Update app.py

app.py

@@ -1,5 +1,9 @@
 import streamlit as st
+
+
 st.set_page_config("Home")
+
+
 ## logo
 with st.sidebar:
     st.markdown("""<div style='text-align: left; margin-top:-200px;margin-left:-40px;'>
@@ -8,7 +12,23 @@ with st.sidebar:
 
 
 
-st.title("IntelliForecast")
+st.markdown("""
+    <div style='text-align: center; margin-top:-70px; margin-bottom: 5px;margin-left: -50px;'>
+    <h2 style='font-size: 20px; font-family: Courier New, monospace;
+                    letter-spacing: 2px; text-decoration: none;'>
+    <img src="https://acis.affineanalytics.co.in/assets/images/logo_small.png" alt="logo" width="70" height="30">
+    <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
+                            -webkit-background-clip: text;
+                            -webkit-text-fill-color: transparent;
+                            text-shadow: none;'>
+                    Energy Demand Forecasting Dashboard
+    </span>
+    <span style='font-size: 40%;'>
+    <sup style='position: relative; top: 5px; color: #ed4965;'>by Affine</sup>
+    </span>
+    </h2>
+    </div>
+    """, unsafe_allow_html=True)
 st.header("Description")
 st.write("Affine Time Series Toolbox is a powerful and versatile tool designed to handle multiple time series data. It excels in forecasting demand with a high level of granularity, allowing for precise predictions at the store and product level. By leveraging this tool, businesses can minimize the need for constant model maintenance and reduce resource demands, all while ensuring accurate and reliable demand forecasts. Its wide-ranging capabilities make it suitable for application in various domains, from e-commerce to the energy sector.")