import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import Pipeline
import streamlit as st

df = pd.read_excel("cars.xls")

x = df.drop('Price', axis=1)
y = df['Price']


x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)


preprocess = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), ['Mileage', 'Cylinder', 'Liter', 'Doors']),
        ('cat', OneHotEncoder(), ['Make', 'Model', 'Trim', 'Type', 'Cruise', 'Sound', 'Leather'])
    ]
)


my_model = LinearRegression()


pipe = Pipeline(steps=[('preprocessor', preprocess), ('model', my_model)])


pipe.fit(x_train, y_train)


y_pred = pipe.predict(x_test)
print('RMSE', mean_squared_error(y_test, y_pred) ** 0.5)
print('R2', r2_score(y_test, y_pred))


st.title("II. El Araba Fiyatı Tahmin:red_car: @aysel_olcer")
st.write('Arabanın özelliklerini seçiniz')


make = st.selectbox('Marka', df['Make'].unique())
model = st.selectbox('Model', df[df['Make'] == make]['Model'].unique())
trim = st.selectbox('Trim', df[(df['Make'] == make) & (df['Model'] == model)]['Trim'].unique())
mileage = st.number_input('Kilometre', 100, 200000)
car_type = st.selectbox('Araç Tipi', df['Type'].unique())
cylinder = st.selectbox('Silindir', df['Cylinder'].unique())
liter = st.number_input('Yakıt Hacmi', 1, 10)
doors = st.selectbox('Kapı sayısı', df['Doors'].unique())
cruise = st.radio('Hız Sbt.', [True, False])
sound = st.radio('Ses Sistemi.', [True, False])
leather = st.radio('Deri döşeme.', [True, False])


def price(make, model, trim, mileage, car_type, cylinder, liter, doors, cruise, sound, leather):
    input_data = pd.DataFrame({
        'Make': [make],
        'Model': [model],
        'Trim': [trim],
        'Mileage': [mileage],
        'Type': [car_type],
        'Cylinder': [cylinder],
        'Liter': [liter],
        'Doors': [doors],
        'Cruise': [cruise],
        'Sound': [sound],
        'Leather': [leather]
    })
    prediction = pipe.predict(input_data)[0]
    return prediction

if st.button('Tahmin Et'):
    pred = price(make, model, trim, mileage, car_type, cylinder, liter, doors, cruise, sound, leather)
    st.write('Fiyat:red_car: $', round(pred, 2))