import streamlit as st
import folium
from streamlit_folium import folium_static
import pandas as pd
import os
import plotly.figure_factory as ff
import plotly.express as px
from pathlib import Path
from datetime import datetime, timedelta
import numpy as np
def map_page():
"""
Render the map visualization page with delivery and depot locations.
Can be called from app.py to display within the main application.
"""
st.title("Delivery Route Map")
st.write("""
This page visualizes the delivery locations and vehicle depots on an interactive map.
Use the filters in the sidebar to customize the view.
""")
# Add help section with expander
with st.expander("📚 How to Use this Page"):
st.markdown("""
## Step-by-Step Guide to the Map Page
The Map page provides an interactive visualization of all delivery locations and vehicle depots. It helps you understand delivery distribution, monitor delivery status, and plan logistics operations.
### 1. Map Navigation
- **Pan**: Click and drag to move around the map
- **Zoom**: Use the scroll wheel or the +/- buttons in the top-left corner
- **View Details**: Click on any marker to see detailed information about that delivery or depot
### 2. Using Map Filters (Sidebar)
- **Show/Hide Elements**:
- Toggle "Show Deliveries" to display or hide delivery markers
- Toggle "Show Depots" to display or hide vehicle depot markers
- Enable "Show Data Table" to view raw delivery data below the map
- Enable "Show Calendar View" to see delivery schedules organized by date
- **Filter by Attributes**:
- Use "Filter by Priority" to show only deliveries of selected priority levels (High, Medium, Low)
- Use "Filter by Status" to show only deliveries with selected statuses (Pending, In Transit, Delivered)
- **Date Filtering**:
- Use the "Date Range" selector to focus on deliveries within specific dates
- This affects both the map display and the calendar view
### 3. Understanding the Map Markers
- **Delivery Markers**:
- Red markers: High priority deliveries
- Orange markers: Medium priority deliveries
- Blue markers: Low priority deliveries
- **Depot Markers**:
- Green house icons: Vehicle depot locations
### 4. Using the Calendar View
- Select specific dates from the dropdown to view scheduled deliveries
- Each tab shows deliveries for one selected date
- Timeline bars are color-coded by priority (red=High, orange=Medium, blue=Low)
- Hover over timeline bars to see detailed delivery information
- Check the summary metrics below each calendar for quick insights
### 5. Reading the Delivery Statistics
- The top section shows key metrics about displayed deliveries:
- Total number of deliveries shown
- Total weight of all displayed deliveries
- Number of pending deliveries
- Breakdown of deliveries by status
### 6. Data Table Features
When "Show Data Table" is enabled:
- Green highlighted rows: Completed deliveries
- Red highlighted rows: Urgent high-priority deliveries due within the next week
- Sort any column by clicking the column header
- Search across all fields using the search box
This map view helps you visualize your delivery operations geographically while the calendar provides a time-based perspective of your delivery schedule.
""")
# Initialize session state variables for filters
if 'map_filters' not in st.session_state:
st.session_state.map_filters = {
'selected_dates': ["All"],
'priority_filter': [],
'status_filter': [],
'date_range': [None, None],
'show_calendar': True,
'show_map': True,
'show_data_table': False,
'cluster_markers': True
}
# Create filters in sidebar
with st.sidebar:
st.header("Map Filters")
# Show/hide options - use session state values as defaults
show_deliveries = st.checkbox(
"Show Deliveries",
value=st.session_state.map_filters.get('show_deliveries', True),
key="show_deliveries_checkbox"
)
st.session_state.map_filters['show_deliveries'] = show_deliveries
show_depots = st.checkbox(
"Show Depots",
value=st.session_state.map_filters.get('show_depots', True),
key="show_depots_checkbox"
)
st.session_state.map_filters['show_depots'] = show_depots
# Show/hide data table
show_data_table = st.checkbox(
"Show Data Table",
value=st.session_state.map_filters.get('show_data_table', False),
key="show_data_table_checkbox"
)
st.session_state.map_filters['show_data_table'] = show_data_table
# Choose visualization tabs
show_calendar = st.checkbox(
"Show Calendar View",
value=st.session_state.map_filters.get('show_calendar', True),
key="show_calendar_checkbox"
)
st.session_state.map_filters['show_calendar'] = show_calendar
# Try to load data
try:
# Get data paths
root_dir = Path(__file__).resolve().parent.parent.parent # Go up to project root level
delivery_path = os.path.join(root_dir, 'data', 'delivery-data', 'delivery_data.csv') # Fixed directory name with underscore
