Setting Up the Backend with FastAPI
In the last blog, we went through the setup of the React frontend for our Resume Analyzer project. Now, let’s dive into the backend setup! We’ll be using FastAPI to build our backend API, which will handle all the business logic, data processing, and communication with the frontend.
Why FastAPI? 🤔
Now, you might be wondering why we chose FastAPI over other frameworks like Flask or Django. Here’s why:
1. Performance 🚀
FastAPI is built on ASGI, which supports asynchronous programming and allows us to handle multiple requests concurrently without blocking the server. Given that our application will be handling multiple resume uploads simultaneously, FastAPI’s async capabilities make it a great choice to ensure fast response times and scalability.
2. Type Safety & Validation 🧑💻
With FastAPI, you get automatic validation of incoming data using Pydantic models. This means that the data being sent to the backend, whether it’s a JSON payload or form data, gets validated instantly. No more manual checks for data consistency!
3. Automatic API Documentation (Swagger) 📑
One of the standout features of FastAPI is its ability to generate interactive API documentation using Swagger UI automatically. As you define your API routes, FastAPI generates beautiful, interactive docs that are accessible directly from your browser. This makes it incredibly easy for the frontend team to see how the backend works, and it helps developers quickly test API endpoints during development.
4. Ease of Use 🛠️
FastAPI is built on Python type hints, which makes the code more readable and easier to debug. Despite its performance-oriented features, FastAPI is intuitive and allows us to write clean and organized code quickly.
1. Pre-Installed Packages 🧰
To get started with FastAPI, we need to install a few Python packages. These will handle everything from serving our API to uploading files and connecting to Firebase for storage.
- FastAPI: The web framework itself.
- Uvicorn: The ASGI server to run the FastAPI app.
- python-multipart: For handling file uploads.
- firebase-admin: To interact with Firebase services.
- Pydantic: Automatically used by FastAPI for data validation and parsing.
These are the basic packages we’ll need for our backend setup.
2. FastAPI & Uvicorn Setup
Once you’ve installed the required packages, we can create a basic FastAPI app. This app will handle requests and responses and serve our API.
pip install fastapi uvicorn python-multipart firebase-admin pydanticWith the packages installed, we can now set up our FastAPI application.
3. Swagger UI: The Game-Changer for API Docs
As you set up your API routes in FastAPI, one of the coolest features is the Swagger UI. FastAPI automatically generates interactive API documentation, which makes testing, debugging, and understanding the API a breeze. Once you launch the server, you can access the Swagger docs by navigating to:
Next Steps
Now that the backend is set up, the next steps are integrating resume file uploads and connecting the backend with the React frontend. We’ll be using Firebase to handle storage and make the resumes easily accessible.
In the next blog, we’ll go deeper into uploading resumes and interacting with the database. Stay tuned!
By using FastAPI, we’ve ensured that our backend is both fast and scalable. The automatic generation of Swagger docs is a huge productivity boost, making development smoother for both the backend and frontend teams. As we continue with the project, we’ll explore more about FastAPI’s capabilities and how to best leverage it for the Resume Analyzer.
Let me know your thoughts, or if you’ve faced any issues while setting up the backend! Happy coding! 🙌
