import gradio as gr
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

# Load the Iris dataset and train a model
iris = load_iris()
X, y = iris.data, iris.target
clf = RandomForestClassifier()
clf.fit(X, y)

# Define the prediction function
def predict_iris(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]]

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_iris,
    inputs=[
        gr.components.Number(label="Sepal Length (cm)"),
        gr.components.Number(label="Sepal Width (cm)"),
        gr.components.Number(label="Petal Length (cm)"),
        gr.components.Number(label="Petal Width (cm)")
    ],
    outputs="text"
)

iface.launch()