Update app.py

app.py

@@ -46,16 +46,13 @@ with tabs[0]:
         start_date, end_date = st.date_input("Select ETA date range:", [min_date, max_date], min_value=min_date, max_value=max_date)
         df = df[(df['ETA'] >= pd.to_datetime(start_date)) & (df['ETA'] <= pd.to_datetime(end_date))]
 
-        # Shipment status selection using Altair with radio fallback
+        # Shipment status distribution
         st.subheader("Shipment Status Distribution")
         status_order = ["Pending", "WH-IN", "Cargo Ready", "Ship Out"]
         status_counts = df['Shipment Status'].value_counts().reindex(status_order).reset_index()
         status_counts.columns = ['Shipment Status', 'Count']
         status_counts = status_counts.dropna()
 
-        # Altair-based radio-like selector
-        selected_status = st.radio("Select a Shipment Status to filter the details below:", options=status_order, index=0)
-
         chart = alt.Chart(status_counts).mark_bar().encode(
             x=alt.X('Shipment Status', sort=status_order),
             y='Count',
@@ -82,9 +79,16 @@ with tabs[0]:
 
         st.altair_chart(line_chart, use_container_width=True)
 
-        # Table view sorted by ETA and filtered by selected status
+        # Status filter before detail view
         st.subheader("Shipment Detail View by ETA")
-        filtered_df = df[df['Shipment Status'] == selected_status]
+        status_options = ["All"] + status_order
+        selected_status = st.radio("Select a Shipment Status to filter the details below:", options=status_options, index=0)
+
+        if selected_status == "All":
+            filtered_df = df.copy()
+        else:
+            filtered_df = df[df['Shipment Status'] == selected_status]
+
         sorted_df = filtered_df[['MAWB', 'ETA', 'ATA', 'Shipment Status', 'Cartons', 'Last Mile Carrier']].sort_values(by='ETA')
         st.dataframe(sorted_df, use_container_width=True)
 
@@ -98,6 +102,6 @@ with tabs[1]:
         # Try to match the correct column
         last_mile_col = [col for col in df_ctn.columns if "last mile" in col.lower()][0]
 
-        # Group by Last Mile Service and count cartons
-        grouped = df_ctn.groupby(last_mile_col).agg({"Cartons": "sum"}).reset_index()
+        # Group by Last Mile Service and aggregate by row count
+        grouped = df_ctn.groupby(last_mile_col).size().reset_index(name='CTN Count')
         st.dataframe(grouped, use_container_width=True)