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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)