import streamlit as st
import pandas as pd
import numpy as np
import joblib

# Load model
model = joblib.load("src/final_model.pkl")
features = joblib.load("src/model_features.pkl")

st.title("🚗 Used Car Price Predictor")

# Simple inputs
model_year = st.number_input("Model Year", 1990, 2024, 2018)
milage = st.number_input("Milage", 0, 500000, 50000)
horsepower = st.number_input("Horsepower", 50, 1500, 200)

car_age = 2024 - model_year
milage_per_year = milage / (car_age if car_age > 0 else 1)

# Create dataframe
input_data = pd.DataFrame([{
    "model_year": model_year,
    "milage": milage,
    "horsepower": horsepower,
    "car_age": car_age,
    "milage_per_year": milage_per_year
}])

# Feature alignment
for col in features:
    if col not in input_data.columns:
        input_data[col] = 0

input_data = input_data[features]

if st.button("Predict Price"):
    prediction = model.predict(input_data)
    prediction = np.expm1(prediction)
    st.success(f"Estimated Price: ${prediction[0]:,.2f}")