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, always a good option when you have something running on your local system 😁. Note: manage.py runs the streamlit app as a subprocess. feel free to move it as per your need.

What's Coming up ?

- Form support for Image Classification
- New Test Cases 
- S3 support

Drop me email for any queries on subir.verma48@gmail.com