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

st.title("Churn Prediction for IT cloud Client")

# import model and preprocess

model = load_model('model_tuned.hdf5')
preprocess = pickle.load(open("preprocessing.pkl", "rb"))

st.write('fill your profile:')

# user input

age = st.slider(label='age:', min_value=0, max_value=150, value=42, step=1)
sex = st.radio(label='gender:', options=['Female', 'Male'])
reg = st.radio(label='Region?', options=['Town', 'City', 'Village'])
mbr = st.selectbox(label='Membership?', options=['Platinum Membership', 'Premium Membership',
                                                                       'Gold Membership', 'Silver Membership',
                                                                       'Basic Membership', 'No Membership'])
ten = st.number_input(label='How long you have been using our services (in months)?:', min_value=0, max_value=999, value=42, step=1)
ref = st.radio(label='you join us via referral?', options=['Yes', 'No'])
ofr = st.radio(label='Which offer that you are using rn?', options=['Credit/Debit Card Offers',
                                                                               'Gift Vouchers/Coupons',
                                                                                'Without Offers'])
med = st.radio(label='What gadget for transaction do you usually use?', options=['Smartphone',
                                                                                'Desktop',
                                                                                'Both'])
itt = st.radio(label='internet type?', options=['Wi-Fi', 'Mobile_Data', 'Fiber_Optic'])
day = st.number_input(label='last login?:', min_value=0, max_value=999, value=42, step=1)
avt = st.number_input(label='spend on the website (in minutes)?:', min_value=0.00, max_value=9999.99, value=0.00, step=0.01)
mch = st.number_input(label='monthly charge:', min_value=0.00, max_value=9999999.99, value=0.00, step=0.01)
alp = st.number_input(label='login period in days:', min_value=0.000000, max_value=999.999999, value=0.000000, step=0.000001)
pts = st.number_input(label='points in wallet:', min_value=0.000000, max_value=99999.999999, value=0.000000, step=0.000001)
spc = st.radio(label='special discount?', options=['Yes', 'No'])
ofa = st.radio(label='prefer offers?', options=['Yes', 'No'])
com = st.radio(label='past complaint?', options=['Yes', 'No'])
cst = st.selectbox(label='your past complaint status?', options=['Solved', 'Solved in Follow-up', 'Unsolved',
                                                                       'Not Applicable', 'No Information Available'])
fdb = st.selectbox(label='your past feedback?', options=['Too many ads', 'Poor Product Quality', 'Poor Website',
                                                                    'Poor Customer Service', 'Products always in Stock',
                                                                    'Reasonable Price', 'Quality Customer Care',
                                                                    'User Friendly Website', 'No reason specified'])

# convert into dataframe

data = pd.DataFrame({'age': [age],
                'gender': [sex],
                'region_category': [reg],
                'membership_category':[mbr],
                'tenure': [ten],
                'joined_through_referral': [ref],
                'preferred_offer_types': [ofr],
                'medium_of_operation': [med],
                'internet_option': [itt],
                'days_since_last_login': [day],
                'avg_time_spent': [avt],
                'avg_transaction_value': [mch],
                'avg_frequency_login_days': [alp],
                'points_in_wallet':[pts],
                'used_special_discount': [spc],
                'offer_application_preference': [ofa],
                'past_complaint': [com],
                'complaint_status': [cst],
                'feedback': [fdb]
                })

# convert gender to the real values in table

if sex == 'Female':
    sex = 'F'
else:
   sex = 'M'

# preprocess the input from user

data_final = preprocess.transform(data)

# prediction
    
if st.button('Predict'):
    prediction = model.predict(data_final)
    prediction = np.where(prediction >= 0.5, 1, 0)
    
    if prediction == 0:
        prediction = 'Congratulations, this person will absolutely stay!'
    else:
        prediction = 'dangers gawat, we need to save this person!'

    st.write('Prediction result: ')
    st.write(prediction)