Spaces:
Build error
Build error
| 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 | |
| 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.Error('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(""" | |
| <h1 style="color:white; text-align:center">Customer Churn Classification App</h1> | |
| <h2 style="color:white;">Welcome Cherished User 👋 </h2> | |
| <h4 style="color:white;">Start predicting customer churn.</h4> | |
| """) | |
| 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.launch(debug=True) |