Update app.py

app.py

@@ -85,105 +85,6 @@ def main():
     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]:.2f}")
-
-if __name__ == "__main__":
-    main()
-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", min_value=1000, max_value=20000)
-        num_companies_worked = st.number_input("Number of Companies Worked", min_value=0, max_value=10)
-        percent_salary_hike = st.number_input("Percent Salary Hike", min_value=10, 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", min_value=0, max_value=15)
-        years_with_curr_manager = st.number_input("Years With Current Manager", min_value=0, max_value=15)
-
-    # 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]
-    })
-
-    # 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)")