Iris Species Prediction App

This web application predicts the species of an iris flower based on its sepal and petal dimensions. It is built with Streamlit and uses a pre-trained machine learning model.

Demo App Link

URL - https://joblib-example.onrender.com/

Getting Started

Prerequisites

• Python 3.12.4
• Streamlit
• Scikit-learn
• Joblib
• Pandas
• Docker (optional, for containerization)

Installation

1. Clone the repository:

   git clone https://github.com/utkarshg1/Joblib-example.git
   cd Joblib-example

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the application:

   python3 -m streamlit run app.py

   Access the app at http://localhost:8501.

Using Docker

To run the app with Docker:

1. Run the Docker container:

   docker compose up --build

   Access the app at http://localhost:8501.

Usage

Once the app is running, input the sepal length, sepal width, petal length, and petal width. Click on "Predict" to get the predicted species and the corresponding probabilities.

Dockerhub link

Dockerhub Link - https://hub.docker.com/r/utkarshg1/streamlit-iris

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

• The Iris dataset is sourced from the UCI Machine Learning Repository.
• Streamlit is used for the UI, and Scikit-learn is used for the machine learning model.