Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta
from st_aggrid import AgGrid, GridOptionsBuilder, JsCode
import random

# Set page config
st.set_page_config(
    page_title="E-commerce Shipping Dashboard",
    page_icon="📦",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Title and header
st.title("📦 E-commerce Shipping Dashboard")
st.markdown("Monitor and track shipping orders in real-time")

# Generate sample data
@st.cache_data
def generate_shipping_data():
    np.random.seed(42)
    
    # Generate data for the last 30 days
    end_date = datetime.now()
    start_date = end_date - timedelta(days=30)
    
    # Order statuses and their weights
    statuses = ['Pending', 'Processing', 'Shipped', 'In Transit', 'Out for Delivery', 'Delivered', 'Delayed', 'Cancelled']
    status_weights = [0.05, 0.10, 0.15, 0.25, 0.10, 0.30, 0.03, 0.02]
    
    # Shipping carriers
    carriers = ['FedEx', 'UPS', 'USPS', 'DHL', 'Amazon Logistics']
    
    # Regions
    regions = ['North America', 'Europe', 'Asia', 'South America', 'Australia']
    
    # Generate 1000 sample orders
    n_orders = 1000
    
    data = []
    for i in range(n_orders):
        order_date = start_date + timedelta(
            days=random.randint(0, 30),
            hours=random.randint(0, 23),
            minutes=random.randint(0, 59)
        )
        
        status = np.random.choice(statuses, p=status_weights)
        carrier = np.random.choice(carriers)
        region = np.random.choice(regions)
        
        # Calculate estimated delivery based on status
        if status in ['Delivered']:
            delivery_date = order_date + timedelta(days=random.randint(1, 7))
        elif status in ['Cancelled']:
            delivery_date = None
        else:
            delivery_date = order_date + timedelta(days=random.randint(1, 10))
        
        # Calculate shipping cost
        base_cost = random.uniform(5, 50)
        if region in ['Asia', 'Australia']:
            base_cost *= 1.5
        
        data.append({
            'Order ID': f'ORD-{10000 + i}',
            'Order Date': order_date,
            'Status': status,
            'Carrier': carrier,
            'Region': region,
            'Shipping Cost': round(base_cost, 2),
            'Estimated Delivery': delivery_date,
            'Customer': f'Customer {i + 1}',
            'Tracking Number': f'TRK{random.randint(100000000, 999999999)}'
        })
    
    return pd.DataFrame(data)

# Load data
df = generate_shipping_data()

# Sidebar filters
st.sidebar.header("Filters")

# Date range filter
date_range = st.sidebar.date_input(
    "Select Date Range",
    value=(df['Order Date'].min().date(), df['Order Date'].max().date()),
    min_value=df['Order Date'].min().date(),
    max_value=df['Order Date'].max().date()
)

# Status filter
status_filter = st.sidebar.multiselect(
    "Order Status",
    options=df['Status'].unique(),
    default=df['Status'].unique()
)

# Carrier filter
carrier_filter = st.sidebar.multiselect(
    "Shipping Carrier",
    options=df['Carrier'].unique(),
    default=df['Carrier'].unique()
)

# Region filter
region_filter = st.sidebar.multiselect(
    "Region",
    options=df['Region'].unique(),
    default=df['Region'].unique()
)

# Apply filters
filtered_df = df[
    (df['Order Date'].dt.date >= date_range[0]) &
    (df['Order Date'].dt.date <= date_range[1]) &
    (df['Status'].isin(status_filter)) &
    (df['Carrier'].isin(carrier_filter)) &
    (df['Region'].isin(region_filter))
].copy()

# Main dashboard metrics
col1, col2, col3, col4 = st.columns(4)

with col1:
    total_orders = len(filtered_df)
    st.metric("Total Orders", f"{total_orders:,}")

with col2:
    delivered_orders = len(filtered_df[filtered_df['Status'] == 'Delivered'])
    delivery_rate = (delivered_orders / total_orders * 100) if total_orders > 0 else 0
    st.metric("Delivery Rate", f"{delivery_rate:.1f}%")

with col3:
    delayed_orders = len(filtered_df[filtered_df['Status'] == 'Delayed'])
    st.metric("Delayed Orders", f"{delayed_orders:,}")

with col4:
    avg_shipping_cost = filtered_df['Shipping Cost'].mean()
    st.metric("Avg Shipping Cost", f"${avg_shipping_cost:.2f}")

