Iris Classification Model

Enter the measurements of the iris flower to predict its species.

Sepal Length:

Sepal Width:

Petal Length:

Petal Width:

API Usage

You can also access this model via the API.

cURL

curl -X POST "https://nipun-ml-deploy-app.hf.space/predict"      -H "Content-Type: application/json"      -d '{"sepal_length": 5.1, "sepal_width": 3.5, "petal_length": 1.4, "petal_width": 0.2}'

Python

import requests

url = "https://nipun-ml-deploy-app.hf.space/predict"
data = {
    "sepal_length": 5.1,
    "sepal_width": 3.5,
    "petal_length": 1.4,
    "petal_width": 0.2
}

response = requests.post(url, json=data)
print(response.json())

JavaScript

fetch("https://nipun-ml-deploy-app.hf.space/predict", {
    method: "POST",
    headers: {
        "Content-Type": "application/json"
    },
    body: JSON.stringify({
        sepal_length: 5.1,
        sepal_width: 3.5,
        petal_length: 1.4,
        petal_width: 0.2
    })
})
.then(response => response.json())
.then(data => console.log(data));