Flask and FastAPI are python-based micro-frameworks for developing small-scale data science and machine learning web and apps.
Despite the fact that FastAPI is a relatively new framework, an increasing number of developers are using it in their new projects. Is it just a marketing ploy, or is FastAPI better than Flask? We’ve put together a comparison of the major pros and cons of Flask and FastAPI to assist you in deciding which will be the ideal choice for your next data science project.
What is Flask?
Flask is a micro web framework written in python.
Armin Ronacher came up with the idea. Flask is built on the WSGI (Web Server Gateway Interface) Werkzeug toolkit (For the implementation of Request and Response) and the Jinja2 (template engine). WSGI is a standard for web application development. It is used to build small-scale web applications and Rest API Flask’s framework is more explicit than Django’s framework, and it is also easier to learn because it requires less basic code to construct a simple web application.
In this real-world top Companies are using Flask
What makes Flask special?
- Lightweight Extensible Framework.
- Integrated unit test support.
- Provided development server and debugger.
- Uses Jinja templating.
- Restful request handling.
When should you use a Flask?
- Flask is old and it has Good Community support
- For Developing web applications and creating a quick Prototype.
Flask Web Application Development
- Creation of a virtual environment
- The venv environment is being restarted.
- Login and register for several users
- Mode Debug
- Creating a User Profile Page
- Creating an avatar
- Handling errors
1. Virtual environment creation
2. Making Necessary Installation
Build a sample webpage using Flask, it will return the string
After Running the application, we need to visit http://127.0.0.1:5000/
- Flask has a built-in development server, integrated support, and other features.
- Flask Provides integrated support for unit tests.
- Flask Uses Jinja2 templates.
- Flask is just a collection of libraries and modules that helps developers to write their applications free without worrying about low-level details like Protocols and Thread Management.
- Because of its simplicity, Flask is particularly beginner-friendly, allowing developers to learn easier. It also allows developers to construct apps quickly and easily.
- Flask makes use of Modules, which is third-party participation that might lead to security breaches. Modules are the intermediary between the framework and the developer.
- Flask does not create automatic documentation; They need several extensions like Flasgger and Flask RESTX and also require additional setup.
- Flask has a single source, which suggests that it will handle each request one by one, therefore regardless of how many multiple requests there are, it will still take them in turns, which takes extra time.
What is FastAPI:
FastAPI is built in ASGI (Asynchronous Server Gateway Interface) and pydantic, Starlette. This framework is used for building a web application and RestAPI. In FastAPI we need uvicorn to run the server. There is no built-in development server So the ASGI server Uvicorn is required to run the FastAPI application.
The best thing that we Highlight in the FastAPI is documentation. It will generate documentation and create Swagger UI. which helps the developers to test endpoints effectively. In fast API, Also includes data validation and returns an Explanation of the error when the user enters the invalid data.
It implements all of the OpenAI requirements and Swagger for these specifications. As a developer we will concentrate on developing logic; the rest is handled by the FastAPI.
In this modern world, the top Websites are moving to FastAPI. The website was developed using FastAPI.
When should you use a FastAPI?
- Has Good speed and performance when compared with Flask.
- Decreasing bugs and Errors in code.
- It Generates Automatic documentation
- Built-in data validation.
What makes FastAPI special?
- Fast Development
- Fewer Bugs
- High and Fast Performance
- Automatic swagger UI
- Data validation
1. Virtual environment creation
2. Making Necessary Installation
Build a webpage using FastAPI, It return string
- FastAPI is considered as one of the fastest Framework in python, It has an native async support and provides a simple and easy-to-use dependency injection framework. Another advantage to consider is built-in data validation and Have Interactive API documentation support .
- Dependency Injection support
- Fast API is based on standards such as JSON Schema (a tool for validating the structure of JSON data), OAuth 2.0 (an industry standard protocol for authorization), and OpenAPI (an open application programming interface).
- In FastAPI there is insufficient Security and also it will support OAuth.
- Because FastAPI is relatively new, the community is small compared to other frameworks, and regardless of its detailed documentation, there are very few external educational materials.
Difference between Flask and FastAPI:
Both offer the same features but the implementation is different. The main difference between Flask and FastAPI is that Flask is built in WSGI (Web Server Gateway Interface) and FastAPI is built in ASGI(Asynchronous Gateway Interface). So, the FastAPI will support concurrency and asynchronous codes. In FastAPI there is automatic documentation for Swagger UI(docs and redocs), But in Flask we need to add some extensions like Flasgger or Flask RESTX and some dependencies setup. Unlike Flask, FastAPI provides data validation for defining a specific data type and it will raise an Error if the user enters an invalid data type.
FastAPI uses an Async library which is helpful to write concurrent code. Async is greatly helpful for doing tasks that involve something like fetching data from API and querying from a database, reading the content of the file and FastAPI has ASGI whereas Flask is a WSGI Application
There is no data validation in Flask. So Flask allows any kind of data type. In Flask the data validation will be handled by developers. But In FastAPI there is inbuilt data validation (pydantic). So it will raise an error when it gets an invalid data type from the user. This is useful for developers to interact with the API endpoints.
