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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
import tensorflow as tf
# Load All Files
# Load the Models
with open('final_pipeline.pkl', 'rb') as file_1:
model_pipeline = pickle.load(file_1)
with open('model_encoder.pkl','rb') as file_2:
encoder_ord = pickle.load(file_2)
model_ann = tf.keras.models.load_model('churn_model.h5')
def run():
with st.form(key='from_churn'):
user_id = st.text_input('User id', value='')
age = st.number_input('Age', min_value=0, max_value=100, value=0)
gender = st.selectbox('Gender', ('M', 'F'), index=1)
region_category = st.selectbox('Region', ('Town', 'City', 'Village'), index=1)
st.markdown('---')
membership_category = st.selectbox('Membership Category', ('Basic Membership', 'No Membership', 'Gold Membership', 'Silver Membership', 'Premium Membership', 'Platinum Membership'), index=1)
joining_days = st.number_input('joining_days', min_value=0, max_value=1000, value=0)
joined_through_referral = st.selectbox('Join Through Referral', ('Yes', 'No'), index=1)
preferred_offer_types = st.selectbox('Offer Type', ('Gift Vouchers/Coupons', 'Credit/Debit Card Offers', 'Without Offers'), index=1)
medium_of_operation = st.selectbox('Gadget Type', ('Desktop', 'Smartphone', 'Both'), index=1)
internet_option = st.selectbox('Internet Type', ('Wi-Fi', 'Mobile_Data', 'Fiber_Optic'), index=1)
days_since_last_login = st.number_input('Days Since Last Login', min_value=0, max_value=100, value=0)
avg_time_spent = st.number_input('Average Time Spent', min_value=0, max_value=3000, value=0)
avg_transaction_value = st.number_input('Average Transaction Value', min_value=0, max_value=100000, value=0)
avg_frequency_login_days = st.number_input('Average Login Days', min_value=0, max_value=100, value=0)
points_in_wallet = st.number_input('Points in Wallet', min_value=0, max_value=3000, value=0)
used_special_discount = st.selectbox('Used Special Discount', ('Yes', 'No'), index=1)
offer_application_preference = st.selectbox('Offer Preference', ('Yes', 'No'), index=1)
past_complaint = st.selectbox('Past Complaint', ('Yes', 'No'), index=1)
complaint_status = st.selectbox('Complaint Status', ('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('Predict')
data_inf = {
'user_id': user_id,
'age': age,
'gender': gender,
'region_category': region_category,
'membership_category': membership_category,
'joining_days': joining_days,
'joined_through_referral': joined_through_referral,
'preferred_offer_types': preferred_offer_types,
'medium_of_operation': medium_of_operation,
'internet_option': internet_option,
'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:
# Split between Numerical Columns and Categorical Columns
enc_columns = ['membership_category']
data_inf[enc_columns] = encoder_ord.fit_transform(data_inf[enc_columns])
# Feature Scaling and Feature Encoding
data_inf_transform = model_pipeline.transform(data_inf)
#data_inf_num_scaled = model_scaler.transform(data_inf_num)
#data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
#data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)
# Predict using Linear Regression
y_pred_inf = model_ann.predict(data_inf_transform)
y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
st.write('# Churn Risk : ', str(int(y_pred_inf)))
if __name__=='__main__':
run() |