import joblib
import pandas as pd
import streamlit as st

dist_dict = {'0-1 Miles' : 1,
             '1-2 Miles' : 2,
             '2-5 Miles' : 3,
             '5-10 Miles' : 4,
             '10+ Miles' : 5}

model = joblib.load('model.joblib')
unique_values = joblib.load('unique_values.joblib')

unique_Marital_Status =  unique_values["Marital Status"]
unique_Gender =  unique_values["Gender"]
unique_Home_Owner =  unique_values["Home Owner"]
unique_Region =  unique_values["Region"]
unique_Commute_Distance =  unique_values["Commute Distance"]


def main():
    st.title("Bike buyer")

    with st.form("questionaire"):
        Marital_Status = st.selectbox("Marital Status", options=unique_Marital_Status)
        Gender = st.selectbox("Gender", options=unique_Gender)
        Home_Owner = st.selectbox("Home Owner", options=unique_Home_Owner)
        Region = st.selectbox("Region", options=unique_Region)
        Commute_Distance = st.selectbox("Commute Distance", options=unique_Commute_Distance)
        Age = st.slider("Age", min_value=0, max_value=100)


        # clicked==True only when the button is clicked
        clicked = st.form_submit_button("Predict bike buyer")
        if clicked:
            result=model.predict(pd.DataFrame({"Marital Status": [Marital_Status],
                                               "Gender": [Gender],
                                               "Home Owner": [Home_Owner],
                                               "Region" : [Region],
                                               "Commute Distance": [dist_dict[Commute_Distance]],
                                               "Age": [Age]}))
            # Show prediction
            if result == [1]:
                i = 'Yes'
            else :
                i = 'No'
            st.success(f'Purchased Bike ={i}')
# Run main()
if __name__ == "__main__":
    main()