Tobi-Ade/Machine-Learning-Deployment

This is an end to end machine learning project. The aim of this project is to develop a machine learning model and deploy as a web service.

Functional requirements:

• User should should be able to login with their credentials
• User should be able to make prediction

Non functional requirements:

• The system should be highly available
• The system should perform at an excellent level
• The system should be compatible with any operation system e.g Mac OS, Windows, iOS, Android

2. Build a model that will provide a solution
3. Wrap and host in a simple web service (Flask)
4. Build a small app (UI - CSS and HTML) that uses it
5. Set up a CI/CD retraining pipeline
6. set up a monitoring device (Aporia, WhyLabs)

Requirements

1. Flask / Django
2. Machine learning knwoledge
3. Software development
4. Devops

Access the web app

• To use the web service, click this link

Steps to run this project locally

Please note that it is advisable to carry out these steps in a virtual environment.

On your terminal:

- Initialize a git repository in your preferred directory using:

 git init

• Clone the repository to your local repository using:
  

  git clone https://github.com/Tobi-Ade/Machine-Learning-Deployment.git

  Now you should see all the project files in your directory
  
  

Runnning the Flask App

- Navigate into the flask app directory by running:

  cd ".\flask app"

• The requirements.txt file contains all the dependencies needed to run the project successfully. You can install these by running:

  pip install -r requirements.txt

• Now that all the dependencies are installed, you can simply start the api by running:

  python app.py

The flask api should start running at this stage. You can send a request and get instant results, or copy the url where the api is running and paste it in your browser, where you can interact with the api.

• The request.py file contains a sample request. To use this, keep the api running and open an new terminal window, navigate to the current directory, then run:

 python request.py

The request.py file can also be modified to see how the result changes. You can also create a custom request using the same format in the request.py.

Runnning the Django App

- Navigate into the django app directory by running:

  cd ".\Django app"

• The requirements.txt file contains all the dependencies needed to run the project successfully. You can install these by running:

  pip install -r requirements.txt

• Navigate into the project directory by running

  cd deployment

• If you check all the files in this directory you can find the manage.py file whihc is needed to run the app (mlApp). Now run:

  python manage.py runserver

• If you have a preferred address where you wish to run the server, you can do that by adding the address after "runserver". Example:
  

  python manage.py runserver 127.0.0.1:8080

This means the server will run on "127.0.0.1:8080"

Now you can copy the url where the serve is running and paste it in your browser, where you can interact with the web app.

Webpage Images

Homepage

Selection Page

Acceptance Page

Rejection Page