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Flask Starter Template

A simple Flask app starter template. This is my preference to bootstrap my Flask projects and not a must follow structure, feel free to change whatever you dislike to fit what you are comfortable with.

Features

Here is a list of the available features:

  • Scalable Folder Structure: The application employs an isolated app directory structure to ensure code maintainability and readability.

  • API Ready: The template includes a ready-to-use API structure.

  • Web UI: The template includes a basic web user interface.

  • User Authentication: The template integrates Flask-Login for user authentication. It allows users to register, log in, and log out. It provides session management, secure password hashing, and user session tracking.

  • Rate Limiting: To protect your application from abuse, rate limiting is enforced.

  • CORS: Cross-origin resource sharing (CORS) is configured to manage the server's shared resources.

  • CACHING: The template includes caching to optimize performance and reduce server load. Caching stores frequently requested data temporarily, leading to faster API responses. It enhances user experience and helps handle high traffic efficiently.

  • Logging: The application includes logging capabilities to record relevant events, errors, and messages. Logging helps in monitoring and troubleshooting the application during development and production.

  • Tests: Unit tests are included to ensure the application's functionality and robustness.

  • Docker Support: A Dockerfile is included for building a Docker image of your application, facilitating easy deployment and scaling.

Getting Started

Running The Application

1.Clone the repository to your local machine:

git clone https://github.com/riad-azz/flask-template && cd flask-template

2.Install the required dependencies:

pip install -r requirements.txt

3.The application can be run with the following command:

python server.py

4.To enable the Ratelimit and Cache features make sure to copy the .env.example content and create a .env file:

# Flask Variables
SECRET_KEY="YOUR-SECRET-KEY"
# Flask Ratelimit
RATELIMIT_ENABLED="True"
RATELIMIT_STORAGE_URI="memory://" # or redis://localhost:6379/0
# Flask Cache
CACHE_ENABLED="True"
CACHE_TYPE="SimpleCache" # or RedisCache
CACHE_STORAGE_URL="YOUR-REDIS-URL" # Required only for CACHE_TYPE RedisCache

Note: for development you need to create a .env.dev file.

Running Tests

You can write tests in flask-template/tests, where you will also find some examples.

To run the tests simply use the command:

python -m pytest

You can switch between running the tests from .env.dev (development environment) or .env (production environment) by going to flask-template/tests/__init__.py and changing the value of FLASK_DEBUG:

import os

# Set 'False' to test with .env
# Set 'True' to test with .env.dev
os.environ["FLASK_DEBUG"] = "True"

Dockerize The Application

To run the application in Docker follow these steps:

1.Install Docker on your machine.

2.Build the Docker image for the application:

docker build -t my-flask-image .

3.Run the Docker image:

docker run -p 5000:5000 --name my-flask-container my-flask-image

Open your browser and visit http://localhost:5000 to see the website.

Flask API

This is how I like to set up my API in Flask. You might want to change this with flask-restful or whatever library that suits you.

You can check app/routes/api/tests.py to get an idea of how the API should work.

API Schemas

All the schemas served with the API that are passed to the success_response must be json serializable. In this example we use BaseModel from pydantic which allows us to turn the model into a dict using the model_dump function:

from pydantic import BaseModel


class TestModel(BaseModel):
    title: str
    content: str
# Flask modules
from flask import Blueprint

# Local modules
from app.schemas.test import TestModel
from app.utils.api import success_response

tests_bp = Blueprint("tests", __name__, url_prefix="/tests")


@tests_bp.route("/success", methods=['GET'])
def test_api_success():
    data = TestModel(title="riad-azz", content="Successful API response")
    data_dict = data.model_dump()
    return success_response(data_dict, 200)

Error handling

For API error handling use werkzeug.exceptions exception classes, and if you would like to create custom exceptions make sure that your exceptions inherit from HTTPException.

The API error handler can be found in app/routes/api/__init__.py:

# Flask modules
from flask import Blueprint
from werkzeug.exceptions import HTTPException
from flask_limiter.errors import RateLimitExceeded

# Other modules
import logging

# Local modules
from app.utils.api import error_response

api_bp = Blueprint("api", __name__, url_prefix="/api")


@api_bp.errorhandler(Exception)
def handle_error(error):
    if isinstance(error, RateLimitExceeded):
        current_limit = error.limit.limit
        return error_response(f"Too many requests: {current_limit}", 429)
    elif isinstance(error, HTTPException):
        return error_response(error.description, error.code)
    else:
        logging.error(error)
        return error_response()

If the exception is unknown the API will return a Internal Server Error by default from the error_response function.

API Response Examples

Run the server and visit the following paths to check the API responses:

Contributing

Contributions to improve this Flask app template are welcome. Please feel free to fork the repository, make changes, and submit a pull request.

License

This project is licensed under the terms of the MIT license. For more details, see the LICENSE file in the repository.