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insurance-prediction-api's Introduction

  • The project is a demo for a class presentation in which I was asked by my lecturer to do a small demo on how we can use api as a way of deploying our machine learning model into production.
  • This demo is simplest one I could create given the limited time I had
  • I used FastAPI framework to build the API and link it with the model
  • I also used Streamlit as a frontend
  • Below is the architure of the application

API architecture

  • To try out the API on local machine you can create a local environment using Pipenv.

  • You can then install FastAPI and Streamlit using the following commands

    pipenv install fastapi

  • You will also need an ASGI server, use the following command to install

    pipenv install "uvicorn[standard]"

  • You can now start the server using the following command:

    uvicorn lm_api:app

  • You can read more on fastapi here

  • To install streamlit using the following command

    pipenv install streamlit

  • To start streamlit use the command below:

streamlit run frontend.py
  • Make sure you cd to src folder for the servers to start running

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