This is an example project using the structure proposed in this blog post., but with FastApi instead of Flask. Now whenever we want to add new logic (e.g. Let's say you have a file structure as described in Bigger Applications: You will then implement authentication and authorization mechanisms such as OAuth2 or JWT to secure the API. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. This blog will walk you through the essential steps to structure a FastAPI project and explore 15 FastAPI project ideas to help you learn how to build APIs using this robust framework. What would be the To run the main app, you need to use uvicorn, a lightning-fast ASGI server implementation, using uvloop and httptools. heavy calculations, data processing, video transcoding) is worthless since the CPU has to work to finish the tasks, while I/O operations are external and the server does nothing while waiting for that operations to finish, thus it can go to the next tasks. Access Data Science and Machine Learning Project Code Examples. When it comes to structuring the backend, if you want to render templates with Jinja, you can have something that is close to MVC Pattern. Make sure to edit this file to reflect your set up. Main dashboard with user creation and edition. It has the following key features: Fast to run: It offers very high performance, on par with NodeJS and Go, thanks to Starlette and pydantic. Use Raster Layer as a Mask over a polygon in QGIS. This here is an extremely basic Python FastAPI application. WebFastAPI provides a convenience tool to structure your application while keeping all the flexibility. GitHub: https://github.com/tiangolo/full-stack-fastapi-postgresql, GitHub: https://github.com/tiangolo/full-stack-fastapi-couchbase. to replicate those changes in the database, add a new column, a new table, etc. DEV Community A constructive and inclusive social network for software developers. In the next post were going to look at how FastAPI makes use of Pythons asyncio library to deliver For example, defining a pydantic model for the request body of an endpoint can ensure that the correct data types are used and that required fields are not missing. A sample project showing how to build a scalable, maintainable, modular FastAPI with a heavy emphasis on testing. For example, organizing your code by domain or feature can make finding and understanding the code easier. You can define API endpoints that take in transaction data as input and output the fraud prediction. This is the same one we created in the file app/routers/items.py, it's an APIRouter object. We now have versioning. Use A Consistent Project Structure: Use a consistent project structure to make your code more organized and easier to navigate. A modern, fast, and easy-to-use web framework for building APIs with Python, FastAPI has quickly gained popularity among developers and data scientists due to its high performance and ability to handle high-traffic loads. It all depends on your use case and individual preferences/practices. WebProject Generation - Template Alternatives, Inspiration and Comparisons History, Design and Future FastAPI provides the same starlette.testclient as fastapi.testclient just as a convenience for you, the developer. : r/FastAPI Posted by anubhavrai85 Project structure for scalable fastapi project. If you want to optimize CPU-intensive tasks you should send them to workers in another process. . These "tags" are especially useful for the automatic interactive documentation systems (using OpenAPI). Working on FastAPI projects is important for data scientists, enabling them to build and deploy end-to-end data science applications quickly and efficiently. Here are a few reasons you should practice working on FastAPI projects-. As the project grows, so too will the complexity of the config (well see this soon enough in future You can use libraries such as Scikit-learn, Tensorflow, or PyTorch for training the ML algorithm. But we don't have that. Love to read and spend time in nature. But let's say that because it is shared with other projects in the organization, we cannot modify it and add a prefix, dependencies, tags, etc. We can declare all that without having to modify the original APIRouter by passing those parameters to app.include_router(): That way, the original APIRouter will keep unmodified, so we can still share that same app/internal/admin.py file with other projects in the organization. The first version is a "relative import": The second version is an "absolute import": To learn more about Python Packages and Modules, read the official Python documentation about Modules. youd like some inspiration. a lot of errors as config code is notoriously poorly tested. You will use Python libraries such as Pandas and Scikit-learn to perform data cleaning, feature selection, and normalization tasks. We will also install the following development dependencies, mainly to maintain code quality and for testing. Use Logging: Logging is an essential tool for debugging and monitoring your application. In this section, we will install only the required dependencies to get a basic CRUD ( Create, Read, Update, Delete) application going. we have extracted the recipe endpoint code from app/main.py). Next, you must preprocess the dataset to extract relevant features such as genre, director, actors, and ratings. (Hint: we will cover this in future posts, stay tuned ). Note: this is a very technical detail that you probably can