Create app.py

app.py

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+import streamlit as st
+
+
+
+st.set_page_config(page_title="Home", page_icon=None, layout="centered",
+                   initial_sidebar_state="auto", menu_items=None)
+
+
+## logo
+with st.sidebar:
+    st.markdown("""<div style='text-align: left; margin-top:-200px;margin-left:-40px;'>
+    <img src="https://affine.ai/wp-content/uploads/2023/05/Affine-Logo.svg" alt="logo" width="300" height="60">
+    </div>""", unsafe_allow_html=True)
+
+
+
+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="60">
+    <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
+                            -webkit-background-clip: text;
+                            -webkit-text-fill-color: transparent;
+                            text-shadow: none;'>
+                    IntelliForecast
+    </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.")
+
+
+st.header("Features")
+st.write("1. Efficient and Scalable Demand Forecasting")
+st.write("2. Reducing Model Maintenance Efforts and Resource Requirements for Granular-Level Forecasting")
+st.write("3. The unique aspect of this tool lies in its pre-trained models, eliminating the need to train individual models for each store and product. Instead, the models are trained on groups of stores and products, streamlining the process and saving valuable time and resources.")
+st.markdown("""<div style='text-align: center; margin-bottom:-50px'>
+    <img src="https://acis.affineanalytics.co.in/assets/images/logo.svg" alt="logo" width="600" height="100 ">
+    </div>""", unsafe_allow_html=True)
+
+
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)