import pickle
import streamlit as st
import pandas as pd

with open('best_model_ada_tuning.pkl', 'rb') as file_1:
    model_svm_tuning = pickle.load(file_1)

def run():
    st.title("Online Retail Customer Churn Prediction")
    st.write("## by Putra Rizqa Yasira")
    st.write("### Batch SBY-004")
    with st.form(key='Form Parameter'):
        customerid = st.number_input("Customer Id : ",min_value=1001,max_value=999999,step=1)
        age  = st.slider("Age :",18,99,1)
        gender = st.radio("Gender",["Male","Female","Prefer not to say"])
        st.markdown('---')
        annualincome = st.number_input("Annual Income (in thousand) : ",min_value=0,max_value=999999,step=100)
        totalspend = st.number_input("Total Spend at Retail : ",min_value=0,max_value=999999,step=100)
        st.markdown('---')
        yearsascustomer  = st.slider("Years as Customer :",0,30,1)
        numofpurchase  = st.slider("Number of Purchase :",0,999,1)
        avgtransaction = st.number_input("Average Transaction Amount : ",min_value=0,max_value=999999,step=100)
        st.markdown('---')
        numofreturn  = st.slider("Number of Return :",0,999,1)
        numofsupport  = st.slider("Number of Support Contact :",0,999,1)
        st.markdown('---')
        satisfaction = st.radio("How Good Us?",["Very Good","Good","Neutral","Bad","Very Bad"])
        lastdaypurchase  = st.slider("Last Purchase Days ago :",0,999,1)
        email = st.radio("Give Us Your Email",["Nope","Of Course!"])
        promotionresponse = st.radio("How About Our Promotion",["I Like To Know More!","I Don't Interest","Very Bad, Unsubscribed"])
        submitted = st.form_submit_button('Predict') 
    
    if gender == "Prefer not to say": gender = "Other"
    if satisfaction == "Very Good":
        satisfaction = 5
    elif satisfaction == "Good":
        satisfaction = 4
    elif satisfaction == "Neutral":
        satisfaction = 3
    elif satisfaction == "Bad":
        satisfaction = 2
    satisfaction = 1

    if email == "Nope":
        email = False
    email = True

    if promotionresponse == "I Like To Know More!":
        promotionresponse = "Responded"
    elif promotionresponse == "I Don't Interest":
        promotionresponse = "Ignored"
    promotionresponse = "Unsubscribed"

    data_inf = {
    'Customer_ID': customerid,
    'Age': age,
    'Gender':gender,
    'Annual_Income':annualincome,
    'Total_Spend':totalspend,
    'Years_as_Customer':yearsascustomer,
    'Num_of_Purchases':numofpurchase,
    'Average_Transaction_Amount':avgtransaction,
    'Num_of_Returns':numofreturn,
    'Num_of_Support_Contacts':numofsupport,
    'Satisfaction_Score':satisfaction,
    'Last_Purchase_Days_Ago':lastdaypurchase,
    'Email_Opt_In':email,
    'Promotion_Response':promotionresponse
    }

    df =  pd.DataFrame([data_inf])

    def age_group(age):
        if age <= 18:
            return  "teenager"
        elif age <= 59:
            return "adult"
        else:
            return "elderly"
        
    df['age_group'] = df['Age'].apply(age_group)

    if submitted:
        y_pred_inf = model_svm_tuning.predict(df)

        st.write('## Hasil Prediksi Klasifikasi Churn : ')
        if y_pred_inf == False:
            st.write("Customer Terklasifikasi Tidak Churn")
        elif y_pred_inf == True:
            st.write("Customer Terklasifikasi Churn")

if __name__ == '__main__':
    run()