Setting Up FastAPI Project Step-by-Step

 

Introduction

In the previous blog, we learned what FastAPI is and why it’s gaining popularity. Now, let’s get hands-on and set up a FastAPI project from scratch the right way.

A good project setup helps you:

  • Write clean code.
  • Scale easily.
  • Avoid confusion later.

Prerequisites

Before we start, make sure you have:

  • Python 3.7+ installed 
  • Basic knowledge of Python 
  • pip installed 

Step 1: Create a Virtual Environment

Using a virtual environment keeps your dependencies clean.

python -m venv venv

Activate it:

Windows:

venv\Scripts\activate

Mac/Linux:

source venv/bin/activate

Step 2: Install FastAPI & Uvicorn

pip install fastapi uvicorn
  • FastAPI → framework
  • Uvicorn → ASGI server (runs your app) 

Step 3: Basic Project Structure

Here’s a clean and scalable folder structure:

fastapi-project/
│
├── app/
│   ├── main.py
│   ├── routes/
│   ├── models/
│   ├── schemas/
│   └── database/
│
├── venv/
├── requirements.txt
└── README.md

Explanation:

  • main.py → Entry point of the app
  • routes/ → API endpoints
  • models/ → Database models
  • schemas/ → Pydantic schemas
  • database/ → DB connection logic 

Step 4: Create Your First App File

Create main.py inside app/:

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"message": "FastAPI project is running!"}

Step 5: Run the Server
uvicorn app.main:app --reload
Breakdown:
  • app.main → folder + file

    • 

  • app → FastAPI instance

    • 

  • --reload → auto-restart on changes

    • 

Step 6: Open in Browser
Once the server is running:
Step 7: Create requirements.txt
Save dependencies: pip freeze > requirements.txt
This helps when deploying or sharing your project.
Best Practices for Setup
1. Always use virtual environments

2. Keep code modular (routes, models, schemas)

3. Use .env for secrets (later blogs)

4. Follow consistent naming conventions


Common Beginner Mistakes
1. Not using virtual environment
2. Running wrong module path (main:app vs app.main:app)
3. Forgetting --reload during development
4. Mixing all code in one file


Key Takeaways
  • FastAPI setup is simple and quick

    • 

  • Proper structure makes your project scalable

    • 

  • Uvicorn is required to run FastAPI apps

    • 

  • Always organize your code from the beginning

    •

Comments

Post a Comment

Popular posts from this blog

Database Integration in FastAPI (SQLAlchemy CRUD)

  Introduction So far, we’ve built APIs without storing data.But real-world applications need a database to: Store user data. Manage products. Persist application state. In this blog, we’ll connect FastAPI with a database using SQLAlchemy and perform CRUD operations . What is SQLAlchemy? SQLAlchemy is a powerful Python ORM (Object Relational Mapper) that allows you to: Interact with databases using Python code. Avoid writing raw SQL queries. Work with models instead of tables. Step 1: Install Dependencies pip install sqlalchemy (For SQLite, no extra driver needed) Project Structure Update app/ ├── main.py ├── database/ │ ├── connection.py ├── models/ │ ├── item.py ├── schemas/ │ ├── item.py ├── routes/ │ ├── item.py Step 2: Database Connection app/database/connection.py from sqlalchemy import create_engine from sqlalchemy . orm import sessionmaker , declarative_base DATABASE_URL = "sqlite:///./test.db" engine = create_engine ( DAT...
Read more

Middleware & CORS in FastAPI

  Introduction As your API grows, you may need to: Log requests Modify responses Handle cross-origin requests This is where Middleware and CORS (Cross-Origin Resource Sharing) come into play.They help control how requests and responses flow through your application. What is Middleware? Middleware is a function that runs: Before a request reaches your route After a response is returned Think of it as a pipeline layer between client and server. Request Flow with Middleware Client → Middleware → Route → Middleware → Response Creating Custom Middleware from fastapi import FastAPI , Request import time app = FastAPI () @ app . middleware ( "http" ) async def log_requests ( request : Request , call_next ): start_time = time . time() response = await call_next ( request ) process_time = time . time() - start_time print ( f"Request: { request . url} | Time: { process_time } " ) return response What This Middle...
Read more

Python Data Handling

Python Data Handling for Beginners In Python, data handling means storing, changing, and using data in different ways. In this post, you will learn: Lists Tuples Dictionaries Sets String Methods Type Conversion 1. Python Lists :Lists store many values in one variable.                fruits = ["apple", "banana", "mango"]                                                                       Method                          Use                                            append()          ...
Read more