# Save all quote sessions to a file
curl -s "https://caspio-pricing-proxy-ab30a049961a.herokuapp.com/api/quote_sessions" -o /c/Users/erik/Downloads/quotes_full.json

# Save all quote items to analyze product selections
curl -s "https://caspio-pricing-proxy-ab30a049961a.herokuapp.com/api/quote_items" -o /c/Users/erik/Downloads/all_items.json

cd /c/Users/erik/Downloads && python3 << 'PYEOF'
import json
from collections import defaultdict, Counter

# Load quote sessions
with open('quotes_full.json', 'r') as f:
    sessions = json.load(f)

# Load quote items
with open('all_items.json', 'r') as f:
    all_items = json.load(f)

# CRITICAL: Apply TWO filters (BOTH must be true)
processed_xmas = [
    order for order in sessions
    if order.get('QuoteID', '').startswith('XMAS')     # Filter 1: XMAS prefix
    and order.get('Status') == 'Processed'              # Filter 2: Processed status
]

print(f'Total Processed XMAS Orders: {len(processed_xmas)}')
print()

# Calculate totals
total_revenue = sum(float(order.get('TotalAmount', 0)) for order in processed_xmas)
total_quantity = sum(int(order.get('TotalQuantity', 0)) for order in processed_xmas)

print(f'Total Revenue: ${total_revenue:,.2f}')
print(f'Total Bundles: {total_quantity}')
print()

# Group by sales rep
by_rep = defaultdict(lambda: {'count': 0, 'revenue': 0, 'quantity': 0})

for order in processed_xmas:
    # Priority: SalesRepName, then SalesRep, then Unassigned
    rep = order.get('SalesRepName') or order.get('SalesRep') or 'Unassigned'
    by_rep[rep]['count'] += 1
    by_rep[rep]['revenue'] += float(order.get('TotalAmount', 0))
    by_rep[rep]['quantity'] += int(order.get('TotalQuantity', 0))

# Display sales rep breakdown
print('Sales Rep Breakdown:')
for rep in sorted(by_rep.keys(), key=lambda r: by_rep[r]['count'], reverse=True):
    stats = by_rep[rep]
    pct = (stats['count'] / len(processed_xmas) * 100) if processed_xmas else 0
    avg = stats['revenue'] / stats['count'] if stats['count'] > 0 else 0
    print(f"  {rep}: {stats['count']} orders ({pct:.0f}%) | ${stats['revenue']:,.2f} revenue | {stats['quantity']} bundles | Avg: ${avg:.2f}/order")

print()
print("=" * 60)
print("TOP SELLING CHRISTMAS BUNDLE PRODUCTS")
print("=" * 60)
print()

# Get processed XMAS quote IDs
processed_xmas_ids = set(order['QuoteID'] for order in processed_xmas)

# Filter items for Processed XMAS orders
xmas_items = [
    item for item in all_items
    if item.get('QuoteID') in processed_xmas_ids
]

# Parse BundleConfiguration to extract product selections
jackets = []
hoodies = []
beanies = []
gloves = []

for item in xmas_items:
    bundle_config = item.get('BundleConfiguration')
    if bundle_config:
        try:
            config = json.loads(bundle_config)

            # CRITICAL: Count ORDERS not bundle quantity!
            # Each ORDER has one jacket style choice, one hoodie style choice, etc.
            # Even if someone orders 4 bundles (Quantity=4), all 4 bundles have the SAME jacket
            # So we count this as 1 order choosing that jacket style
            # Do NOT multiply by Quantity - just count the order once

            if 'jacket' in config:
                jacket_full = config['jacket']
                jacket_style = jacket_full.split(' - ')[0] if ' - ' in jacket_full else jacket_full
                jackets.append(jacket_style)  # Count once per order

            if 'hoodie' in config:
                hoodie_full = config['hoodie']
                hoodie_style = hoodie_full.split(' - ')[0] if ' - ' in hoodie_full else hoodie_full
                hoodies.append(hoodie_style)  # Count once per order

            if 'beanie' in config:
                beanie_full = config['beanie']
                beanie_style = beanie_full.split(' - ')[0] if ' - ' in beanie_full else beanie_full
                beanies.append(beanie_style)  # Count once per order

