Displaying our Tweets in the Flask Web Server

Build a Twitter Analytics App

1 Introduction: Start Here

2 The First Step: Design Your Solution

3 In Which I Rant a Little

4  Design Solution

5 Writing Great Code

6 Writing the Backend Twitter Server

Writing the Code in Small Parts: Part 1, The Basic App

Part 2: Adding a Counter to Exit

Part 3: Adding Language and Retweet Count

Part 4: Organising Our Code

7 Adding the Data to a Database

8 Testing: What and How to Test

8.1 Testing Our Frontend

8.2 Testing Our Backend

9 Displaying our Data using the Flask Webserver

9.1 Introduction to Flask

9.2 Adding templates to our Flask app

9.3 Displaying our Tweets in the Flask Web Server

10 Future Work and Improvements

So we have most of our code in place. Our backend reads and writes the tweets to a database. We have a front end webserver that can display the tweets. Now we just need to connect the two.

Let’s look at our code.

We saw the top_tweets template in the last section, so let’s do that first. We are calling a function get_top_tweets() to get the top tweets, and the date-time. The date-time is only for debug, just so that we can check the script is running regularly. Let’s look at the function get_top_tweets():

We open the database and read the last 30 tweets, as well as the datetime. This is then returned to our function. As we saw in the templates section, the tweets are just printed on the screen:

Let’s look at the function to read the trends next:

This time, we only read the last 10 values; but remember, we have 1 trend, and 3 tweets for each trend.

We return both the trends list, as well as the trend_tweet list, which contains the trending tweets.

Displaying graphs

Till now, our code is simple. All we are doing is taking data from our database, and displaying it on the screen. We already got the tweets in the right format using the twitter api, so we needed to do nothing else.

But the languages data is different. This time, we have to actually plot it.

For plotting, we will be using Google Charts.

I tried dozens of JS charting libraries, but found Google Charts to be the easiest to use. Most of the others require a lot of fiddling.

I am not a JS expert, I just want the libraries to work out of the box. Highcharts failed (at least for me),  d3.js is too complex. Flotcharts worked, but looks a bit dated.

Google Charts surprised me: It worked out of the box, and produces beautiful graphs.

Have a brief look at Google Charts examples, as in most cases, I just copy pasted the code and tweaked it a little.

Let’s look at the function to read the language data:

This time, we only read one value, as the data is in an array. Since SqLite can’t store Python lists, I had converted the list to a string. Here, I convert it back and return it.

This is where I’m using the data. I am re-shaping the data in a list. You may note that we are repeating the 2nd element twice. Like this:

Why are we doing this? Because Google Charts wants us to send the data separately. So in the above, the first value is the language, the second is its percentage usage, and the third is the same thing repeated. It’s repeated because we need it for tooltips, and each item we use needs to be passed in, even if it is repeated.

Let’s look at the template (html) file to see how the data is used.

All this is setup to run the Google Charts Javascript.

This is the function to draw the chart. As you can see, we are drawing 3 columns. The first is a string called Languages. Then a number called Usage. Finally, the tooltip data, which is the repeat of the previous value. Hopefully, now you can see why we needed to pass the data like this.

Here, we pass in the data we read. Again {{ }} is a Jinja command, and the data in here will be replaced by what our Flask server sent it. We’ve already talked about safe: Google doesn’t like it, like Twitter.

Here is an example of how the above field will be filled in real time:

Finally, we set some options and draw the graph. The main option is the is3D flag, which just makes the graph look cool 🙂

And there you go. A basic version of our app, running on our machine. Have a play with it.

PS: Interested in leveling up your Python and getting a great job? Check out the Python Apprenticeship Program.