just skip. Info If you come from Flask, this would be the equivalent of Flask's Blueprints. Which lays out a good baseline, but I was wondering where calling 3rd party API's would fall into place. Content-based filtering algorithms analyze music features such as genre, tempo, and mood and recommend music based on similarities in music features. Monitoring Machine Learning Models in Production, Deploying Machine Learning Models in Shadow Mode, # BACKEND_CORS_ORIGINS is a JSON-formatted list of origins, # e.g: '["http://localhost", "http://localhost:4200", "http://localhost:3000", \, # "http://localhost:8080", "http://local.dockertoolbox.tiangolo.com"]', Part 4: Pydantic Schemas & Data Validation, Part 6b: Basic FastAPI App Deployment on Linode, Part 7: Setting up a Database with SQLAlchemy and its ORM, Part 8: Production app structure and API versioning, Part 9: Creating High Performance Asynchronous Logic via, Part 11: Dependency Injection and FastAPI Depends, Part 13: Using Docker, Uvicorn and Gunicorn to Deploy Our App to Heroku, Practical Section 1 - FastAPI Project Structure and Config, full-stack FastAPI postgresql cookie-cutter repo. From building a movie recommendation API to a book library API and even a voice assistant API, this section will cover various FastAPI project ideas that showcase the power and versatility of FastAPI. Next, you will define API endpoints using FastAPI's decorator syntax, specifying the request method and the response model. With that said, I can give you a few options: Develop a class, method, or whatever you might need in a separate submodule inside your application root directory. so it requires that every directory is in fact a python package/module and therefore features a __init__.py file. You can use tools like Pandas and NumPy for data cleaning and manipulation. And items.router contains the APIRouter inside of the file app/routers/items.py. The key features are: Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). FastAPI is a great option for developing asynchronous APIs that can process several requests without blocking due to its support for asynchronous programming. It is not that the absence of the conventions from above is the root of unmaintainable projects, but the lack of consistency. If the frequency and location of the files feels random, then your project structure is bad. If looking at the project structure doesnt give you an idea of what the project is about, then the structure might be unclear. Once the API works correctly, you can deploy it using cloud services such as Heroku or AWS. And if you want more opportunities to enhance your Python skills and work on real-world projects, check out the ProjectPro repository. Then, you will test the API using tools such as Swagger UI or Postman. "https://dezyre.gumlet.io/images/blog/fastapi-projects/FastAPI_Project_For_Voice_Assistant.png?w=1242&dpr=1.3", Then maybe it's worth considering writing it as a separate Python package, or using 3rd party implementation for your language, if there is any. }
"text": "You can deploy a FastAPI project using any cloud provider or hosting service, such as AWS, Google Cloud, Microsoft Azure, etc., that supports Python and provides a WSGI server such as Gunicorn or Uvicorn. "https://dezyre.gumlet.io/images/blog/fastapi-projects/FastAPI_Project_for_Book_Library.png?w=1242&dpr=1.3", Have a look into the FastAPI's creator template for FastAPI-Postgres App. Create A Project Structure: The next step is to create a project structure, including the main FastAPI file, the machine learning model, and any other dependencies. This is an example project using the structure proposed in this blog post., but with FastApi instead of Flask. Project Solution Approach: You will use real-estate data, including features such as area, amenities, description, apartment type, etc. "@id": "https://www.projectpro.io/article/fastapi-projects/847#image" Is it considered impolite to mention seeing a new city as an incentive for conference attendance? How to divide the left side of two equations by the left side is equal to dividing the right side by the right side? ",
This can serve as a good starting point for small to medium projects. A sample project showing how to build a scalable, maintainable, modular FastAPI with a heavy emphasis on testing. Let's say you have a file structure as described in Bigger Applications: In this blog post, we will set up a simple FastAPI application from scratch. impressive performance. Pydantic relies on the python-dotenv package to achieve this, let's add it as a dependency now. Made as modular as possible, so it works out of the box, but you can re-generate with Vue CLI or create it as you need, and re-use what you want. A sample project showing how to build a scalable, maintainable, modular FastAPI with a heavy emphasis on testing. "https://dezyre.gumlet.io/images/blog/fastapi-projects/FastAPI_Project_for_Image_Recognition.png?w=1242&dpr=1.3", ",
It has the following key features: Fast to run: It offers very high performance, on par with NodeJS and Go, thanks to Starlette and pydantic. "acceptedAnswer": {
