Update app.py

app.py

@@ -5,85 +5,102 @@ import altair as alt
 st.set_page_config(page_title="Shipment Monitoring Dashboard", layout="wide")
 st.title("Shipment Monitoring Dashboard")
 
-uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx"])
-
-if uploaded_file:
-    df = pd.read_excel(uploaded_file, sheet_name=0)
-
-    # Rename columns
-    df.columns = ['MAWB', 'MAWB Serial Number', 'Injection Gateway', 'Clearance Port', 'Latest Status',
-                  'Cartons', 'Packages', 'Weights', 'Not online packages count', 'ETD', 'ETA', 'ATD', 'ATA',
-                  'Last Mile Carrier', 'Readynotice has been notified', 'Vehicle has been arranged',
-                  'Pick-upcompletedfrom', 'WH-IN Scan', 'Cargo Ready', 'Ship Out', 'Note',
-                  'Exception Note', 'Exception Note Description']
-
-    # Add status column
-    def determine_status(row):
-        if pd.isna(row['WH-IN Scan']):
-            return "Pending"
-        elif pd.isna(row['Cargo Ready']):
-            return "WH-IN"
-        elif pd.isna(row['Ship Out']):
-            return "Cargo Ready"
-        else:
-            return "Ship Out"
-
-    df['Shipment Status'] = df.apply(determine_status, axis=1)
-    df['ETA'] = pd.to_datetime(df['ETA'])
-
-    # Filter for Injection Gateway
-    injection_gateways = df['Injection Gateway'].dropna().unique().tolist()
-    selected_gateway = st.selectbox("Filter by Injection Gateway", options=["All"] + injection_gateways)
-    if selected_gateway != "All":
-        df = df[df['Injection Gateway'] == selected_gateway]
-
-    # Filter by ETA date range
-    st.subheader("Filter by ETA Date Range")
-    min_date, max_date = df['ETA'].min(), df['ETA'].max()
-    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 distribution with interactive selection
-    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 selection for interactivity
-    selection = alt.selection_single(fields=['Shipment Status'], bind='legend', name='Select', init={'Shipment Status': 'Pending'})
-
-    chart = alt.Chart(status_counts).mark_bar().encode(
-        x=alt.X('Shipment Status', sort=status_order),
-        y='Count',
-        color='Shipment Status',
-        tooltip=['Shipment Status', 'Count'],
-        opacity=alt.condition(selection, alt.value(1), alt.value(0.4))
-    ).add_params(selection).properties(height=300)
-
-    st.altair_chart(chart, use_container_width=True)
-
-    selected_status = selection.init['Shipment Status'] if selection else "Pending"
-
-    # Volume by ETA
-    st.subheader("Shipment Volume by ETA")
-    volume_by_eta = df.groupby('ETA').agg({
-        'MAWB': 'count',
-        'Cartons': 'sum',
-    }).reset_index().rename(columns={'MAWB': 'Shipment Count'})
-
-    st.dataframe(volume_by_eta, use_container_width=True)
-
-    line_chart = alt.Chart(volume_by_eta).mark_line(point=True).encode(
-        x='ETA:T',
-        y='Cartons:Q',
-        tooltip=['ETA:T', 'Shipment Count:Q', 'Cartons:Q']
-    ).properties(height=400)
-
-    st.altair_chart(line_chart, use_container_width=True)
-
-    # Table view sorted by ETA and filtered by selected status
-    st.subheader("Shipment Detail View by ETA")
-    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)
+tabs = st.tabs(["Main Dashboard", "Last Mile Summary"])
+
+with tabs[0]:
+    uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx"], key="main_upload")
+
+    if uploaded_file:
+        df = pd.read_excel(uploaded_file, sheet_name=0)
+
+        # Rename columns
+        df.columns = ['MAWB', 'MAWB Serial Number', 'Injection Gateway', 'Clearance Port', 'Latest Status',
+                      'Cartons', 'Packages', 'Weights', 'Not online packages count', 'ETD', 'ETA', 'ATD', 'ATA',
+                      'Last Mile Carrier', 'Readynotice has been notified', 'Vehicle has been arranged',
+                      'Pick-upcompletedfrom', 'WH-IN Scan', 'Cargo Ready', 'Ship Out', 'Note',
+                      'Exception Note', 'Exception Note Description']
+
+        # Add status column
+        def determine_status(row):
+            if pd.isna(row['WH-IN Scan']):
+                return "Pending"
+            elif pd.isna(row['Cargo Ready']):
+                return "WH-IN"
+            elif pd.isna(row['Ship Out']):
+                return "Cargo Ready"
+            else:
+                return "Ship Out"
+
+        df['Shipment Status'] = df.apply(determine_status, axis=1)
+        df['ETA'] = pd.to_datetime(df['ETA'])
+
+        # Filter for Injection Gateway
+        injection_gateways = df['Injection Gateway'].dropna().unique().tolist()
+        selected_gateway = st.selectbox("Filter by Injection Gateway", options=["All"] + injection_gateways)
+        if selected_gateway != "All":
+            df = df[df['Injection Gateway'] == selected_gateway]
+
+        # Filter by ETA date range
+        st.subheader("Filter by ETA Date Range")
+        min_date, max_date = df['ETA'].min(), df['ETA'].max()
+        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 distribution with interactive selection
+        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 selection for interactivity
+        selection = alt.selection_single(fields=['Shipment Status'], bind='legend', name='Select', init={'Shipment Status': 'Pending'})
+
+        chart = alt.Chart(status_counts).mark_bar().encode(
+            x=alt.X('Shipment Status', sort=status_order),
+            y='Count',
+            color='Shipment Status',
+            tooltip=['Shipment Status', 'Count'],
+            opacity=alt.condition(selection, alt.value(1), alt.value(0.4))
+        ).add_params(selection).properties(height=300)
+
+        st.altair_chart(chart, use_container_width=True)
+
+        selected_status = selection.init['Shipment Status'] if selection else "Pending"
+
+        # Volume by ETA
+        st.subheader("Shipment Volume by ETA")
+        volume_by_eta = df.groupby('ETA').agg({
+            'MAWB': 'count',
+            'Cartons': 'sum',
+        }).reset_index().rename(columns={'MAWB': 'Shipment Count'})
+
+        st.dataframe(volume_by_eta, use_container_width=True)
+
+        line_chart = alt.Chart(volume_by_eta).mark_line(point=True).encode(
+            x='ETA:T',
+            y='Cartons:Q',
+            tooltip=['ETA:T', 'Shipment Count:Q', 'Cartons:Q']
+        ).properties(height=400)
+
+        st.altair_chart(line_chart, use_container_width=True)
+
+        # Table view sorted by ETA and filtered by selected status
+        st.subheader("Shipment Detail View by ETA")
+        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)
+
+with tabs[1]:
+    st.header("Upload CTN File for Last Mile Summary")
+    ctn_file = st.file_uploader("Upload Excel with Last Mile Service", type=["xlsx"], key="ctn_upload")
+
+    if ctn_file:
+        df_ctn = pd.read_excel(ctn_file, sheet_name=0)
+
+        # 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()
+        st.dataframe(grouped, use_container_width=True)