app.py

import gradio as gr
import asyncio
import joblib
import pandas as pd

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

def keep_alive():
    try:
        predict_loan_status(10000, 10, 600, 250, 5000)
        return "ok"
    except Exception as e:
        return "error:", str(e)
        
def predict_loan_status(
        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',
                                 ])

    scaler = joblib.load('std_scaler.bin')
    # Convert the dictionary to a 2D array
    input_array = user_input_df.values
    # print(user_input_df)
    scaled_array = scaler.transform(input_array)
    # print('scaled array', scaled_array)
    prediction = model.predict(scaled_array)
    # print('Prediction: ', prediction)

    return prediction[0]


options = ['Current', 'Completed', 'ChargedOff']

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")

]

outputs = gr.Label()
title = "Loan Status Classifier"
description = (
    "Enter the details of the loan to check the status of the loan."
)

with gr.Blocks() as demo:
    gr.Interface(
        fn=predict_loan_status,
        inputs=inputs,
        outputs=outputs,
        title=title,
        description=description,
    ).render()

    with gr.Row(visible=False):
        ka_out = gr.Textbox(label="keep_alive_out")
        ka_btn = gr.Button("keep_alive_btn")
        ka_btn.click(fn=keep_alive,
                  inputs=None,
                  outputs=ka_out,
                  api_name="keep_alive"
                 )
    
demo.launch(share=True, ssr_mode=False)

# def greet(name):
#     return "Hello " + name + "!!"
#
#
# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()