import gradio as gr
import joblib
import numpy as np

# Charger le modèle en spécifiant le chemin absolu
model_path = "./linear_regression_model.joblib"
lr = joblib.load(model_path)

# Fonction de prédiction
def predict_price(*features):
    features = np.array(features).reshape(1, -1)
    prediction = lr.predict(features)[0]
    return round(prediction, 2)

# Interface Gradio
input_labels = ["Kms_Driven", "Present_Price",	"Fuel_Type",	"Seller_Type",	"Transmission",	"Age",	"Selling_Price"]  
inputs = [gr.Number(label=label) for label in input_labels]

output = gr.Number(label="Predicted Price")

interface = gr.Interface(fn=predict_price, inputs=inputs, outputs=output, title="Car Price Prediction")

# Lancer l'application
if __name__ == "__main__":
    interface.launch()