Submission for Capstone Project

Description

This repository contains the final submission for the Coursera IBM AI Workflow Certification. All requirements are satisfied:

• Working Docker image
• Unit Testing
• Performance monitoring
• Functional API and API documentation

Configuration

1. Copy and rename the .env.example to .env
2. Define an API_KEY to be used for your application in the .env

Installation

To use the code locally, install the dependencies. It is recommended to use a virtualenv.

pip install -r requirements.txt

Running

The application can either be run in your local python environment or using Docker.

Locally

1. To start the server locally, run:

   IS_LOCAL=true uvicorn app.main:app --host 0.0.0.0 --port 8089
   

2. Open http://localhost:8089/docs in your browser to see the API documentation.

3. Enter the API key defined previously to use the API routes.

Docker

1. Build the docker container:

   docker build -t ai-workflow-capstone .
   

2. Start the docker container

   docker run --rm -p 8089:8080 ai-workflow-capstone:latest
   

API Documentation

The API documentation is available in the docs folder complying with the OpenAPI specification 3.0.2

Model Training

To train the model start the application as described in the section Running and use the /api/v1/model/train route. This will train all models with the available training data.

Testing

The tox tool is used for testing. To run the linter, formatter, unit test and security test, make sure you have installed tox then run it from your command line:

tox