            if 'gloves' in config:
                gloves_full = config['gloves']
                gloves_style = gloves_full.split(' - ')[0] if ' - ' in gloves_full else gloves_full
                gloves.append(gloves_style)  # Count once per order

        except Exception as e:
            pass

# Display top selling products
if jackets:
    jacket_counter = Counter(jackets)
    print("TOP JACKETS:")
    for style, count in jacket_counter.most_common(10):
        pct = (count / len(jackets) * 100) if jackets else 0
        print(f"  {style}: {count} orders ({pct:.1f}%)")
    print()

if hoodies:
    hoodie_counter = Counter(hoodies)
    print("TOP HOODIES:")
    for style, count in hoodie_counter.most_common(10):
        pct = (count / len(hoodies) * 100) if hoodies else 0
        print(f"  {style}: {count} orders ({pct:.1f}%)")
    print()

if beanies:
    beanie_counter = Counter(beanies)
    print("TOP BEANIES:")
    for style, count in beanie_counter.most_common(10):
        pct = (count / len(beanies) * 100) if beanies else 0
        print(f"  {style}: {count} orders ({pct:.1f}%)")
    print()

if gloves:
    gloves_counter = Counter(gloves)
    print("TOP GLOVES:")
    for style, count in gloves_counter.most_common(10):
        pct = (count / len(gloves) * 100) if gloves else 0
        print(f"  {style}: {count} orders ({pct:.1f}%)")

print()
print("=" * 60)
print("DAY OF WEEK ANALYSIS")
print("=" * 60)
print()

# Analyze day of week from CreatedAt field
from datetime import datetime

days_of_week = []

for order in processed_xmas:
    created_at = order.get('CreatedAt')
    if created_at:
        try:
            # Parse the datetime string
            dt = datetime.strptime(created_at, '%Y-%m-%dT%H:%M:%S')

            # Get day of week (0=Monday, 6=Sunday)
            day_num = dt.weekday()
            day_names = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
            day_name = day_names[day_num]

            days_of_week.append(day_name)
        except Exception as e:
            pass

# Count orders by day of week
day_counter = Counter(days_of_week)

print("ORDERS BY DAY OF WEEK:")
print()

# Display in order Monday-Sunday
day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
for day in day_order:
    count = day_counter.get(day, 0)
    pct = (count / len(days_of_week) * 100) if days_of_week else 0
    bar = '█' * int(pct / 2)  # Visual bar chart
    print(f"  {day:12} : {count:3} orders ({pct:5.1f}%) {bar}")

print()
if day_counter:
    most_popular = day_counter.most_common(1)[0]
    print(f"Most Popular Day: {most_popular[0]} with {most_popular[1]} orders")

print()
print("WEEKDAY vs WEEKEND:")
weekday_count = sum(day_counter.get(day, 0) for day in ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'])
weekend_count = sum(day_counter.get(day, 0) for day in ['Saturday', 'Sunday'])

if days_of_week:
    weekday_pct = (weekday_count / len(days_of_week) * 100)
    weekend_pct = (weekend_count / len(days_of_week) * 100)
    print(f"  Weekdays (Mon-Fri): {weekday_count} orders ({weekday_pct:.1f}%)")
    print(f"  Weekends (Sat-Sun): {weekend_count} orders ({weekend_pct:.1f}%)")
PYEOF

📊 Christmas Bundle Order Summary (PROCESSED ONLY)

Total PROCESSED Orders: 20
Total Revenue: $5,712.00
Total Bundles: 80

Sales Rep Breakdown (Processed Orders Only):
👤 Taneisha: 11 orders (55%) | $3,181.00 revenue | 44 bundles | Avg: $289.18/order
👤 Nika: 9 orders (45%) | $2,531.00 revenue | 36 bundles | Avg: $281.22/order

Recent Processed Orders:
1. XMAS1020-1128 - Kelsie Stroud (Nika) - 4 bundles - $283.00 - Processed
2. XMAS1017-2560 - April M Edwards (Taneisha) - 4 bundles - $238.00 - Processed
[... continue list ...]

Note: This report ONLY includes orders with Status="Processed".
Archived and Open orders are excluded from all totals.