import gradio as gr
import joblib

# Save the trained model
joblib.dump(clf, "decision_tree_iris.pkl")

# Load iris dataset labels
iris = load_iris()
target_names = iris.target_names

# Define prediction function
def predict_species(sepal_length, sepal_width, petal_length, petal_width):
    model = joblib.load("decision_tree_iris.pkl")
    features = [[sepal_length, sepal_width, petal_length, petal_width]]
    prediction = model.predict(features)[0]
    return f"Predicted Species: {target_names[prediction]}"

# Build Gradio UI
interface = gr.Interface(
    fn=predict_species,
    inputs=[
        gr.Number(label="Sepal Length"),
        gr.Number(label="Sepal Width"),
        gr.Number(label="Petal Length"),
        gr.Number(label="Petal Width")
    ],
    outputs="text",
    title="Decision Tree Classifier - Iris Dataset 🌸",
    description="Enter flower measurements and let the Decision Tree predict the species."
)

interface.launch()

        __
        __