I developed a Twitter bot using the Markov chain model. The motivation behind this project was to put the skills I've been learning into practical use. This bot tweets in a manner reminiscent of my friend at specific intervals.

To optimize costs and explore new learning opportunities, I migrated the infrastructure technology from AWS to Google Cloud Platform (GCP). The technologies I used include Docker, Google Kubernetes Engine (GKE), and Google Cloud Storage.

I implemented scheduled tasks for the Twitter bot using GKE's CronJobs:

1. fetch_tweets_cronjob: Scheduled to run every Sunday night at midnight (Japan time).

   • Utilizes the Twitter API to fetch the latest tweets (up to 100) from my friend’s Twitter account.
   • Appends the newly fetched tweets to the existing tweet data obtained from Google Storage.
   • Performs text preprocessing, including tokenization, to prepare the data for the Markov chain model.

2. generate_tweets_cronjob: Scheduled to run every morning at 7 AM (Japan time).

   • Uses the tweet data retrieved from Google Storage to automatically generate new tweets using the Markov chain model.
   • Tweets the generated content.

• Language: Python (Markov chain)
• Cloud Platform: Google Cloud Platform (GCP) (Compute Engine, Cloud Storage, Container Registry, Google Kubernetes Engine - GKE)
• Containerization & Orchestration: Docker, Kubernetes

Check out the bot's tweets here: @UbotInSeattle

Feel free to explore the bot's tweets and its underlying technologies!