Update app.py

app.py

@@ -101,7 +101,7 @@ def load_data(active_card):
 st.image("bonnie.png", width=150)  # Adjust width as needed
 
 # Display title
-st.title("Price vs. Sales Volume Tracker Dashboard")
+# st.title("Price vs. Sales Volume Tracker Dashboard")
 
 
 #  Initialize session state for storing which card was clicked and item type
@@ -116,7 +116,7 @@ if 'selected_feature' not in st.session_state:
 # Card selection buttons with logic to reset session state on switch
 col1, col2, col3 = st.columns(3)
 with col1:
-    if st.button("Sales Volume Trend for Item Category"):
+    if st.button("Sales Volume Trend"):
         st.session_state['active_card'] = 'card1'
         # Reset other selections when switching cards
         st.session_state['selected_state'] = None
@@ -125,7 +125,7 @@ with col1:
         st.session_state['selected_containercode'] = None
 
 with col2:
-    if st.button("Sales Volume & Unit Price Correlation for Item Category and Container Code"):
+    if st.button("Sales Volume vs Median Unit Price Trend"):
         st.session_state['active_card'] = 'card2'
         # Reset selections when switching cards
         st.session_state['selected_state'] = None
@@ -134,7 +134,7 @@ with col2:
         st.session_state['selected_containercode'] = None
 
 with col3:
-    if st.button("PE Coefficient Calculation for Regions & Item Categories"):
+    if st.button("Price Elasticity Coefficient Trend YoY"):
         st.session_state['active_card'] = 'card3'
         # Reset selections when switching cards
         st.session_state['selected_state'] = None
@@ -306,18 +306,25 @@ if st.session_state['active_card'] == 'card1':
                     top_stores = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode) & (df['Itemtype'] == selected_itemtype)].groupby('Store', observed=True)['SalesVolume'].sum().nlargest(3).reset_index()
                     bottom_stores = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode) & (df['Itemtype'] == selected_itemtype)].groupby('Store', observed=True)['SalesVolume'].sum().nsmallest(3).reset_index()
 
-                    # Create a table with the top and bottom container codes and stores
-                    st.write("### Top 3 Container Codes:")
-                    st.dataframe(top_containercodes)
-
-                    st.write("### Bottom 3 Container Codes:")
-                    st.dataframe(bottom_containercodes)
-
-                    st.write("### Top 3 Stores:")
-                    st.dataframe(top_stores)
-
-                    st.write("### Bottom 3 Stores:")
-                    st.dataframe(bottom_stores)
+                    # Display top and bottom container codes side by side
+                    st.write("### Container Codes:")
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        st.write("#### Top 3 Container Codes")
+                        st.dataframe(top_containercodes)
+                    with col2:
+                        st.write("#### Bottom 3 Container Codes")
+                        st.dataframe(bottom_containercodes)
+
+                    # Display top and bottom stores side by side
+                    st.write("### Stores:")
+                    col3, col4 = st.columns(2)
+                    with col3:
+                        st.write("#### Top 3 Stores")
+                        st.dataframe(top_stores)
+                    with col4:
+                        st.write("#### Bottom 3 Stores")
+                        st.dataframe(bottom_stores)
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