import gradio as gr
import joblib
import numpy as np

# Load trained model
model = joblib.load("artifacts/model.pkl")


def predict(stock2, stock3, stock4, stock5):
    # Convert input into correct shape (1 sample, 4 features)
    input_data = np.array([[stock2, stock3, stock4, stock5]])

    prediction = model.predict(input_data)[0]
    return float(prediction)


iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Number(label="Stock_2"),
        gr.Number(label="Stock_3"),
        gr.Number(label="Stock_4"),
        gr.Number(label="Stock_5"),
    ],
    outputs="number",
    title="Stock Price Predictor"
)

iface.launch()