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Deploying a Flutter App with a FastAPI Backend on Google Cloud

In the rapidly evolving world of app development, building a robust and scalable application requires the right tools and frameworks. Flutter, with its rich UI capabilities, and FastAPI, known for its high performance, make for a powerful combination. When you deploy this duo on Google Cloud, you can achieve a seamless and efficient development cycle. In this article, we'll guide you through the process of deploying a Flutter app with a FastAPI backend on Google Cloud, providing you with detailed steps, code snippets, and actionable insights.

What is Flutter?

Flutter is an open-source UI software development toolkit created by Google. It allows developers to build natively compiled applications for mobile, web, and desktop from a single codebase. With widgets that provide a fast and expressive way to create UIs, Flutter has gained immense popularity among developers.

Use Cases for Flutter

  • Cross-Platform Development: Create apps for both iOS and Android with a single codebase.
  • Rapid Prototyping: Quickly develop and test app ideas.
  • High-Performance Apps: Build applications with smooth animations and responsive interfaces.

What is FastAPI?

FastAPI is a modern web framework for building APIs with Python 3.6+ based on standard Python type hints. It is designed for speed, allowing for fast development and high performance. FastAPI is ideal for building RESTful APIs, making it a perfect backend for your Flutter app.

Use Cases for FastAPI

  • Microservices: FastAPI is an excellent choice for building microservices due to its speed and flexibility.
  • Data-Driven Applications: Efficiently handle data operations and integrate with databases.
  • Real-Time Data Applications: Build applications that require real-time data processing.

Setting Up the Environment

Before deploying, you need to set up your development environment. Ensure you have the following installed:

pip install fastapi[all] uvicorn

Building the FastAPI Backend

Let's start by creating a simple FastAPI backend. Create a new directory for your FastAPI project and navigate into it:

mkdir fastapi-backend
cd fastapi-backend

Create a file called main.py and add the following code:

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
async def read_root():
    return {"Hello": "World"}

@app.get("/items/{item_id}")
async def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "query": q}

Running the FastAPI Server Locally

You can run the FastAPI application locally using Uvicorn:

uvicorn main:app --reload

Visit http://127.0.0.1:8000/docs in your browser to see the automatic API documentation generated by FastAPI.

Deploying FastAPI on Google Cloud

Step 1: Create a Google Cloud Project

  1. Go to the Google Cloud Console.
  2. Create a new project.
  3. Enable the Cloud Run and Cloud Build APIs.

Step 2: Prepare Your FastAPI App for Deployment

Create a Dockerfile in the same directory:

# Use the official Python image from the Docker hub
FROM python:3.9

# Set the working directory
WORKDIR /app

# Copy requirements.txt and install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy the FastAPI application code
COPY . .

# Expose the port that the app runs on
EXPOSE 8000

# Command to run the FastAPI app using Uvicorn
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Create a requirements.txt file:

fastapi[all]
uvicorn

Step 3: Build and Deploy the Docker Image

  1. Authenticate with Google Cloud:
gcloud auth login
gcloud config set project YOUR_PROJECT_ID
  1. Build the Docker image:
gcloud builds submit --tag gcr.io/YOUR_PROJECT_ID/fastapi-backend
  1. Deploy the Docker image to Cloud Run:
gcloud run deploy fastapi-backend --image gcr.io/YOUR_PROJECT_ID/fastapi-backend --platform managed --region YOUR_REGION --allow-unauthenticated

Follow the prompts to complete the deployment. After deployment, you will receive a URL to access your FastAPI backend.

Building the Flutter App

Now that your FastAPI backend is live, let’s create a simple Flutter app to interact with it. Create a new Flutter project:

flutter create flutter_app
cd flutter_app

Modify lib/main.dart

Replace the contents of lib/main.dart with the following code to call your FastAPI backend:

import 'package:flutter/material.dart';
import 'package:http/http.dart' as http;
import 'dart:convert';

void main() {
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      home: Scaffold(
        appBar: AppBar(title: Text('Flutter & FastAPI')),
        body: Center(child: FutureBuilder<String>(
          future: fetchData(),
          builder: (context, snapshot) {
            if (snapshot.connectionState == ConnectionState.waiting) {
              return CircularProgressIndicator();
            } else if (snapshot.hasError) {
              return Text('Error: ${snapshot.error}');
            } else {
              return Text('Response: ${snapshot.data}');
            }
          },
        )),
      ),
    );
  }

  Future<String> fetchData() async {
    final response = await http.get(Uri.parse('YOUR_FASTAPI_URL'));
    if (response.statusCode == 200) {
      return json.decode(response.body)['Hello'];
    } else {
      throw Exception('Failed to load data');
    }
  }
}

Run the Flutter App

  1. Connect your mobile device or start an emulator.
  2. Run the Flutter app:
flutter run

Conclusion

Deploying a Flutter app with a FastAPI backend on Google Cloud is a straightforward process that leverages powerful tools to create responsive and scalable applications. With this guide, you should now have a basic understanding of both frameworks and the steps necessary to bring your app to life.

Key Takeaways

  • Flutter provides a rich UI toolkit for cross-platform development.
  • FastAPI offers a high-performance backend framework ideal for RESTful APIs.
  • Google Cloud simplifies deployment and scaling of applications.

Whether you're building a small personal project or a large enterprise application, this combination can help you achieve your goals efficiently. Happy coding!

SR
Syed
Rizwan

About the Author

Syed Rizwan is a Machine Learning Engineer with 5 years of experience in AI, IoT, and Industrial Automation.