app.py

import gradio as gr
import joblib
import tensorflow as tf
from tensorflow.keras.models import load_model

import os
import pandas as pd
os.environ["KERAS_BACKEND"] = "jax"
	
import keras

loaded_model = keras.models.load_model('loan_approval_model1.keras')
loaded_preprocessor = joblib.load('preprocessor1.pkl')
def predict_loan_approval(
    Age, AnnualIncome, CreditScore, EmploymentStatus, EducationLevel,
    Experience, LoanAmount, LoanDuration, MaritalStatus,
    HomeOwnershipStatus, MonthlyDebtPayments, CreditCardUtilizationRate,
    NumberOfOpenCreditLines,BankruptcyHistory, LoanPurpose, PreviousLoanDefaults, PaymentHistory,
    LengthOfCreditHistory, SavingsAccountBalance, CheckingAccountBalance,
    TotalAssets, TotalLiabilities, MonthlyIncome,
    JobTenure, NetWorth, MonthlyLoanPayment,RiskScore
):
    input_data = pd.DataFrame({
        'Age': [Age],
        'AnnualIncome': [AnnualIncome],
        'CreditScore': [CreditScore],
        'EmploymentStatus': [EmploymentStatus],
        'EducationLevel': [EducationLevel],
        'Experience': [Experience],
        'LoanAmount': [LoanAmount],
        'LoanDuration': [LoanDuration],
        'MaritalStatus': [MaritalStatus],
        'HomeOwnershipStatus': [HomeOwnershipStatus],
        'MonthlyDebtPayments': [MonthlyDebtPayments],
        'CreditCardUtilizationRate': [CreditCardUtilizationRate],
        'NumberOfOpenCreditLines': [NumberOfOpenCreditLines],
        'BankruptcyHistory': [BankruptcyHistory],
        'LoanPurpose': [LoanPurpose],
        'PreviousLoanDefaults': [PreviousLoanDefaults],
        'PaymentHistory': [PaymentHistory],
        'LengthOfCreditHistory': [LengthOfCreditHistory],
        'SavingsAccountBalance': [SavingsAccountBalance],
        'CheckingAccountBalance': [CheckingAccountBalance],
        'TotalAssets': [TotalAssets],
        'TotalLiabilities': [TotalLiabilities],
        'MonthlyIncome': [MonthlyIncome],
        'JobTenure': [JobTenure],
        'NetWorth': [NetWorth],
        'MonthlyLoanPayment': [MonthlyLoanPayment],
        'RiskScore': [RiskScore]
    })

    processed_data = loaded_preprocessor.transform(input_data)

    prediction = loaded_model.predict(processed_data)[0][0]

    approval_status = "Одобрено" if prediction > 0.5 else "Отказано"
    confidence = prediction if approval_status == "Одобрено" else 1 - prediction
    return approval_status, f"{confidence*100:.2f}%", float(prediction)


# Создание интерфейса Gradio
inputs = [
    gr.Number(label="Возраст", minimum=18, maximum=100),
    gr.Number(label="Годовой доход", minimum=0),
    gr.Number(label="Credit Score", minimum=300, maximum=850),
    gr.Dropdown(label="Работа", choices=["Employed", "Self-Employed", "Unemployed"]),
    gr.Dropdown(label="Образование", choices=["High School", "Associate", "Bachelor", "Master", "Doctorate"]),
    gr.Number(label="Опыт работы", minimum=0, maximum=50),
    gr.Number(label="Размер кредита", minimum=0),
    gr.Number(label="Длительность выплаты", minimum=1),
    gr.Dropdown(label="Семейное положение", choices=["Single", "Married", "Divorced", "Widowed"]),
    gr.Dropdown(label="Собственность", choices=["Own", "Mortgage", "Rent", "Other"]),
    gr.Number(label="Выплата в месяц", minimum=0),
    gr.Number(label="Коэффициент использования кредитной карты", minimum=0, maximum=1),
    gr.Number(label="Количество открытых кредитных линий", minimum=0),
    gr.Radio(label="История банкротства", choices=[0, 1]),
    gr.Dropdown(label="Цель кредита", choices=["Home", "Debt Consolidation", "Education", "Auto", "Other"]),
    gr.Radio(label="Предыдущие дефолты по кредитам", choices=[0, 1]),
    gr.Number(label="Кол-во предыдущих платежей(месяц)", minimum=0),
    gr.Number(label="Длина кредитной истории (лет)", minimum=0),
    gr.Number(label="Баланс сберегательного счета", minimum=0),
    gr.Number(label="Баланс дебетовой карты", minimum=0),
    gr.Number(label="Всего активов", minimum=0),
    gr.Number(label="Всего обязательств", minimum=0),
    gr.Number(label="Ежемесячный доход", minimum=0),
    gr.Number(label="Стаж работы (лет)", minimum=0),
    gr.Number(label="Полная стоимость имущества", minimum=0),
    gr.Number(label="Ежемесячный платеж по кредиту", minimum=0),
    gr.Number(label="Оценка риска", minimum=0, maximum=100)
] 



# Создание и запуск интерфейса
iface = gr.Interface(
    fn=predict_loan_approval,
    inputs=inputs,
    outputs=[
        gr.Label(label="Ответ модели"),
        gr.Textbox(label="Уверенность модели"),
        gr.Number(label="Вероятность(от 0 до 1)")
    ],
    title="Калькулятор одобрения кредита",
    description='',

)

iface.launch(debug=True)