How to deploy your ML model as Fast API?

Another post starts with you beautiful people!
Thank you all for showing so much interests in my last posts about object detection and recognition using YOLOv4. I was very happy to see many aspiring data scientists have learnt from my past three posts about using YOLOv4. Today I am going to share you all a new skill to learn. Most of you have seen my post about deploying and consuming ML models as Flask API where we have learnt to deploy and consume a keras model with Flask API . In this post you are going to learn a new framework- FastAPI to deploy your model as Rest API. After completing this post you will have a new industry standard skill.

What is FastAPI?
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. It is easy to learn, fast to code and ready for production. Yes, you heard it right! Flask is not meant to be used in production but with FastAPI you can use your model in production with minimal code. Along with this advantage FastAPI also have inbuilt OpenAPI (previously known as Swagger) support. With this OpenAPI support you don't need to use Postman or any other tool for testing your API. You will have a UI where you can easily test your endpoints.

How to install FastAPI?
We can install FastAPI using pip. For this installation open your Anaconda Prompt with admin rights, activate your virtual environment and run the following two commands one by one-

pip install fastapi
pip install uvicorn

Here uvicorn is a lightning-fast ASGI server and enables us to use FastAPI for all our asynchronous calls without any worry. Once you installed this framework, you can confirm the installation by following code snippet-

See the similarity with Flask API. Here in the same manner we have initialized the FastAPI as app variable. Save the above file and to run this file use the command: uvicorn main:app --reload where main is your Python file name and app is the variable we used for the initialization of FastAPI() function. Here --reload parameter is used to take any changes in Python file immediately. After making any changes you will not need to rerun the server. Once your server is up and running you will see following look like message in console-

Now open the url http://127.0.0.1:8000/items/5?q=somequery in a browser window. You will see a json response. Now open the url http://127.0.0.1:8000/docs and you will see screen like below-

See, this is the OpenAPI UI where you can easily test your API endpoints. Now you are aware of basic of FastAPI, we will move to the next step.

Serving my YOLO model as FastAPI?

In this post I am going to deploy my YOLO model as FastAPI. If you still have not tried YOLO then you can learn from previous post and trained your custom model. The API deployment process will be the same for any kind of ML model. Here I am using my custom YOLOv3 models which detects price tags in a given shelf image and then extract the price from those tags. Basically aim of these models are to extract the price of a brand and send in response to front end. So in this example input will be the complete path of an image and response will be extracted price.

Here is my code snippet of main Python file-

As you can see code is similar as we saw earlier. Here I have defined my get_price() function as asynchronous using 'async' keyword. Function is taking one input arguement- image which is the complete path of the image. I am reading the image using OpenCV. The HTTP method of this call is 'GET'. The complete logic for price tag detection and recognition is written in another Python file tag_detection.py which I have imported and called the required function. This tag_detection() function will return the extracted prices. You can replace this function with your own classification/regression or NLP model logic.

I have saved this file as price_extraction.py. So I ran following command to run the server: uvicorn price_extraction:app --reload and after server started I open the OpenAPI UI with command http://127.0.0.1:8000/docs-

Now to test the endpoint click on the GET or your HTTP method button. In my case it is looking like below-

Once you expand your HTTP method, you need to click on Try it out button to enable the input field. Based on your ML model input fields may be more than one. In my case it is one. After clicking on this button, enter the values of the input field and click on the Execute button. This button will trigger your endpoint and return the response.

In my case response screen looks like below-

And my input image was this-

Quite interesting right! With minimal code we are able to deploy and consume our model as Rest API. We also don't need to use any external tool for testing the endpoints. With OpenAPI inbuilt support no wonder companies like Microsoft, Netflix and Uber are using the FastAPI in production. Now you don't need to think too much; just take your existing ML model, wrap it in FastAPI code you learned today from this post and start experimenting with it because we learn by doing experiments in Data Science.

Once you are done with this go ahead and deploy your FastAPI in Heroku by following this link to showcase your model with others. In my next post I will share you my new learning till then Go chase your dreams, have an awesome day, make every second count and see you later in my next post.