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#Importing the libraries | |
import gradio as gr | |
import pickle | |
import pandas as pd | |
import numpy as np | |
import joblib | |
from PIL import Image | |
#using joblib to load the model: | |
num_imputer = joblib.load('num_imputer.joblib') # loading the imputer | |
cat_imputer = joblib.load('cat_imputer.joblib') # loading the imputer | |
encoder = joblib.load('encoder.joblib') # loading the encoder | |
scaler = joblib.load('scaler.joblib') # loading the scaler | |
model = joblib.load('ml.joblib') # loading the model | |
# Create a function that applies the ML pipeline and makes predictions | |
def predict(gender,SeniorCitizen,Partner,Dependents, tenure, PhoneService,MultipleLines, | |
InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies, | |
Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges): | |
# Create a dataframe with the input data | |
input_df = pd.DataFrame({ | |
'gender': [gender], | |
'SeniorCitizen': [SeniorCitizen], | |
'Partner': [Partner], | |
'Dependents': [Dependents], | |
'tenure': [tenure], | |
'PhoneService': [PhoneService], | |
'MultipleLines': [MultipleLines], | |
'InternetService': [InternetService], | |
'OnlineSecurity': [OnlineSecurity], | |
'OnlineBackup': [OnlineBackup], | |
'DeviceProtection': [DeviceProtection], | |
'TechSupport': [TechSupport], | |
'StreamingTV': [StreamingTV], | |
'StreamingMovies': [StreamingMovies], | |
'Contract': [Contract], | |
'PaperlessBilling': [PaperlessBilling], | |
'PaymentMethod': [PaymentMethod], | |
'MonthlyCharges': [MonthlyCharges], | |
'TotalCharges': [TotalCharges] | |
}) | |
# Create a list with the categorical and numerical columns | |
cat_columns = [col for col in input_df.columns if input_df[col].dtype == 'object'] | |
num_columns = [col for col in input_df.columns if input_df[col].dtype != 'object'] | |
# Impute the missing values | |
input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns]) | |
input_df_imputed_num = num_imputer.transform(input_df[num_columns]) | |
# Encode the categorical columns | |
input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat).toarray(), | |
columns=encoder.get_feature_names_out(cat_columns)) | |
# Scale the numerical columns | |
input_df_scaled = scaler.transform(input_df_imputed_num) | |
input_scaled_df = pd.DataFrame(input_df_scaled , columns = num_columns) | |
#joining the cat encoded and num scaled | |
final_df = pd.concat([input_encoded_df, input_scaled_df], axis=1) | |
final_df = final_df.reindex(columns=['SeniorCitizen','tenure','MonthlyCharges','TotalCharges', | |
'gender_Female','gender_Male','Partner_No','Partner_Yes','Dependents_No','Dependents_Yes','PhoneService_No', | |
'PhoneService_Yes','MultipleLines_No','MultipleLines_Yes','InternetService_DSL','InternetService_Fiber optic', | |
'InternetService_No','OnlineSecurity_No','OnlineSecurity_Yes','OnlineBackup_No','OnlineBackup_Yes','DeviceProtection_No', | |
'DeviceProtection_Yes','TechSupport_No','TechSupport_Yes','StreamingTV_No','StreamingTV_Yes','StreamingMovies_No', | |
'StreamingMovies_Yes','Contract_Month-to-month','Contract_One year','Contract_Two year','PaperlessBilling_No', | |
'PaperlessBilling_Yes','PaymentMethod_Bank transfer (automatic)','PaymentMethod_Credit card (automatic)','PaymentMethod_Electronic check', | |
'PaymentMethod_Mailed check']) | |
# Make predictions using the model | |
predict = model.predict(final_df) | |
prediction_label = "THIS CUSTOMER WILL CHURN" if predict.item() == "Yes" else "THIS CUSTOMER WILL NOT CHURN" | |
return prediction_label | |
#return predictions | |
#define the input interface | |
input_interface = [] | |
with gr.Blocks(css=".gradio-container {background-color:silver}") as app: | |
title = gr.Label('VODAFONE CUSTOMER CHURN PREDICTION') | |
img = gr.Image("VODA.png").style(height= 210 , width= 1250) | |
with gr.Row(): | |
gr.Markdown("This application provides predictions on whether a customer will churn or remain with the Company. Please enter the customer's information below and click PREDICT to view the prediction outcome.") | |
with gr.Row(): | |
with gr.Column(scale=3.5, min_width=500): | |
input_interface = [ | |
gr.components.Radio(['male', 'female'], label='What is your Gender?'), | |
gr.components.Number(label="Are you a Seniorcitizen? (No=0 and Yes=1), 55years and above"), | |
gr.components.Radio(['Yes', 'No'], label='Do you have a Partner?'), | |
gr.components.Dropdown(['No', 'Yes'], label='Do you have any Dependents?'), | |
gr.components.Number(label='Length of Tenure (No. of months with Vodafone)'), | |
gr.components.Radio(['No', 'Yes'], label='Do you use Phone Service?'), | |
gr.components.Radio(['No', 'Yes'], label='Do you use Multiple Lines?'), | |
gr.components.Radio(['DSL', 'Fiber optic', 'No'], label='Do you use Internet Service?'), | |
gr.components.Radio(['No', 'Yes'], label='Do you use Online Security?'), | |
gr.components.Radio(['No', 'Yes'], label='Do you use Online Backup?'), | |
gr.components.Radio(['No', 'Yes'], label='Do you use Device Protection?'), | |
gr.components.Radio(['No', 'Yes'], label='Do you use the Tech Support?'), | |
gr.components.Radio(['No', 'Yes'], label='Do you Streaming TV?'), | |
gr.components.Radio(['No', 'Yes'], label='Do you Streaming Movies?'), | |
gr.components.Dropdown(['Month-to-month', 'One year', 'Two year'], label='Please what Contract Type do you Subscribe to?'), | |
gr.components.Radio(['Yes', 'No'], label='Do you use Paperless Billing?'), | |
gr.components.Dropdown(['Electronic check', 'Mailed check', 'Bank transfer (automatic)', | |
'Credit card (automatic)'], label='What type of Payment Method do you use please?'), | |
gr.components.Number(label="How much is you Monthly Charges?"), | |
gr.components.Number(label="How much is your Total Charges?") | |
] | |
with gr.Row(): | |
predict_btn = gr.Button('Predict') | |
# Define the output interfaces | |
output_interface = gr.Label(label="churn", type="label", style="font-weight: bold; font-size: larger; color: red") | |
predict_btn.click(fn=predict, inputs=input_interface, outputs=output_interface) | |
app.launch(share=True) | |