#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('assets\\num_imputer.joblib') # loading the imputer cat_imputer = joblib.load('assets\\cat_imputer.joblib') # loading the imputer encoder = joblib.load('assets\\encoder.joblib') # loading the encoder scaler = joblib.load('assets\\scaler.joblib') # loading the scaler model = joblib.load('assets\\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("assets\\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=False)