Update app.py

app.py

@@ -1,3 +1,4 @@
+#Import Relevant Libraries
 import streamlit as st
 import pickle
 import pandas as pd
@@ -10,6 +11,51 @@ with open('model_and_key_components.pkl', 'rb') as file:
 model = saved_components['model']
 unique_values = saved_components['unique_values']
 
+# Page Title with Style
+st.markdown(
+    f"""
+    <div style="text-align: center;">
+        <h1 style="color: #800000;">👥 Employee Attrition Prediction App</h1>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# Welcome Message with Style (Centered)
+st.markdown(
+    f"""
+    <div style="text-align: center;">
+        <p>👋 Welcome to the Employee Attrition Prediction App!</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# Sepsis Information
+st.markdown(
+    """
+    **Employee attrition** refers to the phenomenon of employees leaving their jobs for various reasons. It's crucial for organizations to predict attrition to retain valuable talent.
+    """
+)
+
+# Link to Detailed Article on Employee Attrition
+st.markdown("🔗 **Learn more about employee attrition from [AIHR](https://www.aihr.com/wp-content/uploads/Reasons-for-Employee-Attrition.png)**")
+
+st.markdown("---")
+
+# Main content
+st.image("https://www.aihr.com/wp-content/uploads/Reasons-for-Employee-Attrition.png")
+
+# Additional Information for Sample Prediction
+st.write("📊 To make a sample prediction, you can refer to the training dataset information available in the sidebar or input the information of the employee whose attrition you want to predict.")
+
+# About Section with Style
+st.sidebar.title("ℹ️ About")
+st.sidebar.info(
+    "This app predicts employee attrition using machine learning on HR data, aiding HR professionals in retention strategies. "
+    "It utilizes a machine learning model trained on an employee attrition dataset."
+)
+
 # Define the Streamlit app
 def main():
     st.title("Employee Attrition Prediction App 🕵️‍♂️")
@@ -55,7 +101,7 @@ def main():
         years_with_curr_manager = st.number_input("Years With Current Manager")
 
     # Predict button
-    if st.button("Predict 📊"):
+    if st.button("Predict Employee Attrition 📊"):
 
         # Create a DataFrame to hold the user input data
         input_data = pd.DataFrame({