Automate CI/CD pipelines with GitHub Actions

Deploy a Machine Learning model using FastAPI and automate test pipelines with GitHub Actions.

Tech Stack

Scikit-Learn
FastAPI
pytest
Docker

Installation

Clone it

Clone the repository, move to the project directory, then:

Creat a virtual env
Install the needed packages and dependencies
Run the server

$ git clone https://github.com/zekaouinoureddine/movie-reviews-lr.git
$ cd movie-reviews-lr
$ conda create --name mrenv python==3.10.9
$ pip install -r requirements.txt
$ uvicorn src.main:app --reload

Dockerize it

Build the image and fire up the container:

docker build --tag mrlr .
docker run -d --name mrlr -p 8000:8000 mrlr

Run it

To ensure if the app is running, open you favorite browser then go to 127.0.0.1:8000. You should see the following message in the browser:

{"message": "The API is working ..."}

Test it

Once you make sure your API is running on the local server or in the Docker container go to 127.0.0.1:8000/docs. Here, you can test your API using some examples.

Stop it

Before moving on, dont forget to bring down the container:

$ docker stop mrlr

cURL

cURL is a command-line tool for sending HTTP requests. It supports various protocols such as HTTP, HTTPS, FTP, and many more. It can be used to send and receive data from a server, and is often used for testing APIs and debugging network issues. cURL is available for most operating systems, including Windows, Mac, and Linux. Ensure that it is installed on your machine, then type:

curl -X "GET" "http://127.0.0.1:8000/predict/?review=I%20like%20this%20amazing%20movie" -H "accept: application/json"

Output

{
  "success": true,
  "result": {
    "sentiment": "Positive",
    "positive prediction": 0.6022566919053794,
    "negative prediction": 0.39774330809462055,
    "time": 0.020993947982788086
  }
}

Amazing job!

Author Infos

If you like it, give it a ⭐, then follow me on:

LinkedIn: Nour Eddine ZEKAOUI
Twitter: @NZekaoui