import joblib
import pandas as pd

# Load the model
mileage_model = joblib.load('mileage_predictor.pkl')
price_model = joblib.load('price_predictor.pkl')
year_model = joblib.load('year_predictor.pkl')

# Prepare input data for prediction
def prepare_input(data_dict):
    # Prepare a DataFrame from a dictionary of input data
    input_df = pd.DataFrame([data_dict])
    input_df['Car_Age'] = 2024 - input_df['Year']
    input_df.drop(columns=['Year'], inplace=True)

    return input_df

# Make prediction
def predict(input_data):
    # Predict mileage and price for a given input.
    prepared_data = prepare_input(input_data)
    mileage = mileage_model.predict(prepared_data)[0]
    price = price_model.predict(prepared_data)[0]
    year = year_model.predict(prepared_data)[0]
    
    return mileage, price, int(year)

# Sample data for prediction
data = {
    'Year': 2022,
    'Kilometers_Driven': 30000,
    'Fuel_Type': 'Petrol',
    'Transmission': 'Manual',
    'Owner_Type': 'First',
    'Location': 'Mumbai',
    'Engine CC': 1200,
    'Power': 85,
    'Seats': 5
}

# Make prediction 

predicted_mileage, predicted_price, predicted_year = predict(data)

print(f"Predicted Mileage (Km/L): {predicted_mileage:.2f}")
print(f"Predicted Price: ₹{predicted_price:,.2f} Lakhs")
print(f"Predicted Year: {predicted_year}")