import gradio as gr
import joblib
import numpy as np
import sklearn


model = joblib.load('random_forest_model.pkl')  # Update with the actual path

# Define a function for classification
def classify(*features):
    # Convert the input features to a 2D numpy array
    features_array = np.array([features])
    # Make a prediction
    prediction = model.predict(features_array)
    return f"Predicted Age Category: {prediction[0]}"

# Create the Gradio interface
iface = gr.Interface(
    fn=classify,
    inputs=[
        gr.Slider(minimum=0, maximum=100, value=50, label=f"Feature {i+1}") for i in range(15)
    ],
    outputs="text",
    title="Age Classification",
    description="Enter the 15 features to classify the age category."
)

# Launch the interface
iface.launch()