In this project, we built a machine learning model to predict the number of Covid positive cases and deployed it on Flask. The Flask app is deployed on Google App Engine and can be accessed through a public url. We also verified the elastic scale-up performance via Load Test with Locust.
- Application Deployed on: https://covid-prediction-311000.uc.r.appspot.com
- ML Framework: Sklearn
- Platform: Flask + Google App Engine
- Load Test Framework: Locust
- Dataset: https://raw.githubusercontent.com/jingyi-xie/covid-prediction/main/national-history-update.csv
The workflow of this project is as below:
Here is an image of the frontend website:
Here is an image of the load test result:
To deploy this ML model and Flask app on Google Cloud Platform, you can follow these steps:
Launch Google Cloud Platform, create a new project. Change your current project to it and activate Cloud Shell.
Git clone this repository to your GCP local and cd into it.
Create a virtual environment and activate it. (To deactivate it, run deactivate).
make set-up
source ~/.covid_venv/bin/activate
Install the required packages.
make install
Run this app, the flask app will be running on http://127.0.0.1:8080/.
python3 main.py
You can test it from the frontend website or send a POST request to the running app through a script.
bash predict-local.sh
(optional) Verfiy the current project is working. Switch your project if it's not what you want.
gcloud projects describe $GOOGLE_CLOUD_PROJECT
gcloud config set project $GOOGLE_CLOUD_PROJECT
Create app engine in GCP.
gcloud app create
When it asks you to choose a region, select one(in my case is 14 us-central). Type "yes" when it asks you to continue.
Deploy this app on cloud, the app will be running on the provided public url.
gcloud app deploy
You can test it from the frontend website or send a POST request to the running app through a script. Remember to change the website address in predict.sh.
bash predict.sh
Run following command, the locust server will be running on http://0.0.0.0:8089/.
locust
Go to the webpage, fill out the form and try to test it.