import gradio as gr
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

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

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define model
model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))

# Train model
model.fit(X_train, y_train)

# Define a function for prediction
def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
    features = [[sepal_length, sepal_width, petal_length, petal_width]]
    prediction = model.predict(features)
    return iris.target_names[prediction[0]]

# Create Gradio interface
iris_interface = gr.Interface(
    fn=predict_iris,
    inputs=["number", "number", "number", "number"],
    outputs="text",
    title="Iris clarrification model",
    description="Measurements -->  species prediction.",
    examples=[
        [5.1, 3.5, 1.4, 0.2],
        [6.9, 3.1, 5.4, 2.1]
    ]
)

# Launch the interface
iris_interface.launch()