vehicle_path = os.path.join(root_dir, 'data', 'vehicle-data', 'vehicle_data.csv') # Fixed directory name with underscore
# Check if files exist
if not os.path.exists(delivery_path):
# Try with hyphen instead of underscore
delivery_path = os.path.join(root_dir, 'data', 'delivery-data', 'delivery_data.csv')
if not os.path.exists(delivery_path):
st.warning(f"Delivery data file not found at: {delivery_path}")
st.info("Please generate data first with: python src/utils/generate_all_data.py")
return
if not os.path.exists(vehicle_path):
# Try with hyphen instead of underscore
vehicle_path = os.path.join(root_dir, 'data', 'vehicle-data', 'vehicle_data.csv')
if not os.path.exists(vehicle_path):
st.warning(f"Vehicle data file not found at: {vehicle_path}")
st.info("Please generate data first with: python src/utils/generate_all_data.py")
return
# Load data
delivery_data = pd.read_csv(delivery_path)
vehicle_data = pd.read_csv(vehicle_path)
# Ensure delivery_date is properly formatted as datetime
if 'delivery_date' in delivery_data.columns:
delivery_data['delivery_date'] = pd.to_datetime(delivery_data['delivery_date'])
# Add more filters if data is available - CONVERT TO MULTI-SELECT
if 'priority' in delivery_data.columns:
with st.sidebar:
all_priorities = sorted(delivery_data['priority'].unique().tolist())
selected_priorities = st.multiselect(
"Filter by Priority",
options=all_priorities,
default=st.session_state.map_filters.get('priority_filter', all_priorities),
key="priority_multiselect"
)
st.session_state.map_filters['priority_filter'] = selected_priorities
if selected_priorities:
delivery_data = delivery_data[delivery_data['priority'].isin(selected_priorities)]
if 'status' in delivery_data.columns:
with st.sidebar:
all_statuses = sorted(delivery_data['status'].unique().tolist())
selected_statuses = st.multiselect(
"Filter by Status",
options=all_statuses,
default=st.session_state.map_filters.get('status_filter', all_statuses),
key="status_multiselect"
)
st.session_state.map_filters['status_filter'] = selected_statuses
if selected_statuses:
delivery_data = delivery_data[delivery_data['status'].isin(selected_statuses)]
if 'delivery_date' in delivery_data.columns:
with st.sidebar:
# Get the min/max dates from the ORIGINAL unfiltered data
# Load original data to get proper date range
original_data = pd.read_csv(delivery_path)
if 'delivery_date' in original_data.columns:
original_data['delivery_date'] = pd.to_datetime(original_data['delivery_date'])
min_date = original_data['delivery_date'].min().date()
max_date = original_data['delivery_date'].max().date()
# Get saved values from session state
saved_start_date = st.session_state.map_filters.get('date_range', [None, None])[0]
saved_end_date = st.session_state.map_filters.get('date_range', [None, None])[1]
# Validate saved dates - ensure they're within allowed range
if saved_start_date and saved_start_date < min_date:
saved_start_date = min_date
if saved_end_date and saved_end_date > max_date:
saved_end_date = max_date
# Set default values with proper validation
default_start_date = saved_start_date if saved_start_date else min_date
default_end_date = saved_end_date if saved_end_date else min(min_date + timedelta(days=7), max_date)
# Add date range picker
try:
date_range = st.date_input(
"Date Range",
value=(default_start_date, default_end_date),
min_value=min_date,
max_value=max_date,
key="date_range_input"
)
# Update session state with new date range
if len(date_range) == 2:
st.session_state.map_filters['date_range'] = list(date_range)
start_date, end_date = date_range
mask = (delivery_data['delivery_date'].dt.date >= start_date) & (delivery_data['delivery_date'].dt.date <= end_date)
delivery_data = delivery_data[mask]
except Exception as e:
# If there's any error with the date range, reset it
st.error(f"Error with date range: {e}")
st.session_state.map_filters['date_range'] = [min_date, max_date]
date_range = (min_date, max_date)
mask = (delivery_data['delivery_date'].dt.date >= min_date) & (delivery_data['delivery_date'].dt.date <= max_date)
delivery_data = delivery_data[mask]
# MOVED STATISTICS TO THE TOP
st.subheader("Delivery Overview")
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Deliveries Shown", len(delivery_data))
with col2:
if 'weight_kg' in delivery_data.columns:
total_weight = delivery_data['weight_kg'].sum()
st.metric("Total Weight", f"{total_weight:.2f} kg")
with col3:
if 'status' in delivery_data.columns:
pending = len(delivery_data[delivery_data['status'] == 'Pending'])
st.metric("Pending Deliveries", pending)
# Status count columns - dynamic based on available statuses
if 'status' in delivery_data.columns:
status_counts = delivery_data['status'].value_counts()
