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

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)


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



my_model=LinearRegression()
pipe=Pipeline(steps=[("preprocessor",preproccer),("model",my_model)])



pipe.fit(X_train,y_train)


y_pred=pipe.predict(X_test)
rmse=mean_squared_error(y_test,y_pred)**0.5
r2=r2_score(y_test,y_pred)


#modeli yayma, kullanıma sunma


# ### Streamlit





import streamlit as st



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
st.title("Car Price Prediction :blue_car: @neslisahozturk")
st.write("Select the features")
make=st.selectbox("Brand",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",200,60000)
car_type=st.selectbox("Type",df[(df['Make']==make) & (df['Model']==model) & (df['Trim']==trim )]['Type'].unique())
cylinder=st.selectbox("Cylinder",df['Cylinder'].unique())
liter=st.number_input("Liter",1,6)
doors=st.selectbox("Door",df['Doors'].unique())
cruise=st.radio("Velocity Cons.",[True,False])
sound=st.radio("Sound System",[True,False])
leather=st.radio("Leather seats",[True,False])


if st.button("Prediction"):
	pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)

	st.write("Predicted Price ",round(pred[0],2))