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.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
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=20, random_state=42)

preprocessor = ColumnTransformer(
    transformers= [
        ("num", StandardScaler(),["Mileage", "Cylinder", "Liter", "Doors"]),
        ("cat", OneHotEncoder(), ["Make", "Model", "Trim", "Type"])
    ]
)

model = LinearRegression()

pipeline = Pipeline(steps=[
    ("preprocessor", preprocessor), 
    ("regressor", model)
])

pipeline.fit(x_train, y_train)
pred = pipeline.predict(x_test)

rmse = mean_squared_error(pred, y_test)**.5
r2 = r2_score(pred, y_test)

def price_pred(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 = pipeline.predict(input_data)[0]
    return prediction

def main():
    st.title("MLOps Car Price Prediction :red_car:")
    st.write("Enter Car Details to predict the price")

    make = st.selectbox("Model", 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("Mileage", 200, 60000)
    car_type = st.selectbox("Type", df["Type"].unique())
    cylinder = st.selectbox("Cylinder", df["Cylinder"].unique())
    liter = st.number_input("Liter", 1, 6)
    doors = st.selectbox("Doors", df["Doors"].unique())
    cruise = st.radio("Cruise", [0,1])
    sound = st.radio("Sound", [0,1])
    leather = st.radio("Leather", [0,1])

    if st.button("Predict"):
        price = price_pred(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
        st.write(price)
        price = float(price)
        st.write(f"The predicted price is : ${price:.2f}")


if __name__ == "__main__":
    main()














    
    # make = st.selectbox("Make", df["Make"].unique())

    # model = st.selectbox("Model", df[df["Make"]==make]["Model"].unique())

    # trim = st.selectbox("Trim", df[df["Model"]==model]["Trim"].unique())