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

# Load dataset and model
iris = load_iris()
X = iris.data
y = iris.target

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X, y)

# Prediction function
def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
    sample = [[sepal_length, sepal_width, petal_length, petal_width]]
    pred = model.predict(sample)[0]
    return f"🌺 Predicted Species: {iris.target_names[pred].capitalize()}"

# Gradio Interface
interface = gr.Interface(
    fn=predict_iris,
    inputs=[
        gr.Slider(4.0, 8.0, value=5.5, label="Sepal Length (cm)"),
        gr.Slider(2.0, 4.5, value=3.0, label="Sepal Width (cm)"),
        gr.Slider(1.0, 7.0, value=4.0, label="Petal Length (cm)"),
        gr.Slider(0.1, 2.5, value=1.2, label="Petal Width (cm)")
    ],
    outputs="text",
    title="🌿 Iris Flower Species Classifier (Random Forest)",
    description="Adjust the flower's features to predict its species using Random Forest."
)

interface.launch()

        __
        __