Spaces:
Runtime error
Runtime error
| 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("https://huggingface.co/spaces/Abubakari/churn_prediction_App/raw/main/telecom%20churn.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() | |