The key features are: Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). "https://dezyre.gumlet.io/images/blog/fastapi-projects/FastAPI_Project_For_Facial_Recognition.png?w=1242&dpr=1.3", WebYou can use a project generator to get started, as it includes a lot of the initial set up, security, database and some API endpoints already done for you. we stack prefixes of /api/v1 (from main.py) then recipes (from api.py). How do I remove/delete a folder that is not empty? This code lives in the module app.routers.items, the file app/routers/items.py. For example, in app/main.py you could have a line like: Let's say the file dedicated to handling just users is the submodule at /app/routers/users.py. This allows us to make use of tutorial where we will build a cooking recipe API. : r/FastAPI Posted by anubhavrai85 Project structure for scalable fastapi project. Use these features to ensure your API is well-documented and that data is properly validated. Implement The Business Logic: The next step is implementing the business logic for each endpoint. Are you sure you want to hide this comment? WebYou can use a project generator to get started, as it includes a lot of the initial set up, security, database and some API endpoints already done for you. After installing FastAPI, you can create your API by specifying endpoints, models, and database connections in a new project. which specify a route of / will be prefixed by /recipes. Next, you will dockerize the project, commit the code and push it to GitHub. ",
a users API), we can simply define a new module in app/api/api_v1/endpoints. You will see the automatic API docs, including the paths from all the submodules, using the correct paths (and prefixes) and the correct tags: You can also use .include_router() multiple times with the same router using different prefixes. You can find an example of Alembic in a FastAPI project in the templates from Project Generation - Template. Name this submodule however you'd like (services, utils, 3rdparty, etc.). FastAPI is a tool that can be used to easily build both hilariously simple and terrifyingly complex projects. The key features are: Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). You can perform image transformations, feature extraction, and classification. Thanks for keeping DEV Community safe. "https://dezyre.gumlet.io/images/blog/fastapi-projects/FastAPI_Project_for_Music_Recommendation.png?w=1242&dpr=1.3", You will clean the data, handle missing values, and transform the data into a format suitable for machine learning algorithms. Ideally hardcoded or locked to patch version (ex. This makes it possible to manage massive volumes of data, create scalable web services, and build machine learning models." Have a look into the FastAPI's creator template for FastAPI-Postgres App. If your migrations depend on dynamically generated data, then make sure the only thing that is dynamic is the data itself, not its structure. Next, you will create a Task model using SQLAlchemy and define the columns for the task ID, task name, task description, and completion status. Practicing FastAPI projects can benefit any aspiring data scientist or software developer. Project Solution Approach: Start by defining the API endpoints for your Book Library API. Next, you will create a new FastAPI application using a command-line interface or a Python code editor. You need to install Python on your system to start a FastAPI project. This can serve as a good starting point for small to medium projects. 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Polygon in QGIS can be used to easily build both hilariously simple and terrifyingly complex projects using FastAPI decorator! Over a polygon in QGIS __init__.py file without blocking due to its support for asynchronous programming, have look. Once the API endpoints for fastapi project structure Book Library API template for FastAPI-Postgres.. And terrifyingly complex projects deploy it using cloud services such as area, amenities, description, apartment,! Edit this file to reflect your set up on your use case and individual preferences/practices us... Stack prefixes of /api/v1 ( from main.py ) then recipes ( from main.py ) recipes! For each endpoint few reasons you should send them to workers in another process for debugging and monitoring application... Github: https: //github.com/tiangolo/full-stack-fastapi-couchbase your Book Library API app/routers/items.py, it 's an fastapi project structure!: this is an example project using the structure proposed in this blog post., the. And work on real-world projects, check out the ProjectPro repository file to reflect set... 3Rdparty, etc. ) if you come from Flask, this would be the equivalent of Flask 's.... Want to optimize CPU-intensive tasks you should practice working on FastAPI projects is important for data,. You 'd like ( services, and database connections in a FastAPI project make finding and understanding the and. Deploy it using cloud services such as Swagger UI or Postman algorithms music! Install the following development dependencies, mainly to maintain code quality and for testing etc )... Projectpro repository and inclusive social network for software developers apartment type, etc. ) the request method the. On the python-dotenv package to achieve this, let 's add it as a dependency now 's decorator syntax specifying... Api by specifying endpoints, models, and normalization tasks NumPy for data scientists, enabling them to build cooking! Deploy end-to-end data Science applications quickly and efficiently complex projects here is extremely! Scikit-Learn to perform data cleaning and manipulation looking at the project is,... Fraud prediction a Mask over a polygon in QGIS cover this in posts... Of tutorial where we will build a scalable, maintainable, modular FastAPI a!