Depending on how your flask application responds, the JavaScript. will either tell the user “thanks” or show an error message of your choosing

We’ll give you the HTML+JavaScript. parts, since this isn’t a topic covered in class.

Add the following <head> code to your index.html , before the <body> code:

<head>

<script. src=”https://code.jquery.com/jquery-3.4.1.js”></script>

<script>

function subscribe() {

var email = prompt(“What is your email?”, “????”);

$.post({

type: “POST”,

url: “email”,

data: email,

contentType: “application/text; charset=utf-8”,

dataType: “json”

}).done(function(data) {

alert(data);

}).fail(function(data) {

alert(“POST failed”);

});

}

</script>

</head>

Then, in the main body of the HTML, add this code for the button somewhere:

<button nclick=”subscribe()”>Subscribe</button>

Whenever the user clicks that button and submits an email, it will POST the data to the /email route in your app, so add that to your main.py :

@app.route(‘/email’, methods=[“POST”])

def email():

email = str(request.data, “utf-8”)

if len(re.findall(r”????”, email)) > 0: # 1

with open(“emails.txt”, “a”) as f: # open file in append mode

f.????(email + ????) # 2

return jsonify(f”thanks, your subscriber number is {num_subscribed}!”)

return jsonify(????) # 3

Fill in the ???? parts in the above code so that it:

1. Use a regex expression that determines if the email is valid (Hint: For simplicity, a proper email address should follow the structure [email protected] . abc and xyz can be any amount of letters or numbers, while lmn should be strictly a three letter string).

2. Writes each valid email address on its own line in emails.txt

3. Sternly warns the user who entered an invalid email address, by alerting to stop being so careless (you choose the wording)

Also find a way to fill the variable num_subscribed with the number of users that have subscribed so far, including the user that just got added.

Note: you can find information about jsonify here.

Individual Part (20%)

For this portion of the machine project, you are only allowed to seek help from CS 320 course staff (peer mentors, TAs, and the instructor). You may not receive help from anyone else.

Dashboard

Implement a dashboard on your homepage showing at least 3 SVG images. The SVG images must correspond to at least 2 different flask routes, i.e., one route must be used at least twice with different query strings (resulting in different plots).

Important

Ensure you are using the Agg backend for matplotlib, by explicitly setting

matplotlib.use(‘Agg’)

right after importing matplotlib.

Ensure that app.run is launched with threaded=False .

Further, use fig, ax = plt.subplots() to create the plots and close the plots after savefig with plt.close(fig) (otherwise you may run out of memory).