Update app.py

app.py

@@ -1,4 +1,4 @@
-#Import Relevant Libraries
+# Import Relevant Libraries
 import streamlit as st
 import pickle
 import pandas as pd
@@ -59,6 +59,23 @@ st.markdown(
     unsafe_allow_html=True
 )
 
+# Input Descriptions in Sidebar
+st.sidebar.title("Input Descriptions")
+st.sidebar.markdown("**Age:** Age of the employee.")
+st.sidebar.markdown("**Monthly Income:** Monthly income of the employee.")
+st.sidebar.markdown("**Number of Companies Worked:** Number of companies the employee has worked for.")
+st.sidebar.markdown("**Percent Salary Hike:** Percentage increase in salary for the employee.")
+st.sidebar.markdown("**Training Times Last Year:** Number of training sessions attended by the employee last year.")
+st.sidebar.markdown("**Department:** Department the employee belongs to (Sales, Research & Development, Human Resources).")
+st.sidebar.markdown("**Environment Satisfaction:** Level of satisfaction with the work environment (1: Low, 2: Medium, 3: High, 4: Very High).")
+st.sidebar.markdown("**Job Role:** Job role of the employee.")
+st.sidebar.markdown("**Job Satisfaction:** Level of job satisfaction (1: Low, 2: Medium, 3: High, 4: Very High).")
+st.sidebar.markdown("**Work Life Balance:** Level of satisfaction with work-life balance (1: Low, 2: Medium, 3: High, 4: Very High).")
+st.sidebar.markdown("**Over Time:** Whether the employee works overtime.")
+st.sidebar.markdown("**Relationship Satisfaction:** Level of satisfaction with work relationships (1: Low, 2: Medium, 3: High, 4: Very High).")
+st.sidebar.markdown("**Years Since Last Promotion:** Number of years since the employee's last promotion.")
+st.sidebar.markdown("**Years With Current Manager:** Number of years the employee has been with the current manager.")
+
 # Define the Streamlit app
 def main():
     # Define layout with three columns
@@ -121,7 +138,7 @@ def main():
         probability = model.predict_proba(input_data)[:, 1]
 
         # Display predicted attrition status
-        st.subheader("Predicted Attrition Status 🔮")
+        st.subheader("Predicted Attrition Status 🎯")
         if prediction[0] == 1:
             st.error("Employee is predicted to leave (Attrition = Yes)")
         else: