Wittline / fastapi-template

Completely Scalable FastAPI based template for Machine Learning, Deep Learning and any other software project which wants to use Fast API as an API framework.

Fast-API 🚀

Why This ? 🤨

Need Clean and Scalable Code Architecture for ML/DL and NLP driven micro-service based Projects ?

Introduction: Structuring of API

• api_template: Contains all the API related Code Base.

  • manage.py: Only entry point for API. Contains no logic.
  • .env: Most important file for your api and contains global configs. Acoid using application/variable level configs here.
  • application: It contains all your api related codes and test modules. I prefer keeping application folder at global.
  • logs: Logs is self-explanatory. FYI it will not contain any configuration information, just raw logs. Feel free to move according to your comfort but not inside the application folder.
  • models: As a part of Machine-Learning/ Deep-Learning app you might need to add model files here or if you have huge files on cloud add symlinks if possibles.
  • resources: To store any documentation, application related csv/txt/img files etc.
  • settings: Logger/DataBase/Model global settings files in yaml/json format.

• application:

  • main: priority folder of all your application related code.
    • 🏗 infrastructure: Data Base and ML/DL models related backbone code
    • 📮 routers: API routers and they strictly do not contain any business logic
    • 📡 services: All processing and business logic for routers here at service layer
    • ⚒ utility:
      • config_loader Load all application related config files from settings directory
      • logger Logging module for application
      • manager A manager utility for Data Related Task which can be common for different services
    • 🐍 config.py: Main config of application, inherits all details from .env file
  • test: Write test cases for your application here.
  • initializer.py: Preload/Initialisation of Models and Module common across application. Preloading model improves inferencing.

Running Locally ? 📍

Run Command uvicorn manage:app --host 0.0.0.0 --port 8000

Docker Support 🐳

docker build -t fastapi-image  .
docker run -d --name fastapi-container -p 8000:8000 fastapi-image

Sample Demo App ~ Powered by Streamlit ⚡️

Always good to have an interface to show a quick demo 😁. Note: manage.py runs the streamlit app as a subprocess. feel free to move it as per your need.

What is new ?

• Form Support for Image Classification
• Cutelogs GUI Integration for Easy LogsView

Drop me email for any queries on [e-mail]