app.py

import streamlit as st
import pandas as pd
import lightgbm as lgb
import pickle

# Load the trained LightGBM model
with open('lightgbm_model.pkl', 'rb') as model_file:
    model = pickle.load(model_file)

# Define mappings
workclass_map = {
    'Private': 1,
    'State-gov': 2,
    'Federal-gov': 3,
    'Self-emp-not-inc': 4,
    'Self-emp-inc': 5,
    'Local-gov': 6,
    'Without-pay': 7,
    'Never-worked': 8,
    '?': 9
}

education_ranks = {
    'Preschool': 1,
    '1st-4th': 2,
    '5th-6th': 3,
    '7th-8th': 4,
    '9th': 5,
    '10th': 6,
    '11th': 7,
    '12th': 8,
    'HS-grad': 9,
    'Some-college': 10,
    'Assoc-voc': 11,
    'Assoc-acdm': 12,
    'Bachelors': 13,
    'Masters': 14,
    'Prof-school': 15,
    'Doctorate': 16
}

marital_map = {
    'Married-civ-spouse': 1,
    'Married-spouse-absent': 1,
    'Married-AF-spouse': 1,
    'Widowed': 2,
    'Divorced': 2,
    'Separated': 2,
    'Never-married': 2
}

occupation_map = {
    'Exec-managerial': 1,
    'Machine-op-inspct': 2,
    'Prof-specialty': 3,
    'Other-service': 4,
    'Adm-clerical': 5,
    'Craft-repair': 6,
    'Transport-moving': 7,
    'Handlers-cleaners': 8,
    'Sales': 9,
    'Farming-fishing': 10,
    'Tech-support': 11,
    'Protective-serv': 12,
    'Armed-Forces': 13,
    'Priv-house-serv': 14
}

relationship_map = {
    'Not-in-family': 1,
    'Unmarried': 2,
    'Own-child': 3,
    'Other-relative': 4,
    'Husband': 5,
    'Wife': 6
}

income_map = {
    '<=50K': 0,
    '>50K': 1
}

# Define the input fields for the user to provide data
def get_user_input():
    age = st.number_input('Age', min_value=0, max_value=120, value=30)
    workclass = st.selectbox('Workclass', list(workclass_map.keys()))
    fnlwgt = st.number_input('Fnlwgt', min_value=0, value=100000)
    education = st.selectbox('Education', list(education_ranks.keys()))
    education_num = st.number_input('Education Num', min_value=0, max_value=20, value=10)
    marital_status = st.selectbox('Marital Status', list(marital_map.keys()))
    occupation = st.selectbox('Occupation', list(occupation_map.keys()))
    relationship = st.selectbox('Relationship', list(relationship_map.keys()))
    capital_gain = st.number_input('Capital Gain', min_value=0, value=0)
    capital_loss = st.number_input('Capital Loss', min_value=0, value=0)
    hours_per_week = st.number_input('Hours Per Week', min_value=0, max_value=168, value=40)

    user_data = {
        'age': age,
        'workclass_rank': workclass_map[workclass],
        'fnlwgt': fnlwgt,
        'education_rank': education_ranks[education],
        'education.num': education_num,
        'marital_status_binary': marital_map[marital_status],
        'occupation_rank': occupation_map[occupation],
        'relationship_rank': relationship_map[relationship],
        'capital.gain': capital_gain,
        'capital.loss': capital_loss,
        'hours.per.week': hours_per_week
    }
    
    features = pd.DataFrame(user_data, index=[0])
    return features

# Main function to run the app
def main():
    st.title('Income Prediction App')
    st.write('This app predicts whether a person makes over $50K a year based on their demographics and work information.')

    user_input = get_user_input()
    
    st.subheader('User Input:')
    st.write(user_input)
    
    prediction_proba = model.predict(user_input)
  
    st.subheader('Prediction:')
    if prediction_proba[0] < 0.5:
        st.write('The model predicts: Income <= $50K')
    else:
        st.write('The model predicts: Income > $50K')

    st.subheader('Prediction Probability:')
    st.write(f'Probability of making over $50K: {prediction_proba[0]:.2f}')

if __name__ == '__main__':
    main()