Update app.py

app.py

@@ -52,7 +52,7 @@ def load_data(active_card):
     card_specific_cols = {
         'card1': ['FyWeek', 'State', 'Itemtype', 'Chaincode', 'SalesVolume'],
         'card2': ['FyWeek', 'Fy', 'State','Store','Address','Zipcode','City','Itemtype', 'Chaincode', 'Containercode', 'SalesVolume', 'UnitPrice', 'Sales'],
-        'card3': ['FyWeek', 'Fy', 'State', 'Store', 'Itemtype', 'Chaincode', 'SalesVolume', 'UnitPrice', 'Sales']  # Added for PE calculation card
+        'card3': ['FyWeek', 'Fy', 'State','Store','Address','Zipcode','City','Itemtype', 'Chaincode', 'Containercode', 'SalesVolume', 'UnitPrice', 'Sales'] # Added for PE calculation card
     }
 
     # Choose columns based on the active card
@@ -80,8 +80,9 @@ def load_data(active_card):
     ddf = dd.read_csv("fy21-24.csv", usecols=required_columns, dtype=dtype_spec)
     df = ddf.compute()
 
+    # st.write("+++++++++++++++++++++++")
 
-    if active_card in ['card2','card3']:
+    if active_card in ['card2', 'card3', 'card4']:
         df = df.groupby(['FyWeek', 'Fy', 'Chaincode', 'Store', 'Address', 'Zipcode', 'City', 'State', 'Containercode', 'Itemtype'], observed=True).agg({
         'SalesVolume': 'sum',
         'UnitPrice': 'mean',
@@ -91,20 +92,23 @@ def load_data(active_card):
         df['Week'] = df['Week'].astype(int)  # Convert 'Week' to int
         df['Year'] = df['FY'].str[2:].astype(int)  # Extract year part and convert to int
         df['Dt'] = date_from_week(df['Year'], df['Week'])
-
     # Add the region column based on state
     df['Region'] = df['State'].map(state_to_region)
 
-    # st.write(df.columns)
     return df
 
+# st.image("bonnie.png", width=200) 
+# # Main interactive section
+# st.title('Bonnnie Plants Price vs Sales Volume Trcaker')
+
 # Display logo
 st.image("bonnie.png", width=150)  # Adjust width as needed
 
 # Display title
-# st.title("Bonnie Plants Pricing & Sales Analytics Dashboard")
-st.title("Price vs. Sales Volume Tracker Dashboard")
+st.title("Bonnie Plants Pricing & Sales Analytics Dashboard")
 
+# Close the div for logo and title
+# st.markdown('</div>', unsafe_allow_html=True)
 
 #  Initialize session state for storing which card was clicked and item type
 if 'active_card' not in st.session_state:
@@ -116,7 +120,7 @@ if 'selected_feature' not in st.session_state:
     st.session_state['selected_feature'] = 'Chaincode'  # Default to 'Chain Code'
 
 # Card selection buttons
-col1, col2 ,col3= st.columns(3)
+col1, col2, col3= st.columns(3)
 # Define buttons for plot categories, update session state when clicked
 with col1:
     if st.button("Sales Volume Trend for Item Category"):
@@ -128,7 +132,8 @@ with col2:
 with col3:
     if st.button("PE Coefficient Calculation for Regions & Item Categories"):
         st.session_state['active_card'] = 'card3'
-        
+
+
 start_time=time.time()
 # st.write(st.session_state['active_card'])
 df = load_data(st.session_state['active_card'])
@@ -155,6 +160,7 @@ if st.session_state['active_card'] == 'card1':
     start_time = time.time()
     group_data = filtered_df.groupby(['FyWeek', selected_feature],observed=True)['SalesVolume'].sum().reset_index()
     time_taken = time.time() - start_time
+    # st.write(f"Data grouped in {time_taken:.2f} seconds")
 
     # Plotting
     fig = px.bar(group_data, x='FyWeek', y='SalesVolume', color=selected_feature,
@@ -164,12 +170,7 @@ if st.session_state['active_card'] == 'card1':
 
 ##########################################################################################################
 
-
-########################################### CARD #2 ####################################################
-
-
-
-
+##########################################################################################################
 
 if st.session_state['active_card'] == 'card2':
     # Dropdown to select item type (using session_state)
@@ -178,7 +179,7 @@ if st.session_state['active_card'] == 'card2':
         index=list(df['Itemtype'].unique()).index(st.session_state['selected_item_type']))
 
     # Dropdown to select the grouping category (container code, chain code, or state)
-    group_by_option = st.selectbox('Group by', ['Containercode', 'Chaincode', 'State'])
+    group_by_option = st.selectbox('Group by', ['Containercode', 'Chaincode', 'State','Region'])
 
     # Multi-select checkbox to select multiple years
     selected_years = st.multiselect('Select Year(s)', [2021, 2022, 2023, 2024], default=[2021])
@@ -198,7 +199,7 @@ if st.session_state['active_card'] == 'card2':
 
     # Filter the data for only the top 3 values
     top_group_data = filtered_df[filtered_df[group_by_option].isin(top_3_values)]
-
+    
     # Aggregate data
     agg_df = top_group_data.groupby([group_by_option, 'Year', 'Week', 'Dt'], observed=True).agg({
         'SalesVolume': 'sum',
@@ -261,21 +262,17 @@ if st.session_state['active_card'] == 'card2':
             yaxis2=dict(title='UnitPrice', overlaying='y', side='right'),
             legend=dict(x=0.9, y=1.15),
             hovermode="x unified",  # Show both values in a tooltip
-            height=400,
-            autosize=False,
-            margin=dict(l=1, r=1, t=1, b=1)
+            height=600,
+            margin=dict(l=50, r=50, t=50, b=50)
         )
 
         # Rotate X-axis labels
         fig.update_xaxes(tickangle=90)
 
         # Display the Plotly figure in Streamlit
-        st.plotly_chart(fig, use_container_width=False)
-
-
-
-##########################################################################################################
+        st.plotly_chart(fig, use_container_width=True)
 
+################################
 if st.session_state['active_card'] == 'card3':
     # Dropdown for selecting the item type
     item_type_options = df['Itemtype'].unique()
@@ -329,4 +326,3 @@ if st.session_state['active_card'] == 'card3':
     )
 
     st.plotly_chart(fig, use_container_width=True)
-    ###################################################################################################################