Iris Prediction Application

This is a FastAPI application that predicts the species of an Iris flower based on its sepal length, sepal width, petal length, and petal width. It utilizes machine learning to provide predictions along with the probabilities of each Iris species.

Demo Link for application

url - https://iris-fastapi-meer.onrender.com/

Features

• User-friendly web interface to input Iris flower measurements.
• Predictions of Iris species (Setosa, Versicolor, or Virginica).
• Displays probabilities for each species.

Requirements

To run this application, you need the following dependencies:

• Python 3.12.4
• FastAPI
• Jinja2
• Uvicorn
• pydantic
• scikit-learn

You can install all dependencies using the following command:

pip install -r requirements.txt

How to Run

1. Clone the repository:

   git clone https://github.com/utkarshg1/FastAPI-ML.git
   cd FastAPI-ML

2. Set Up Python Virtual environment

   python -m venv env

3. Activaate virtual environment

   source env/bin/activate # env\Script\activate.bat for Windows machine

4. Upgrade pip

   python -m pip install --upgrade pip

5. Install dependencies:

   pip install -r requirements.txt

6. Run the application:

   python main.py

7. Open your web browser and go to http://localhost:8000. You will see a form where you can input the sepal and petal measurements for the Iris flower.

8. After submitting the form, the application will display the predicted species along with the probabilities for each species.

Endpoints

GET /

Renders the form where users can input the measurements for the Iris flower.

POST /predict

Handles form submission and returns the predicted Iris species along with the probabilities of each species.

Example

Here's an example of how to use the web interface:

1. Input the following measurements into the form:

   • Sepal length: 5.1
   • Sepal width: 3.5
   • Petal length: 1.4
   • Petal width: 0.2

2. Click the submit button.

3. The application will display the predicted species along with the probabilities of each species.

Dockerhub Link

Dockerhub link - https://hub.docker.com/r/utkarshg1/fastapi-iris

License

This project is licensed under the Apache License 2.0, See the LICENSE file for more details.

Acknowledgments

• This project is built using FastAPI.
• The machine learning model used for predictions is powered by scikit-learn.