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| # Importing required Libraries | |
| from IPython.utils.py3compat import encode | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import pickle | |
| # Loading Machine Learning Objects | |
| def load_saved_objets(filepath='ML_items'): | |
| "Function to load saved objects" | |
| with open(filepath, 'rb') as file: | |
| loaded_object = pickle.load(file) | |
| return loaded_object | |
| # Instantiating ML_items | |
| loaded_object = load_saved_objets() | |
| pipeline_of_my_app = loaded_object["pipeline"] | |
| num_cols = loaded_object['numeric_columns'] | |
| cat_cols = loaded_object['categorical_columns'] | |
| encoder_categories = loaded_object["encoder_categories"] | |
| # Main function to collect the inputs process them and outpuT the predicition | |
| def predict_churn( | |
| TotalCharges, | |
| MonthlyCharges, | |
| tenure, | |
| StreamingTV, | |
| PaperlessBilling, | |
| DeviceProtection, | |
| TechSupport, | |
| InternetService, | |
| OnlineSecurity, | |
| StreamingMovies, | |
| PaymentMethod, | |
| Dependents, | |
| Parter, | |
| tenure_group, | |
| OnlineBackup, | |
| gender, | |
| SeniorCitizen, | |
| MultipleLines, | |
| Contract, | |
| PhoneService, | |
| ): | |
| df = pd.DataFrame( | |
| [ | |
| [ | |
| TotalCharges, | |
| MonthlyCharges, | |
| tenure, | |
| StreamingTV, | |
| PaperlessBilling, | |
| DeviceProtection, | |
| TechSupport, | |
| InternetService, | |
| OnlineSecurity, | |
| StreamingMovies, | |
| PaymentMethod, | |
| Dependents, | |
| Parter, | |
| tenure_group, | |
| OnlineBackup, | |
| gender, | |
| SeniorCitizen, | |
| MultipleLines, | |
| Contract, | |
| PhoneService, | |
| ] | |
| ], | |
| columns= num_cols + cat_cols, | |
| ).replace("", np.nan) | |
| df[cat_cols] = df[cat_cols].astype("object") | |
| # Passing data to pipeline to make prediction | |
| output = pipeline_of_my_app.predict(df) | |
| # Labelling Model output | |
| if output == 0: | |
| model_output = "No" | |
| else: | |
| model_output = "Yes" | |
| return model_output | |
| # Setting up app interface and data inputs | |
| inputs = [] | |
| with gr.Blocks() as demo: | |
| # Setting Titles for App | |
| gr.Markdown("<h2 style='text-align: center;'> Customer Churn Prediction App </h2> ", unsafe_allow_html=True) | |
| gr.Markdown("<h6 style='text-align: center;'> (Fill in the details below and click on PREDICT button to make a prediction for Customer Churn) </h6> ", unsafe_allow_html=True) | |
| with gr.Column(): #main frame | |
| with gr.Row(): #col 1 : for num features | |
| for i in num_cols: | |
| inputs.append(gr.Number(label=f"Input {i} ")) | |
| with gr.Row(): #col 2 : for cat features | |
| for (lab, choices) in zip(cat_cols, encoder_categories): | |
| inputs.append(gr.inputs.Dropdown( | |
| choices=choices.tolist(), | |
| type="value", | |
| label=f"Select {lab}", | |
| default=choices.tolist()[0],)) | |
| # Setting up preediction Button | |
| with gr.Row(): | |
| make_prediction = gr.Button("Predict") | |
| # Setting up prediction output Row | |
| with gr.Row(): | |
| output_prediction = gr.Text(label="Will Customer Churn?") | |
| make_prediction.click(predict_churn, inputs, output_prediction) | |
| # Launching app | |
| demo.launch() |