Flask doesn’t have any inbuilt documentation for swagger UI. We need to add some extensions like Flassger and Flask-RESTEX and some dependency setups. But In FastAPI it generates an automatic swagger UI when the API is created. For accessing the auto-generated swagger UI hit the endpoint with /docs or /redoc. It will show all the endpoints in your application.
|@app.route(“/get”, methods= [‘GET’])
At some point, you’ll want to deploy your application and show it to the world.
Flask makes advantage of WSGI, which stands for Web Server Gateway Interface. The disadvantage is that it is synchronous. This means that if you have a large number of requests, they will have to wait in line for the queue to finish.
FastAPI is an ASGI (Asynchronous Server Gateway Interface), web server, which is lightning fast because it is Asynchronous. So, if you have a lot of requests, they don’t have to wait for the others to finish before being processed.
In Flask Async can be performed by thread or multiprocessing or use some tools like celery, Create await/async for handling routes
In FastAPI there is a default AsyncIO. So we can simply add the async keyword before the function.
FastAPI was Built with Primary concerns
- Speed and Developer Experience
- Open Standards.
- FastAPI can connect Starlette, Pydantic, OpenAPI, and JSON Schema.
- FastAPI uses Pydantic for data validation and Starlette for tooling making it twice as fast as Flask and equivalent to high-speed web APIs written in Node or Go.
- Starlette + Uvicorn supports async requests, while Flask does not.
- Data validation, serialization and deserialization (for API development), and automatic documentation are all included (via JSON Schema and OpenAPI).
Which Framework is Best for AI/ML
Both Flask and FastAPI are the popular Framework for developing Machine learning and web applications.
But most data scientists and Machine learning developers prefer Flask. Flask is the primary choice of Machine learning developers for writing the API’s .
A few disadvantages of using Flask is time consuming for running the big applications. The major disadvantage in flask is to add more dependencies by adding plugins and the other one is lack of Async support whereas FastAPI supports Async by default .
FastAPI is used for the creation of ML instances and applications. In the machine learning community Flask is one of the popular frameworks.Flask is perfect for ML engineers who want to create web models.
FastAPI, on the other hand, is the best bet for a framework that provides both speed and scalability.
Migrating Flask TO FastAPI :
Yes, It is possible to migrate a Flask application to FastAPI. In FastAPI there is native async support. Flask also supports Async but is not as extensive as FastAPI. There is some syntactical difference between Flask and FastAPI.
The Application object in Flask and FastAPI is
Simple Example for Migrate Flask to FastAPI:
- Flask Application
1.To Migrate the Flask to FastAPI we need to install and import libraries.
2. URL Parameters
In FastAPI the request methods are defined in the methods FastAPI objects. For Example @app.get, @app.post, @app.put.
The Request Methods in Flask and FastAPI is
Here in the put request route, we are passing a body as an Employee object. And also we will create a new class called Employee and inherit it from the base model. And pass the type of the URL parameter employee_id within the route, instead of that pass the type of the parameter in employee_get()
Like URL Parameters, Query parameters are also used for managing the state(For sorting or Filtering).
Run the server in Flask And FastAPI
And Finally the FastAPI Application looks like :
- FastAPI Application
Use ASGI Application web server Uvicorn to run the FastAPI application (uvicorn.run(app)).
When Should you choose FastAPI instead of Flask and Django?
- Native Async Support:The FastAPI web framework was created using the ASGI web server. Native asynchronous support eliminates inference latency.
- Improved latency: As a high-performance framework, the framework’s total latency is lower when compared to Flask and Django.
- Production-ready:With the FastAPI web framework’s auto validation and short defaults, developers may easily design web apps without rewriting the code.
- High Performance: Developers have access to the key functionalities of Starlette and Pydantic because they are compatible. Needless to say, Pydantic is one of the quickest libraries, and as a result, overall speed improves, making FastAPI the preferred library for web development.
- Learn Simple:This is a minimalist framework so it is easy to understand and learn.
Flask Or FastAPI: Which is better
|Flask is a micro web framework to develop small-scale web applications and Rest API. Flask is depends on WSGI toolkit (werkzeug, Jinja2)
|FastAPI is considered one of the Fastest frameworks compared to the flask. FastAPI is built in Pydantic and starlette.
|Flask is built in Web Server Gateway Interface(WSGI)
|FastAPI is built in Asynchronous Server Gateway Interface(ASGI)
|It does not have any inbuilt documentation such as swagger UI and needs to add some extensions like Flasgger or Flask RESTX.
|In FastAPI it has inbuilt documentation like (docs and redocs).
|There is no Inbuilt data validation in Flask, we need to define the data type in requests.
|In FastApI there is an Inbuilt data validation that will raise the error if the user provides an invalid data type.
|Flask is more flexible than Other frameworks.
|FastAPI is flexible in code standards and it does not restrict the code layouts.
After learning about both Flask and FastAPI. Both are used to create a Web application and Rest API. But FastAPI is better when compared with Flask. Because FastAPI has a native ASGI support (Asynchronous Server Gateway Interface). So it is Faster and High in performance. Also, it has inbuilt documentation(Swagger UI) and data validation. FastAPI has High and Fast performance, Efficiency and it’s easy to understand and learn. When compared to Flask, FastAPI has less community support but it reaches a lot in a short period of time.