app.py

import asyncio
import joblib
import gradio as gr
import joblib
import pandas as pd
from gradio_client import Client

model = joblib.load("loan_status_classifier_pipeline.joblib")
asyncio.set_event_loop_policy(asyncio.DefaultEventLoopPolicy())


def predict(
        loan_original_amount,
        credit_score_range_lower,
        stated_monthly_income,
        investors,
        monthly_loan_payment,):

    input_dict = {
        'LoanOriginalAmount': loan_original_amount,
        'CreditScoreRangeLower': credit_score_range_lower,
        'StatedMonthlyIncome': stated_monthly_income,
        'Investors': investors,
        'MonthlyLoanPayment': monthly_loan_payment,

    }

    user_input_df = pd.DataFrame(data=[[loan_original_amount,
                                        credit_score_range_lower,
                                        stated_monthly_income,
                                        investors,
                                        monthly_loan_payment]],
                                 columns=[
                                     'LoanOriginalAmount',
                                     'CreditScoreRangeLower',
                                     'StatedMonthlyIncome',
                                     'Investors',
                                     'MonthlyLoanPayment',
                                 ])

    response = model.predict(user_input_df.values)
    print(f"Response: {response}")

    return response[0]

inputs = [
    gr.Slider(1000, 100000, label="Loan Original Amount"),
    gr.Slider(100, 2000, step=1, label='Credit Score Range (Lower)'),
    gr.Slider(1000, 100000, step=10, label="Stated Monthly Income"),
    gr.Slider(0, 1000, step=1, label='Number of Investors'),
    gr.Slider(20, 5000, step=5, label="Monthly Loan Payment")

]
options = ['Current', 'Completed', 'ChargedOff']
outputs = gr.Label()
title = "Loan Status Classifier"
description = (
    "Enter the details of the loan to check the status of the loan."
)
gr.Interface(
    fn=predict,
    inputs=inputs,
    outputs=outputs,
    title=title,
    description=description,
    api_name="predict"
).launch(share=True, ssr_mode=False)