Flower Classifier

This is a Django application. The core functionality of the application is a flower classifier built using scikit-learn. The model training code can be found in the train.py file.

The application is deployed on Render using Docker. The Dockerfile contains instructions to build an image from an online image that I published to dockerHub. It can be found at https://hub.docker.com/r/bisratberhanu/flower_classifier. The flower classifier accepts important features and determines the type of flower from four possible types.

deployed on : https://flower-classifier-tgog.onrender.com/

Demo

Commands to Run the Application

There are two ways to run the application

1. save the following code in a file called docker-compose.yml and run docker compose up in the terminal

version: "3.9"
services:
  app:
    container_name: django_app
    restart: always
    image: bisratberhanu/flower_classifier:django_app
    ports:
      - 8070:8000
    command: ["python3", "manage.py", "runserver", "0.0.0.0:8000"]

2. If building the image locally is preferred,this could be the case if minor changes are to be made to the code. In this case,follow the following steps:
   • Clone the repository
   • change the docker-compose with the following code:

version: "3.9"
services:
  app:
    container_name: django_app
    restart: always
    build: .  #this builds locally
    ports:
      - 8070:8000
    command: ["python3", "manage.py", "runserver", "0.0.0.0:8000"]

• Run docker compose up --build in the terminal

Important Files

• train.py contains the code to train the model.

• decision_tree_classifier.pkl is the trained model.

• views.pycontains the API post and get methods.

• templates/index.html contains the html, css and Javascript code.

Features

• The application has a home page where the user can input the features of the flower and get the prediction.
• the application also shows the path the decision classifier took to reach the conclusion.