# Create a varying number of columns based on unique statuses
status_cols = st.columns(len(status_counts))
for i, (status, count) in enumerate(status_counts.items()):
with status_cols[i]:
# Choose color based on status
delta_color = "normal"
if status == "Delivered":
delta_color = "off"
elif status == "In Transit":
delta_color = "normal"
elif status == "Pending":
delta_color = "inverse" # Red
# Calculate percentage
percentage = round((count / len(delivery_data)) * 100, 1)
st.metric(
f"{status}",
count,
f"{percentage}% of total",
delta_color=delta_color
)
# Create map
singapore_coords = [1.3521, 103.8198] # Center of Singapore
m = folium.Map(location=singapore_coords, zoom_start=12)
# Add delivery markers
if show_deliveries:
for _, row in delivery_data.iterrows():
# Create popup content
popup_content = f"ID: {row['delivery_id']}
"
if 'customer_name' in row:
popup_content += f"Customer: {row['customer_name']}
"
if 'address' in row:
popup_content += f"Address: {row['address']}
"
if 'time_window' in row:
popup_content += f"Time Window: {row['time_window']}
"
if 'priority' in row:
popup_content += f"Priority: {row['priority']}
"
if 'delivery_date' in row:
popup_content += f"Date: {row['delivery_date'].strftime('%b %d, %Y')}
"
if 'status' in row:
popup_content += f"Status: {row['status']}
"
# Choose marker color based on priority
color = 'blue'
if 'priority' in row:
if row['priority'] == 'High':
color = 'red'
elif row['priority'] == 'Medium':
color = 'orange'
# Add marker to map
folium.Marker(
[row['latitude'], row['longitude']],
popup=folium.Popup(popup_content, max_width=300),
tooltip=f"Delivery {row['delivery_id']}",
icon=folium.Icon(color=color)
).add_to(m)
# Add depot markers
if show_depots:
for _, row in vehicle_data.iterrows():
# Create popup content
popup_content = f"Vehicle ID: {row['vehicle_id']}
"
if 'vehicle_type' in row:
popup_content += f"Type: {row['vehicle_type']}
"
if 'driver_name' in row:
popup_content += f"Driver: {row['driver_name']}
"
# Add marker to map
folium.Marker(
[row['depot_latitude'], row['depot_longitude']],
popup=folium.Popup(popup_content, max_width=300),
tooltip=f"Depot: {row['vehicle_id']}",
icon=folium.Icon(color='green', icon='home', prefix='fa')
).add_to(m)
# Display the map
folium_static(m, width=800, height=500)
# Display calendar visualization if selected
if show_calendar and 'delivery_date' in delivery_data.columns and 'time_window' in delivery_data.columns:
st.subheader("Delivery Schedule Calendar")
# Process data for calendar view
calendar_data = delivery_data.copy()
# Extract start and end times from time_window
calendar_data[['start_time', 'end_time']] = calendar_data['time_window'].str.split('-', expand=True)
# Create start and end datetime for each delivery
calendar_data['Start'] = pd.to_datetime(
calendar_data['delivery_date'].dt.strftime('%Y-%m-%d') + ' ' + calendar_data['start_time']
)
calendar_data['Finish'] = pd.to_datetime(
calendar_data['delivery_date'].dt.strftime('%Y-%m-%d') + ' ' + calendar_data['end_time']
)
# Create task column for Gantt chart
calendar_data['Task'] = calendar_data['delivery_id'] + ': ' + calendar_data['customer_name']
# Create color mapping for priority
if 'priority' in calendar_data.columns:
color_map = {'High': 'rgb(255, 0, 0)', 'Medium': 'rgb(255, 165, 0)', 'Low': 'rgb(0, 0, 255)'}
calendar_data['Color'] = calendar_data['priority'].map(color_map)
else:
calendar_data['Color'] = 'rgb(0, 0, 255)' # Default blue
# Get all available dates and add ƒmulti-select filter
all_dates = sorted(calendar_data['delivery_date'].dt.date.unique())
# Format dates for display in the dropdown
date_options = {date.strftime('%b %d, %Y'): date for date in all_dates}
# Get default selection from session state
default_selections = st.session_state.map_filters.get('calendar_selected_dates', [])
# Validate default selections - only keep dates that exist in current options
valid_default_selections = [date_str for date_str in default_selections if date_str in date_options.keys()]
# If no valid selections remain, default to first date (if available)
if not valid_default_selections and date_options:
valid_default_selections = [list(date_options.keys())[0]]
# Add multiselect for date filtering with validated defaults
selected_date_strings = st.multiselect(
"Select dates to display",
options=list(date_options.keys()),
default=valid_default_selections,
key="calendar_date_selector"
)
# Save selections to session state
st.session_state.map_filters['calendar_selected_dates'] = selected_date_strings
# Convert selected strings back to date objects
selected_dates = [date_options[date_str] for date_str in selected_date_strings]
if not selected_dates:
st.info("Please select at least one date to view the delivery schedule.")
