import pandas as pd
import gradio as gr
import joblib


le=joblib.load('le_col.pkl')
scale=joblib.load('std_col.pkl')
lr=joblib.load('model.pkl')

le_col=['Prand']
std_col=['year','miles','condition']

def predicion_car_price(y,m,c,p):
    try:
        input_data=pd.DataFrame({
            'year':[y],
            'miles':[m],
            'condition':[c],
            'Prand':[p]
        })
        for col in le_col:
            input_data[col]=le[col].transform(input_data[col])
        input_data[std_col]=scale.transform(input_data[std_col])
        prediction=lr.predict(input_data)
        return prediction[0]
    except Exception as e:
        return str(e)
gr.Interface(
    inputs=[
        gr.Number(label='year'),
        gr.Number(label='miles'),
        gr.Number(label='condition'),
        gr.Dropdown([
    "Toyota", "Mercedes-Benz", "Ford", "Honda", "BMW", "Chevrolet", "Nissan", "Kia",
    "Subaru", "Jeep", "Audi", "Volkswagen", "Hyundai", "Lexus", "Land", "Dodge", "Acura",
    "Mazda", "Ram", "Volvo", "Porsche", "INFINITI", "Cadillac", "Chrysler", "GMC", "Alfa",
    "Jaguar", "MINI", "Maserati", "Buick", "Lincoln", "Mitsubishi", "FIAT", "Scion",
    "Aston", "Genesis", "Karma", "McLaren", "Rolls-Royce", "Bentley", "Pontiac", "Saturn"],label='Prand')
    ],
    fn=predicion_car_price,
    outputs=gr.Textbox(label='Prediction'),
    title='Predictin_Car_Price'
).launch()