dd

import gradio as gr
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn import metrics

# Load Iris dataset
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# Train a Support Vector Classifier
clf = svm.SVC(kernel='linear')
clf.fit(X_train, y_train)

def iris_classifier(sepal_length, sepal_width, petal_length, petal_width):
    prediction = clf.predict([[sepal_length, sepal_width, petal_length, petal_width]])
    return iris.target_names[prediction[0]]

iface = gr.Interface(
    fn=iris_classifier, 
    inputs=["number", "number", "number", "number"], 
    outputs="text",
    title="Iris Classifier",
    description="Enter the measurements of an iris flower to predict its species."
)
iface.launch()