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| # IMPORT LIBRARIES | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import pickle | |
| import xgboost as xgb | |
| from xgboost import XGBClassifier | |
| # Function to load ML toolkit | |
| def load_ml_toolkit(file_path): | |
| with open(file_path, "rb") as file: | |
| loaded_toolkit = pickle.load(file) | |
| return loaded_toolkit | |
| # Importing the toolkit | |
| loaded_toolkit = load_ml_toolkit(r"export/App_toolkit.pkl") | |
| encoder = loaded_toolkit["encoder"] | |
| scaler = loaded_toolkit["scaler"] | |
| # Import the model | |
| model = XGBClassifier() | |
| model.load_model(r"xgb_model.json") | |
| #Colmuns to work with | |
| input_cols = ["tenure", "montant", "frequence_rech", "arpu_segment", "frequence", "data_volume", "regularity", "freq_top_pack"] | |
| columns_to_scale = ["montant", "frequence_rech", "arpu_segment", "frequence", "data_volume", "regularity", "freq_top_pack"] | |
| categoricals = ["tenure"] | |
| # Function to process inputs and return prediction | |
| def process_and_predict(*args, encoder=encoder, scaler=scaler, model=model): | |
| # Convert inputs into a DataFrame | |
| input_data = pd.DataFrame([args], columns=input_cols) | |
| # Encode the categorical column | |
| input_data["tenure"] = encoder.transform(input_data["tenure"]) | |
| # Scale the numeric columns | |
| input_data[columns_to_scale] = scaler.transform(input_data[columns_to_scale]) | |
| # Making the prediction | |
| model_output = model.predict(input_data) | |
| return {"Prediction: CHURN": float(model_output[0]), "Prediction: STAY": 1-float(model_output[0])} | |
| #App Interface | |
| with gr.Blocks() as turn_on_the_gradio: | |
| gr.Markdown("# 📞 EXPRESSO TELECOM CUSTOMER CHURN ☎️") | |
| gr.Markdown(''' | |
| ## WELCOME CHERISHED USER👋 | |
| ### PLEASE GO AHEAD AND MAKE A PREDICTION 🙂''') | |
| # Receiving Inputs | |
| gr.Markdown("**SECTION ONE**") | |
| gr.Markdown("**CUSTOMER NETWORK ACTTIVITIES**") | |
| with gr.Row(): | |
| montant = gr.Slider(label="Top-up amount", minimum=20, step=1, interactive=True, value=1, maximum= 500000) | |
| data_volume = gr.Slider(label="Number of connections", minimum=0, step=1, interactive=True, value=1, maximum= 2000000) | |
| with gr.Row(): | |
| frequence_rech = gr.Slider(label="Recharge Frequency", minimum=1, step=1, interactive=True, value=1, maximum=220) | |
| freq_top_pack = gr.Slider(label="Top Package Activation Frequency", minimum=1, step=1, interactive=True, value=1, maximum=1050) | |
| regularity = gr.Slider(label="Regularity (out of 90 days)", minimum=1, step=1, interactive=True, value=1, maximum=90) | |
| tenure = gr.Dropdown(label="Tenure (time on the network)", choices=["D 3-6 month", "E 6-9 month", "F 9-12 month", "G 12-15 month", "H 15-18 month", "I 18-21 month", "J 21-24 month", "K > 24 month"], value="K > 24 month") | |
| gr.Markdown("**SECTION 2**") | |
| gr.Markdown("**CUSTOMER INCOME DETAILS**") | |
| with gr.Row(): | |
| arpu_segment = gr.Slider(label="Income over the last 90 days", step=1, maximum=287000, interactive=True) | |
| frequence = gr.Slider(label="Number of times the customer has made an income", step=1, minimum=1, maximum=91, interactive=True) | |
| # Output Prediction | |
| output = gr.Label("...") | |
| submit_button = gr.Button("Submit") | |
| submit_button.click(fn = process_and_predict, | |
| outputs = output, | |
| inputs=[tenure, montant, frequence_rech, arpu_segment, frequence, data_volume, regularity, freq_top_pack]) | |
| turn_on_the_gradio.launch(inbrowser= True) |