else:
# Filter calendar data to only include selected dates
filtered_calendar = calendar_data[calendar_data['delivery_date'].dt.date.isin(selected_dates)]
# Group tasks by date for better visualization
date_groups = filtered_calendar.groupby(filtered_calendar['delivery_date'].dt.date)
# Create tabs only for the selected dates
date_tabs = st.tabs([date.strftime('%b %d, %Y') for date in selected_dates])
for i, (date, tab) in enumerate(zip(selected_dates, date_tabs)):
with tab:
# Filter data for this date
day_data = filtered_calendar[filtered_calendar['delivery_date'].dt.date == date]
if len(day_data) > 0:
# Create figure
fig = px.timeline(
day_data,
x_start="Start",
x_end="Finish",
y="Task",
color="priority" if 'priority' in day_data.columns else None,
color_discrete_map={"High": "red", "Medium": "orange", "Low": "blue"},
hover_data=["customer_name", "address", "weight_kg", "status"]
)
# Update layout
fig.update_layout(
title=f"Deliveries scheduled for {date.strftime('%b %d, %Y')}",
xaxis_title="Time of Day",
yaxis_title="Delivery",
height=max(300, 50 * len(day_data)),
yaxis={'categoryorder':'category ascending'}
)
# Display figure
st.plotly_chart(fig, use_container_width=True)
# Show summary
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Total Deliveries", len(day_data))
with col2:
if 'weight_kg' in day_data.columns:
st.metric("Total Weight", f"{day_data['weight_kg'].sum():.2f} kg")
with col3:
if 'priority' in day_data.columns and 'High' in day_data['priority'].values:
st.metric("High Priority", len(day_data[day_data['priority'] == 'High']))
# NEW - Add delivery status breakdown for this day
if 'status' in day_data.columns:
st.write("##### Deliveries by Status")
status_counts = day_data['status'].value_counts()
status_cols = st.columns(min(4, len(status_counts)))
for i, (status, count) in enumerate(status_counts.items()):
col_idx = i % len(status_cols)
with status_cols[col_idx]:
st.metric(status, count)
else:
st.info(f"No deliveries scheduled for {date.strftime('%b %d, %Y')}")
# Display raw data table if selected
if show_data_table:
st.subheader("Delivery Data")
# Create a copy for display
display_df = delivery_data.copy()
# Convert delivery_date back to string for display
if 'delivery_date' in display_df.columns:
display_df['delivery_date'] = display_df['delivery_date'].dt.strftime('%b %d, %Y')
# Compute which deliveries are urgent (next 7 days)
if 'delivery_date' in delivery_data.columns:
today = datetime.now().date()
next_week = today + timedelta(days=7)
# Function to highlight rows based on delivery status and urgency
def highlight_rows(row):
delivery_date = pd.to_datetime(row['delivery_date']).date() if 'delivery_date' in row else None
# Check status first - highlight delivered rows in green
if 'status' in row and row['status'] == 'Delivered':
return ['background-color: rgba(0, 255, 0, 0.1)'] * len(row)
# Then check for urgent high-priority deliveries - highlight in red
elif delivery_date and delivery_date <= next_week and delivery_date >= today and row['priority'] == 'High':
return ['background-color: rgba(255, 0, 0, 0.1)'] * len(row)
else:
return [''] * len(row)
# Display styled dataframe
st.dataframe(display_df.style.apply(highlight_rows, axis=1))
else:
st.dataframe(display_df)
except Exception as e:
st.error(f"Error loading data: {str(e)}")
st.info("Please generate the data first by running: python src/utils/generate_all_data.py")
st.write("Error details:", e) # Detailed error for debugging
# Make the function executable when file is run directly
if __name__ == "__main__":
# This is for debugging/testing the function independently
st.set_page_config(page_title="Map View - Delivery Route Optimization", page_icon="🗺️", layout="wide")
map_page()