import gradio as gr import pickle import pandas as pd import numpy as np import joblib from PIL import Image num_imputer = joblib.load('numerical_imputer.joblib') cat_imputer = joblib.load('categorical_imputer.joblib') encoder = joblib.load('encoder.joblib') scaler = joblib.load('scaler.joblib') model = joblib.load('Final_model.joblib') # 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] }) # Selecting categorical and numerical columns separately 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'] # Apply the imputers on the input data 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 predictions = model.predict(final_df) # Make predictions using the model #predictions = model.predict(final_df) # Convert the numpy array to an integer #prediction_label = int(predictions.item()) prediction_label = "Beware!!! This customer is likely to Churn" if predictions.item() == "Yes" else "This customer is Not likely churn" return prediction_label #return predictions input_interface=[] with gr.Blocks(css=".gradio-container {background-color: powderblue}") as app: img = gr.Image("C:/Users/user/Documents/AZUBI PROGRAM/CAREER ACELERATOR/LP4-buiding an app/Gradio/lp4_part2-1/telecom churn.png").style(height='13') Title=gr.Label('CUSTOMER CHURN PREDICTION APP') with gr.Row(): Title with gr.Row(): img #with gr.Blocks() as app: # with gr.Blocks(css=".gradio-interface-container {background-color: powderblue}"): #with gr.Row(): # gr.Label('Customer Churn Prediction Model') with gr.Row(): gr.Markdown("This app predicts whether a customer will leave your company or not. Enter the details of the customer below to see the result") #with gr.Row(): #gr.Label('This app predicts whether a customer will leave your company or not. Enter the details of the customer below to see the result') with gr.Row(): with gr.Column(scale=3, min_width=600): input_interface = [ gr.components.Radio(['male', 'female'], label='Select your gender'), gr.components.Number(label="Are you a Seniorcitizen; No=0 and Yes=1"), gr.components.Radio(['Yes', 'No'], label='Do you have Partner'), gr.components.Dropdown(['No', 'Yes'], label='Do you have any Dependents? '), gr.components.Number(label='Lenght of tenure (no. of months with Telco)'), gr.components.Radio(['No', 'Yes'], label='Do you have PhoneService? '), gr.components.Radio(['No', 'Yes'], label='Do you have MultipleLines'), gr.components.Radio(['DSL', 'Fiber optic', 'No'], label='Do you have InternetService'), gr.components.Radio(['No', 'Yes'], label='Do you have OnlineSecurity?'), gr.components.Radio(['No', 'Yes'], label='Do you have OnlineBackup?'), gr.components.Radio(['No', 'Yes'], label='Do you have DeviceProtection?'), gr.components.Radio(['No', 'Yes'], label='Do you have TechSupport?'), gr.components.Radio(['No', 'Yes'], label='Do you have StreamingTV?'), gr.components.Radio(['No', 'Yes'], label='Do you have StreamingMovies?'), gr.components.Dropdown(['Month-to-month', 'One year', 'Two year'], label='which Contract do you use?'), gr.components.Radio(['Yes', 'No'], label='Do you prefer PaperlessBilling?'), gr.components.Dropdown(['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'], label='Which PaymentMethod do you prefer?'), gr.components.Number(label="Enter monthly charges"), gr.components.Number(label="Enter total charges") ] with gr.Row(): submit_btn = gr.Button('Submit') predict_btn = gr.Button('Predict') # Define the output interfaces output_interface = gr.Label(label="churn") predict_btn.click(fn=predict, inputs=input_interface, outputs=output_interface) app.launch(share=True)