Update app.py

app.py

@@ -152,7 +152,7 @@ st.write(f"Data loaded in {time_taken:.2f} seconds")
 ############################################ CARD #1 ####################################################
 if st.session_state['active_card'] == 'card1':
     # Step 1: Sales Volume vs FyWeek for the whole dataset (no filter)
-    st.subheader("Total Sales Volume vs Fiscal Week")
+    st.subheader("Total Sales Volume by Fiscal Week")
     df['FY_Week'] = df['FY'].astype(str) + '_' + df['Week'].astype(str)
     # Split FY_Week again for correct sorting
     if not df.empty and 'FY_Week' in df.columns:
@@ -163,13 +163,12 @@ if st.session_state['active_card'] == 'card1':
         
         # Create a line chart using Plotly
         fig = px.line(total_sales_df, x='FY_Week', y='SalesVolume',
-                      title='Total Sales Volume vs Fiscal Week',
                       labels={'SalesVolume': 'Sales Volume', 'FY_Week': 'Fiscal Week'})
         st.plotly_chart(fig)
 
     # Step 2: Top 3 states based on sales volume as buttons/cards
     top_states = df.groupby('State', observed=True)['SalesVolume'].sum().nlargest(3).index
-    st.write("### Top 3 Selling States :")
+    st.write("### Top 3 Selling States in the last 4 years (drill down by state)")
     col1, col2, col3 = st.columns(3)
     if len(top_states) > 0 and col1.button(top_states[0]):
         st.session_state['selected_state'] = top_states[0]
@@ -183,7 +182,7 @@ if st.session_state['active_card'] == 'card1':
         selected_state = st.session_state['selected_state']
 
         # Step 3: Sales volume vs FyWeek for the selected state
-        st.subheader(f"Sales Volume vs Fiscal Week for {selected_state}")
+        st.subheader(f"Sales Volume by Fiscal Week for {selected_state} (drill down by Chaincode) ")
         state_sales_df = df[df['State'] == selected_state].groupby('FY_Week', observed=True)['SalesVolume'].sum().reset_index()
 
         if not state_sales_df.empty and 'FY_Week' in state_sales_df.columns:
@@ -192,7 +191,6 @@ if st.session_state['active_card'] == 'card1':
             state_sales_df = state_sales_df.sort_values(by=['FY', 'Week'])
 
             fig = px.line(state_sales_df, x='FY_Week', y='SalesVolume',
-                          title=f'Sales Volume vs Fiscal Week in {selected_state}',
                           labels={'SalesVolume': 'Sales Volume', 'FY_Week': 'Fiscal Week'})
             st.plotly_chart(fig)
 
@@ -214,7 +212,7 @@ if st.session_state['active_card'] == 'card1':
             selected_chaincode = st.session_state['selected_chaincode']
 
             # Step 5: Sales volume vs FyWeek for the selected chaincode in the selected state
-            st.subheader(f"Sales Volume vs Fiscal Week for {selected_chaincode} in {selected_state}")
+            st.subheader(f"Sales Volume by Fiscal Week for {selected_chaincode} in {selected_state}")
             chain_sales_df = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode)].groupby('FY_Week', observed=True)['SalesVolume'].sum().reset_index()
 
             if not chain_sales_df.empty and 'FY_Week' in chain_sales_df.columns:
@@ -223,13 +221,13 @@ if st.session_state['active_card'] == 'card1':
                 chain_sales_df = chain_sales_df.sort_values(by=['FY', 'Week'])
 
                 fig = px.line(chain_sales_df, x='FY_Week', y='SalesVolume',
-                              title=f'Sales Volume vs Fiscal Week in {selected_chaincode}, {selected_state}',
+                              # title=f'Sales Volume vs Fiscal Week in {selected_chaincode}, {selected_state}',
                               labels={'SalesVolume': 'Sales Volume', 'FY_Week': 'Fiscal Week'})
                 st.plotly_chart(fig)
 
             # Step 6: Top 3 itemtypes based on sales volume as buttons/cards
             top_itemtypes = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode)].groupby('Itemtype', observed=True)['SalesVolume'].sum().nlargest(3).index
-            st.write(f"### Top Itemtype in {selected_chaincode}, {selected_state}:")
+            st.write(f"### Top Item Type in {selected_chaincode}, {selected_state} (drill down by ItemType) :")
 
             col1, col2, col3 = st.columns(3)
             if len(top_itemtypes) > 0 and col1.button(top_itemtypes[0]):
@@ -244,7 +242,7 @@ if st.session_state['active_card'] == 'card1':
                 selected_itemtype = st.session_state['selected_itemtype']
 
                 # Step 7: Dual-axis plot for Sales volume and UnitPrice vs FyWeek for the selected itemtype
-                st.subheader(f"Sales Volume & Unit Price vs Fiscal Week for {selected_itemtype} in {selected_chaincode}, {selected_state}")
+                # st.subheader(f"Sales Volume & Unit Price vs Fiscal Week for {selected_itemtype} in {selected_chaincode}, {selected_state}")
                 item_sales_df = df[(df['State'] == selected_state) & (df['Chaincode'] == selected_chaincode) & (df['Itemtype'] == selected_itemtype)].groupby('FY_Week', observed=True).agg({
                     'SalesVolume': 'sum',
                     'UnitPrice': 'mean'
@@ -280,7 +278,7 @@ if st.session_state['active_card'] == 'card1':
 
                     # Update layout for dual axes
                     fig.update_layout(
-                        title=f"Sales Volume & Unit Price vs Fiscal Week in {selected_itemtype}, {selected_chaincode}, {selected_state}",
+                        title=f"Sales Volume vs Unit Price by Fiscal Week for {selected_itemtype}, {selected_chaincode}, {selected_state}",
                         xaxis_title='Fiscal Week',
                         yaxis_title='Sales Volume',
                         yaxis2=dict(title='Unit Price', overlaying='y', side='right'),