import gradio as gr import pickle # import time import pandas as pd import numpy as np from utils import create_new_columns, create_processed_dataframe import os pipeline_pkl = "full_pipeline.pkl" log_reg = "logistic_reg_class_model.pkl" # hist_df = "history.csv" # def check_csv(csv_file, data): # if os.path.isfile(csv_file): # data.to_csv(csv_file, mode='a', header=False, index=False, encoding='utf-8') # else: # history = data.copy() # history.to_csv(csv_file, index=False) def tenure_values(): cols = ['0-2', '3-5', '6-8', '9-11', '12-14', '15-17', '18-20', '21-23', '24-26', '27-29', '30-32', '33-35', '36-38', '39-41', '42-44', '45-47', '48-50', '51-53', '54-56', '57-59', '60-62', '63-65', '66-68', '69-71', '72-74'] return cols def predict_churn(gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges): data = [gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges] x = np.array([data]) dataframe = pd.DataFrame(x, columns=train_features) dataframe = dataframe.astype({'MonthlyCharges': 'float', 'TotalCharges': 'float', 'tenure': 'float'}) dataframe_ = create_new_columns(dataframe) try: processed_data = pipeline.transform(dataframe_) except Exception as e: raise gr.gradio('Kindly make sure to check/select all') else: # check_csv(hist_df, dataframe) # history = pd.read_csv(hist_df) processed_dataframe = create_processed_dataframe(processed_data, dataframe) predictions = model.predict_proba(processed_dataframe) return round(predictions[0][0], 3), round(predictions[0][1], 3) theme = gr.themes.Default().set(body_background_fill="#0E1117", background_fill_secondary="#FFFFFF", background_fill_primary="#262730", body_text_color="#FF4B4B", checkbox_background_color='#FFFFFF', button_secondary_background_fill="#FF4B4B") def load_pickle(filename): with open(filename, 'rb') as file: data = pickle.load(file) return data pipeline = load_pickle(pipeline_pkl) model = load_pickle(log_reg) train_features = ['gender', 'SeniorCitizen', 'Partner', 'Dependents','tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection','TechSupport','StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges'] # theme = gr.themes.Base() with gr.Blocks(theme=theme) as demo: gr.HTML("""

Customer Churn Classification App

Welcome Cherished User 👋

Start predicting customer churn.

""") with gr.Row(): gender = gr.Dropdown(label='Gender', choices=['Female', 'Male']) Contract = gr.Dropdown(label='Contract', choices=['Month-to-month', 'One year', 'Two year']) InternetService = gr.Dropdown(label='Internet Service', choices=['DSL', 'Fiber optic', 'No']) with gr.Accordion('Yes or no'): with gr.Row(): OnlineSecurity = gr.Radio(label="Online Security", choices=["Yes", "No", "No internet service"]) OnlineBackup = gr.Radio(label="Online Backup", choices=["Yes", "No", "No internet service"]) DeviceProtection = gr.Radio(label="Device Protection", choices=["Yes", "No", "No internet service"]) TechSupport = gr.Radio(label="Tech Support", choices=["Yes", "No", "No internet service"]) StreamingTV = gr.Radio(label="TV Streaming", choices=["Yes", "No", "No internet service"]) StreamingMovies = gr.Radio(label="Movie Streaming", choices=["Yes", "No", "No internet service"]) with gr.Row(): SeniorCitizen = gr.Radio(label="Senior Citizen", choices=["Yes", "No"]) Partner = gr.Radio(label="Partner", choices=["Yes", "No"]) Dependents = gr.Radio(label="Dependents", choices=["Yes", "No"]) PaperlessBilling = gr.Radio(label="Paperless Billing", choices=["Yes", "No"]) PhoneService = gr.Radio(label="Phone Service", choices=["Yes", "No"]) MultipleLines = gr.Radio(label="Multiple Lines", choices=["No phone service", "Yes", "No"]) with gr.Row(): MonthlyCharges = gr.Number(label="Monthly Charges") TotalCharges = gr.Number(label="Total Charges") Tenure = gr.Number(label='Months of Tenure') PaymentMethod = gr.Dropdown(label="Payment Method", choices=["Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"]) submit_button = gr.Button('Prediction') # print(type([[122, 456]])) with gr.Row(): with gr.Accordion('Churn Prediction'): output1 = gr.Slider(maximum=1, minimum=0, value=0.0, label='Yes') output2 = gr.Slider(maximum=1, minimum=0, value=0.0, label='No') # with gr.Accordion('Input History'): # output3 = gr.Dataframe() submit_button.click(fn=predict_churn, inputs=[gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges], outputs=[output1, output2]) # demo = gr.Interface(fn=predict_churn, inputs=[gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, # InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges], outputs=['slider', 'slider'], theme=theme) demo.launch(debug=True)