import streamlit as st 
import pandas as pd
import numpy as np 
import pickle 
import json
import datetime
import tensorflow as tf
from tensorflow.keras.models import load_model

# Load Models
with open('final_pipeline.pkl', 'rb') as file_1:
  model_pipeline = pickle.load(file_1)

model_ann = load_model ('churn_model.h5')

def run():

    with st.form(key='from_fifa_2022'):
        user_Id = st.text_input('UserID', value='')
        age = st.number_input('Age', min_value=10, max_value=80, step=1, help='Usia Prediksi')
        st.write('M = Male | F = Female')
        gender = st.selectbox('Gender', ('M', 'F'), index=1)
        region_category = st.selectbox('Region', ('Town', 'City', 'Village'), index=1)
        membership_category = st.selectbox('Membership', ('No Membership', 'Basic Membership', 'Gold Membership', 'Silver Membership', 'Premium Membership', 'Platinum Membership'), index=1)
        st.markdown('---')
        
        joining_date = st.date_input('JoiningDate', datetime.date(2019, 7, 6))
        joined_through_referral = st.selectbox('Referral', ('Yes', 'No'), index=1)
        preferred_offer_types = st.selectbox('Offer', ('Gift Vouchers/Coupons', 'Credit/Debit Card Offers', 'Without Offers'), index=1)
        medium_of_operation = st.selectbox('Devices', ('Desktop', 'Smartphone', 'Both'), index=1)
        internet_option = st.selectbox('InternetOption', ('Wi-Fi', 'Mobile_Data', 'Fiber_Optic'), index=1)
        last_visit_time = st.date_input('LastVisit', datetime.date(2019, 7, 6))
        st.markdown('---')
        
        days_since_last_login = st.number_input('DaysLastLogin', min_value=1, max_value=100)
        avg_time_spent = st.number_input('AverageTimeSpent', min_value=1, max_value=1000)
        avg_transaction_value = st.number_input('Transaction', min_value=1, max_value=100000)
        avg_frequency_login_days = st.number_input('FrequencyLogin', min_value=1, max_value=31)
        points_in_wallet = st.number_input('WalletPoint', min_value=10, max_value=10000)
        st.markdown('---')
        
        used_special_discount = st.selectbox('UsedDiscount', ('Yes', 'No'), index=1)
        offer_application_preference = st.selectbox('OfferAplication', ('Yes', 'No'), index=1)
        past_complaint = st.selectbox('PastComplaint', ('Yes', 'No'), index=1)
        complaint_status = st.selectbox('ComplaintStatus', ('Not Applicable', 'Unsolved', 'Solved', 'Solved in Follow-up', 'No Information Available'), index=1)
        feedback = st.selectbox('Feedback', ('Poor Product Quality', 'No reason specified', 'Too many ads', 'Poor Website',
                                            'Poor Customer Service', 'Reasonable Price', 'User Friendly Website', 'Products always in Stock', 'Quality Customer Care'), index=1)
        
        submitted = st.form_submit_button('Prediction')

    data_inf = {
        'user_Id': user_Id,
        'age': age,
        'gender': gender,
        'region_category': region_category,
        'membership_category': membership_category,
        'joining_date': joining_date,
        'joined_through_referral': joined_through_referral,
        'preferred_offer_types': preferred_offer_types,
        'medium_of_operation': medium_of_operation,
        'internet_option': internet_option,
        'last_visit_time': last_visit_time,
        'days_since_last_login': days_since_last_login,
        'avg_time_spent': avg_time_spent,
        'avg_transaction_value': avg_transaction_value,
        'avg_frequency_login_days': avg_frequency_login_days,
        'points_in_wallet': points_in_wallet,
        'used_special_discount': used_special_discount,
        'offer_application_preference': offer_application_preference,
        'past_complaint': past_complaint,
        'complaint_status': complaint_status,
        'feedback': feedback
    }

    data_inf = pd.DataFrame([data_inf])
    st.dataframe(data_inf)
    
    if submitted:
        # input pipeline
        data_inf_final = model_pipeline.transform(data_inf)
        
        # Predict Model
        y_pred_inf = model_ann.predict(data_inf_final)
        y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
        if y_pred_inf == 0:
            hasil_prediksi = "Not Churn"
        else:
            hasil_prediksi = "Churn"
        
        # Print Hasil prediksi
        st.write('Prediksi Kemungkinan : ', hasil_prediksi)

if __name__== '__main__':
    run()