# Charts section
st.markdown("---")

# Row 1: Status distribution and carrier performance
col1, col2 = st.columns(2)

with col1:
    st.subheader("Order Status Distribution")
    status_counts = filtered_df['Status'].value_counts()
    fig_status = px.pie(
        values=status_counts.values,
        names=status_counts.index,
        title="Orders by Status"
    )
    fig_status.update_traces(textposition='inside', textinfo='percent+label')
    st.plotly_chart(fig_status, use_container_width=True)

with col2:
    st.subheader("Carrier Performance")
    carrier_performance = filtered_df.groupby('Carrier').agg({
        'Order ID': 'count',
        'Shipping Cost': 'mean'
    }).round(2)
    carrier_performance.columns = ['Total Orders', 'Avg Cost']
    
    fig_carrier = px.bar(
        carrier_performance.reset_index(),
        x='Carrier',
        y='Total Orders',
        title="Orders by Carrier",
        color='Avg Cost',
        color_continuous_scale='Blues'
    )
    st.plotly_chart(fig_carrier, use_container_width=True)

# Row 2: Timeline and regional analysis
col1, col2 = st.columns(2)

with col1:
    st.subheader("Order Timeline")
    # Group by date and status
    timeline_data = filtered_df.groupby([filtered_df['Order Date'].dt.date, 'Status']).size().reset_index()
    timeline_data.columns = ['Date', 'Status', 'Count']
    
    fig_timeline = px.line(
        timeline_data,
        x='Date',
        y='Count',
        color='Status',
        title="Daily Order Trends by Status"
    )
    st.plotly_chart(fig_timeline, use_container_width=True)

with col2:
    st.subheader("Regional Analysis")
    regional_data = filtered_df.groupby('Region').agg({
        'Order ID': 'count',
        'Shipping Cost': 'sum'
    }).round(2)
    regional_data.columns = ['Orders', 'Total Shipping Cost']
    
    fig_region = px.bar(
        regional_data.reset_index(),
        x='Region',
        y='Orders',
        title="Orders by Region",
        color='Total Shipping Cost',
        color_continuous_scale='Greens'
    )
    st.plotly_chart(fig_region, use_container_width=True)

# Detailed orders table
st.markdown("---")
st.subheader("Order Details")

# Configure AgGrid
gb = GridOptionsBuilder.from_dataframe(filtered_df)

# Configure columns
gb.configure_default_column(
    groupable=True,
    value=True,
    enableRowGroup=True,
    editable=False,
    filterable=True,
    sortable=True
)

# Configure specific columns
gb.configure_column("Order ID", pinned="left", width=120)
gb.configure_column("Order Date", type=["dateColumnFilter", "customDateTimeFormat"], 
                   custom_format_string="yyyy-MM-dd HH:mm")
gb.configure_column("Shipping Cost", type=["numericColumn", "numberColumnFilter", "customCurrencyFormat"],
                   custom_currency_symbol="$")

# Status column with custom styling
status_style = JsCode("""
function(params) {
    if (params.value == 'Delivered') {
        return {'color': 'white', 'backgroundColor': 'green'}
    } else if (params.value == 'Delayed') {
        return {'color': 'white', 'backgroundColor': 'red'}
    } else if (params.value == 'Cancelled') {
        return {'color': 'white', 'backgroundColor': 'darkred'}
    } else if (params.value == 'Shipped' || params.value == 'In Transit') {
        return {'color': 'white', 'backgroundColor': 'blue'}
    } else {
        return {'color': 'black', 'backgroundColor': 'lightgray'}
    }
}
""")

gb.configure_column("Status", cellStyle=status_style)

# Configure selection and pagination
gb.configure_selection("multiple", use_checkbox=True)
gb.configure_pagination(paginationAutoPageSize=True)

gridOptions = gb.build()

# Display the grid
grid_response = AgGrid(
    filtered_df,
    gridOptions=gridOptions,
    data_return_mode='AS_INPUT',
    update_mode='MODEL_CHANGED',
    fit_columns_on_grid_load=True,
    enable_enterprise_modules=False,
    height=400,
    allow_unsafe_jscode=True
)

# Selected orders summary
if grid_response['selected_rows']:
    st.subheader("Selected Orders Summary")
    selected_df = pd.DataFrame(grid_response['selected_rows'])
    
    col1, col2, col3 = st.columns(3)
    with col1:
        st.metric("Selected Orders", len(selected_df))
    with col2:
        selected_cost = selected_df['Shipping Cost'].sum()
        st.metric("Total Shipping Cost", f"${selected_cost:.2f}")
    with col3:
        selected_delivered = len(selected_df[selected_df['Status'] == 'Delivered'])
        selected_rate = (selected_delivered / len(selected_df) * 100) if len(selected_df) > 0 else 0
        st.metric("Delivery Rate", f"{selected_rate:.1f}%")

# Footer
st.markdown("---")
st.markdown("*Dashboard updates in real-time. Use filters to customize your view.*")