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import streamlit as st
import pickle
from tensorflow.keras.models import load_model
import pandas as pd
import numpy as np
# Load All Files
with open('final_pipeline.pkl', 'rb') as file_1:
model_pipeline = pickle.load(file_1)
model_ann = load_model('customer_churn.h5')
def run():
with st.form(key='Churn_Customer_Prediction'):
user_id = st.text_input('User_ID', value='')
age = st.number_input('Age', min_value=23, max_value=65, value=23)
gender = st.selectbox('Gender', ('Male', 'Female'), index=1)
days_since_last_login = st.number_input('Last Login', min_value=0, max_value=26, value=0)
avg_time_spent = st.number_input('Avg. Time Spent', min_value=0, max_value=3236, value=0)
avg_transaction_value = st.number_input('Avg. Transaction Value', min_value=800, max_value=99915, value=29271)
avg_frequency_login_days = st.number_input('Avg. Frequency Login Days', min_value=0, max_value=73, value=0)
points_in_wallet = st.number_input('Points in Wallet', min_value=0, max_value=2070, value=0)
joining_date = st.date_input("Select Join Date")
last_visit_time = st.time_input('Last Visit Time')
st.markdown('---')
region_category = st.selectbox('Region Category', ('Village', 'Town', 'City'), index=1)
membership_category = st.selectbox('Membership Category', ('No Membership', 'Basic Membership',
'Silver Membership', 'Premium Membership',
'Gold Membership', 'Platinum Membership'), index=1)
preferred_offer_types = st.selectbox('Preffered Offer', ('Without Offers', 'Credit/Debit Card Offers',
'Gift Vouchers/Coupons'), index=1)
medium_of_operation = st.selectbox('Medium Ops', ('Desktop', 'Mobile', 'Both'
'Gift Vouchers/Coupons'), index=1)
internet_option = st.selectbox('Internet Ops', ('Wi-Fi', 'Fiber_Optic', 'Mobile-Data'), index=1)
feedback = st.selectbox('Feedback', ('Poor Website', 'Poor Customer Service', 'Poor Product Quality',
'Too many ads', 'No reason specified', 'Products always in Stock',
'Reasonable Price', 'Quality Customer Care', 'User Friendly Website'), index=1)
complaint_status = st.selectbox('Complaint Status', ('No Information Available', 'Not Aplicable', 'Unsolved',
'Solved', 'Solved in Follow-up'), index=1)
st.markdown('---')
joined_through_referral = st.selectbox('Join Through Referral', ('Yes', 'No'), index=1)
used_special_discount = st.selectbox('Use Special Discount', ('Yes', 'No'), index=1)
offer_application_preference = st.selectbox('Offer Application Preference', ('Yes', 'No'), index=1)
past_complaint = st.selectbox('Past Complaint', ('Yes', 'No'), index=1)
submitted = st.form_submit_button('Predict')
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])
data_inf_transform = model_pipeline.transform(data_inf)
a = st.dataframe(data_inf_transform)
b = ''
if len(data_inf_transform) == 0:
b = 'Not Churn'
else:
# Predict using ANN: Sequential API
y_pred_inf = model_ann.predict(data_inf_transform)
y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
if y_pred_inf == 0:
b = 'Not Churn'
else:
b = 'Churn'
if submitted:
st.write('# Prediction : ', b)
if __name__ == '__main__':
run() |