app.py

import joblib
import pandas as pd
import streamlit as st 


model = joblib.load('model (1).joblib')
unique_values = joblib.load('unique_values (1).joblib')
    
unique_Married_Single =  unique_values["Married/Single"]
unique_House_Ownership =  unique_values["House_Ownership"]
unique_Car_Ownership =  unique_values["Car_Ownership"]
unique_Profession =  unique_values["Profession"]
unique_CITY =  unique_values["CITY"]
unique_STATE =  unique_values["STATE"]


def main():
    st.title("Loan Risk_Flag Analysis")

    with st.form("questionaire"):
        Income = st.slider("Income", min_value=10000, max_value=9999999)
        Age = st.slider("Age", min_value=10, max_value=100)
        Experience = st.slider("Experience", min_value=0, max_value=20)
        CURRENT_JOB_YRS = st.slider("CURRENT_JOB_YRS", min_value=0, max_value=14)
        CURRENT_HOUSE_YRS = st.slider("CURRENT_HOUSE_YRS", min_value=10, max_value=14)

        Married_Single = st.selectbox("Married/Single", unique_Married_Single)
        House_Ownership = st.selectbox("House_Ownership", unique_House_Ownership)
        Car_Ownership = st.selectbox("Car_Ownership", unique_Car_Ownership)
        Profession = st.selectbox("Profession", unique_Profession)
        CITY = st.selectbox("CITY", unique_CITY)
        STATE = st.selectbox("STATE", unique_STATE)
        
        

        clicked = st.form_submit_button("Predict Risk_Flag")
        if clicked:
            result=model.predict(pd.DataFrame({"Income": [Income],
                                               "Age": [Age],
                                               "Experience": [Experience],
                                               "CURRENT_JOB_YRS": [CURRENT_JOB_YRS],
                                               "CURRENT_HOUSE_YRS": [CURRENT_HOUSE_YRS],
                                               "Married_Single": [Married_Single],
                                               "House_Ownership": [House_Ownership],
                                               "Car_Ownership": [Car_Ownership],
                                               "Profession": [Profession],
                                               "CITY": [CITY],
                                               "STATE": [STATE]}))
            result = 'none_risk_flag' if result[0] == 1 else 'risk_flag'
            st.success('The predicted income is {}'.format(result))

if __name__=='__main__':
    main()