app.py

#import modules
import numpy as np
import gradio as gr
import joblib
import pandas as pd
import itertools
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()]

def flatten(list_of_lists):
    return itertools.chain.from_iterable(list_of_lists)

inputs = flatten([input_col1, input_col2])

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)