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#import modules
import numpy as np
import gradio as gr
import joblib
import pandas as pd
import os
from gradio.components import Dropdown, Number, Textbox
def load_model():
cwd = os.getcwd()
destination = os.path.join(cwd, "saved cap")
Final_model_file_path = os.path.join(destination, "Final_model.joblib")
preprocessor_file_path = os.path.join(destination, "preprocessor.joblib")
Final_model = joblib.load(Final_model_file_path)
preprocessor = joblib.load(preprocessor_file_path)
return Final_model, preprocessor
Final_model, preprocessor = load_model()
#define prediction function
def make_prediction(REGION, TENURE, MONTANT, FREQUENCE_RECH, REVENUE, ARPU_SEGMENT, FREQUENCE, DATA_VOLUME, ON_NET, ORANGE, TIGO, ZONE1, ZONE2, MRG, REGULARITY, FREQ_TOP_PACK):
#make a dataframe from input data
input_data = pd.DataFrame({'REGION':[REGION],
'TENURE':[TENURE],
'MONTANT':[MONTANT],
'FREQUENCE_RECH':[FREQUENCE_RECH],
'REVENUE':[REVENUE],
'ARPU_SEGMENT':[ARPU_SEGMENT],
'FREQUENCE':[FREQUENCE],
'DATA_VOLUME':[DATA_VOLUME],
'ON_NET':[ON_NET],
'ORANGE':[ORANGE],
'TIGO':[TIGO],
'ZONE1':[ZONE1],
'ZONE2':[ZONE2],
'MRG':[MRG],
'REGULARITY':[REGULARITY],
'FREQ_TOP_PACK':[FREQ_TOP_PACK]})
transformer = preprocessor.transform(input_data)
predt = Final_model.predict(transformer)
#return prediction
if predt[0]==1:
return "Customer will Churn"
return "Customer will not Churn"
# Create the input components for gradio
input_col1 = [Dropdown(choices=['DAKAR', 'THIES', 'SAINT-LOUIS', 'LOUGA', 'KAOLACK', 'DIOURBEL', 'TAMBACOUNDA', 'KAFFRINE', 'KOLDA']),
Dropdown(choices=['K > 24 month', 'I 18-21 month', 'H 15-18 month', 'G 12-15 month', 'J 21-24 month', 'F 9-12 month', 'E 6-9 month', 'D 3-6 month']),
Number(),
Number(),
Number(),
Number(),
Number(),
Number(),
Number()]
input_col2 = [Number(),
Number(),
Number(),
Number(),
Number(),
Number(),
Number(),
Number(),
Number(),
Dropdown(choices=['NO']),
Number(),
Number()]
output = Textbox(label='Prediction')
# Create the interface component
app = gr.Interface(fn=make_prediction, inputs=[input_col1, input_col2],
title="Customer Churn Predictor",
description="Enter the fields below and click the submit button to Make Your Prediction",
outputs=output)
app.launch(debug=True)
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