Iris_Flower_Specie_Prediction

Introduction

This project seeks to use machine learning to predict the species of an iris flower, given information on its petals and sepals. It also features an interface that makes it easier for users to interact with the model to predict the specie of an iris flower.

It comes as a challenge from the Slightly Techie community.

You can find the HuggingFace deployed app on here.

Process Description

The process begins with exporting the necessary items from the notebook, building an interface that works correctly, importing the necessary items for modelling, and then writing the code to process inputs. The process can be summarized as:

Export machine learning items from notebook,
Import machine learning items into the app script,
Build an interface,
Write a function to process inputs,
Pass values through the interface,
Recover these values in backend,
Apply the necessary processing,
Submit the processed values to the ML model to make the predictions,
Process the predictions obtained and display them on the interface.

Installation

To setup and run this project you need to have Python3 installed on your system. Then you can clone this repo. At the repo's root, use the code from below which applies:

Windows:

  python -m venv venv; venv\Scripts\activate; python -m pip install -q --upgrade pip; python -m pip install -qr requirements.txt

Linux & MacOs:

  python3 -m venv venv; source venv/bin/activate; python -m pip install -q --upgrade pip; python -m pip install -qr requirements.txt

NB: For MacOs users, please install Xcode if you have an issue.

You can then run the app (still at the repository root):

App built with Gradio Interface
```
  python app.py
```
With inbrowser = True defined, it should open a browser tab automatically. If it doesn't, type this address in your browser: http://127.0.0.1:7860/

Screenshots

Gradio App Interface with a Prediction

Contact Information