predict.py

import streamlit as st
import pandas as pd
import numpy as np
from joblib import load
import function


# Load All Files
# Muat model menggunakan joblib
loaded_model_RF = load('model_RF_under.joblib')
preproc = load('preprocessor.joblib')


def run():
  with st.form('key=loan_default_prediction'):
      Income = st.slider('Income', 10000,10000000,1000000)
      Age = st.number_input('Age', min_value=20, max_value=80, value=30, step=1)
      Experience = st.slider('Experience', 0, 20, 5)
      Married_Single = st.radio('Married/Single', ('single','married'), index=1)
      st.markdown('---')

      House_Ownership = st.radio('House_Ownership',('rented','norent_noown','owned'), index=1)
      Car_Ownership = st.selectbox('Car_Ownership', ('no','yes'), index=0)
      Profession = st.text_input('Profession','')
      CITY = st.selectbox('CITY', ('Rewa','Parbhani','Alappuzha','Bhubaneswar','Tiruchirappalli[10]','Jalgaon','Tiruppur','Jamnagar','Kota[6]','Karimnagar','Hajipur[31]','Adoni','Erode[17]','Kollam','Madurai','Anantapuram[24]','Kamarhati','Bhusawal','Sirsa','Amaravati','Secunderabad','Ahmedabad','Ajmer','Ongole','Miryalaguda','Ambattur','Indore','Pondicherry','Shimoga','Chennai','Gulbarga','Khammam','Saharanpur','Gopalpur','Amravati','Udupi','Howrah','Aurangabad[39]','Hospet','Shimla','Khandwa','Bidhannagar','Bellary','Danapur','Purnia[26]','Bijapur','Patiala','Malda','Sagar','Durgapur','Junagadh','Singrauli','Agartala','Thanjavur','Hindupur','Naihati','North_Dumdum','Panchkula','Anantapur','Serampore','Bathinda','Nadiad','Kanpur','Haridwar','Berhampur','Jamshedpur','Hyderabad','Bidar','Kottayam','Solapur','Suryapet','Aizawl','Asansol','Deoghar','Eluru[25]','Ulhasnagar','Aligarh','South_Dumdum','Berhampore','Gandhinagar','Sonipat','Muzaffarpur','Raichur','Rajpur_Sonarpur','Ambarnath','Katihar','Kozhikode','Vellore','Malegaon','Kochi','Nagaon','Nagpur','Srinagar','Davanagere','Bhagalpur','Siwan[32]','Meerut','Dindigul','Bhatpara','Ghaziabad','Kulti','Chapra','Dibrugarh','Panihati','Bhiwandi','Morbi','Kalyan-Dombivli','Gorakhpur','Panvel','Siliguri','Bongaigaon','Patna','Ramgarh','Ozhukarai','Mirzapur','Akola','Satna','Motihari[34]','Jalna','Jalandhar','Unnao','Karnal','Cuttack','Proddatur','Ichalkaranji','Warangal[11][12]','Jhansi','Bulandshahr','Narasaraopet','Chinsurah','Jehanabad[38]','Dhanbad','Gudivada','Gandhidham','Raiganj','Kishanganj[35]','Varanasi','Belgaum','Tirupati[21][22]','Tumkur','Coimbatore','Kurnool[18]','Gurgaon','Muzaffarnagar','Aurangabad','Bhavnagar','Arrah','Munger','Tirunelveli','Mumbai','Mango','Nashik','Kadapa[23]','Amritsar','Khora,_Ghaziabad','Ambala','Agra','Ratlam','Surendranagar_Dudhrej','Delhi_city','Bhopal','Hapur','Rohtak','Durg','Korba','Bangalore','Shivpuri','Thrissur','Vijayanagaram','Farrukhabad','Nangloi_Jat','Madanapalle','Thoothukudi','Nagercoil','Gaya','Chandigarh_city','Jammu[16]','Kakinada','Dewas','Bhalswa_Jahangir_Pur','Baranagar','Firozabad','Phusro','Allahabad','Guna','Thane','Etawah','Vasai-Virar','Pallavaram','Morena','Ballia','Surat','Burhanpur','Phagwara','Mau','Mangalore','Alwar','Mahbubnagar','Maheshtala','Hazaribagh','Bihar_Sharif','Faridabad','Lucknow','Tenali','Barasat','Amroha','Giridih','Begusarai','Medininagar','Rajahmundry[19][20]','Saharsa[29]','New_Delhi','Bhilai','Moradabad','Machilipatnam','Mira-Bhayandar','Pali','Navi_Mumbai','Mehsana','Imphal','Kolkata','Sambalpur','Ujjain','Madhyamgram','Jabalpur','Jamalpur[36]','Ludhiana','Bareilly','Gangtok','Anand','Dehradun','Pune','Satara','Srikakulam','Raipur','Jodhpur','Darbhanga','Nizamabad','Nandyal','Dehri[30]','Jorhat','Ranchi','Kumbakonam','Guntakal','Haldia','Loni','Pimpri-Chinchwad','Rajkot','Nanded','Noida','Kirari_Suleman_Nagar','Jaunpur','Bilaspur','Sambhal','Dhule','Rourkela','Thiruvananthapuram','Dharmavaram','Nellore[14][15]','Visakhapatnam[4]','Karawal_Nagar','Jaipur','Avadi','Bhimavaram','Bardhaman','Silchar','Buxar[37]','Kavali','Tezpur','Ramagundam[27]','Yamunanagar','Sri_Ganganagar','Sasaram[30]','Sikar','Bally','Bhiwani','Rampur','Uluberia','Sangli-Miraj_&_Kupwad','Hosur','Bikaner','Shahjahanpur','Sultan_Pur_Majra','Vijayawada','Bharatpur','Tadepalligudem','Tinsukia','Salem','Mathura','Guntur[13]','Hubli–Dharwad','Guwahati','Chittoor[28]','Tiruvottiyur','Vadodara','Ahmednagar','Fatehpur','Bhilwara','Kharagpur','Bettiah[33]','Bhind','Bokaro','Karaikudi','Raebareli','Pudukkottai','Udaipur','Mysore[7][8][9]','Panipat','Latur','Tadipatri','Bahraich','Orai','Raurkela_Industrial_Township','Gwalior','Katni','Chandrapur','Kolhapur'), index=1)
      STATE = st.selectbox('STATE',('Madhya_Pradesh','Maharashtra','Kerala','Odisha','Tamil_Nadu','Gujarat','Rajasthan','Telangana','Bihar','Andhra_Pradesh','West_Bengal','Haryana','Puducherry','Karnataka','Uttar_Pradesh','Himachal_Pradesh','Punjab','Tripura','Uttarakhand','Jharkhand','Mizoram','Assam','Jammu_and_Kashmir','Delhi','Chhattisgarh','Chandigarh','Uttar_Pradesh[5]','Manipur','Sikkim'),index=1)
      CURRENT_JOB_YRS = st.slider('CURRENT_JOB_YRS',0,15,5)
      CURRENT_HOUSE_YRS = st.slider('CURRENT_HOUSE_YRS', 10,15,12)
      submitted = st.form_submit_button('Predict')

  data_inf = {
      'Income': Income,
      'Age': Age,
      'Experience': Experience,
      'Married/Single': Married_Single,
      'House_Ownership': House_Ownership,
      'Profession': Profession,
      'Car_Ownership': Car_Ownership,
      'CITY': CITY,
      'STATE': STATE,
      'CURRENT_JOB_YRS': CURRENT_JOB_YRS,
      'CURRENT_HOUSE_YRS': CURRENT_HOUSE_YRS,
  }
  
  data_inf = pd.DataFrame([data_inf])
  data_inf['CITY'] = data_inf['CITY'].apply(lambda x: function.map_to_tier(x))
  data_inf['STATE'] = data_inf['STATE'].apply(lambda x: function.classify_region(x))
  final_df_inf = preproc.transform(data_inf)
  final_df_inf
  
  if submitted:
     y_pred_inf = loaded_model_RF.predict(final_df_inf)
     y_pred_inf = np.where(y_pred_inf >= 0.5 , 'Default' , 'Not Default')
     st.write('Customer will be : ', str(y_pred_inf))
     

if __name__ == '__main__':
    run()