app.py

import streamlit as st
import pickle
import pandas as pd
from catboost import CatBoostClassifier

# Load the trained model and unique values from the pickle file
with open('model_and_key_components.pkl', 'rb') as file:
    saved_components = pickle.load(file)

model = saved_components['model']
unique_values = saved_components['unique_values']

# Define the Streamlit app
def main():
    st.title("Employee Attrition Prediction App")
    st.sidebar.title("Model Settings")

    # Sidebar inputs
    with st.sidebar.expander("View Unique Values"):
        st.write("Unique values for each feature:")
        for column, values in unique_values.items():
            st.write(f"- {column}: {values}")

    # Main content
    st.write("Welcome to the Employee Attrition Prediction App!")
    st.write("This app helps HR practitioners predict employee attrition using a trained CatBoost model.")
    st.write("Please provide the following information to make a prediction:")

    # Define layout with three columns
    col1, col2, col3 = st.columns(3)

    # Column 1
    with col1:
        age = st.number_input("Age", min_value=18, max_value=70)
        monthly_income = st.number_input("Monthly Income")
        num_companies_worked = st.number_input("Number of Companies Worked")
        percent_salary_hike = st.number_input("Percent Salary Hike", min_value=0, max_value=25)
        training_times_last_year = st.number_input("Training Times Last Year", min_value=0, max_value=6)

    # Column 2
    with col2:
        department = st.selectbox("Department", ['Sales', 'Research & Development', 'Human Resources'])
        environment_satisfaction = st.selectbox("Environment Satisfaction", [1, 2, 3, 4])
        job_role = st.selectbox("Job Role", ['Sales Executive', 'Research Scientist', 'Laboratory Technician',
                                              'Manufacturing Director', 'Healthcare Representative', 'Manager',
                                              'Sales Representative', 'Research Director', 'Human Resources'])
        job_satisfaction = st.selectbox("Job Satisfaction", [1, 2, 3, 4])
        work_life_balance = st.selectbox("Work Life Balance", [1, 2, 3, 4])

    # Column 3
    with col3:
        over_time = st.checkbox("Over Time")
        relationship_satisfaction = st.selectbox("Relationship Satisfaction", [1, 2, 3, 4])
        years_since_last_promotion = st.number_input("Years Since Last Promotion")
        years_with_curr_manager = st.number_input("Years With Current Manager")

    # Convert numerical features to strings
    age = str(age)
    monthly_income = str(monthly_income)
    num_companies_worked = str(num_companies_worked)
    percent_salary_hike = str(percent_salary_hike)
    training_times_last_year = str(training_times_last_year)
    years_since_last_promotion = str(years_since_last_promotion)
    years_with_curr_manager = str(years_with_curr_manager)
    
# Create a DataFrame to hold the user input data
input_data = pd.DataFrame({
    'Age': [age],
    'Department': [department],
    'EnvironmentSatisfaction': [environment_satisfaction],
    'JobRole': [job_role],
    'JobSatisfaction': [job_satisfaction],
    'MonthlyIncome': [monthly_income],
    'NumCompaniesWorked': [num_companies_worked],
    'OverTime': [over_time],
    'PercentSalaryHike': [percent_salary_hike],
    'RelationshipSatisfaction': [relationship_satisfaction],
    'TrainingTimesLastYear': [training_times_last_year],
    'WorkLifeBalance': [work_life_balance],
    'YearsSinceLastPromotion': [years_since_last_promotion],
    'YearsWithCurrManager': [years_with_curr_manager]
})

# Reorder columns to match the expected order
input_data = input_data[['Age', 'Department', 'EnvironmentSatisfaction', 'JobRole', 'JobSatisfaction',
                         'MonthlyIncome', 'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',
                         'RelationshipSatisfaction', 'TrainingTimesLastYear', 'WorkLifeBalance',
                         'YearsSinceLastPromotion', 'YearsWithCurrManager']]

    # Make predictions
    prediction = model.predict(input_data)
    probability = model.predict_proba(input_data)[:, 1]

    # Display prediction
    if prediction[0] == 0:
        st.success("Employee is predicted to stay (Attrition = No)")
    else:
        st.error("Employee is predicted to leave (Attrition = Yes)")

        # Offer recommendations for retaining the employee
        st.subheader("Suggestions for retaining the employee:")
        st.markdown("- Invest in orientation programs and career development for entry-level staff, which could contribute to higher retention.")
        st.markdown("- Implement mentorship programs and career development initiatives aimed at engaging and retaining younger employees.")
        st.markdown("- Offer robust training and development programs and regular promotions to foster career growth. This investment in skills and career advancement can contribute to higher job satisfaction and retention.")
        st.markdown("- Recognize the diverse needs of employees based on marital status and consider tailoring benefits or support programs accordingly.")
        st.markdown("- Consider offering benefits that cater to the unique needs of married, single, and divorced employees.")
        st.markdown("- Introduce or enhance policies that support work-life balance for employees with families.")
        st.markdown("- Recognize the unique challenges and opportunities within each department and tailor retention strategies accordingly.")

    # Display probability
    st.write(f"Probability of Attrition: {probability[0]*100:.2f}%")

if __name__ == "__main__":
    main()