import json

import datetime

class KaizenDiscountEngine:

def __init__(self):

self.target_discount = 0.50 # 50% Threshold

self.active_deals = []

def analyze_deal_relevance(self, deal_data):

"""

Scientific reasoning: Evaluates deal authenticity

based on stock levels and expiration timestamps.

"""

discount_value = deal_data.get("discount_percent", 0)

is_active = deal_data.get("stock_count", 0) > 0

# Superfast filtering: Only process 50%+

if discount_value >= self.target_discount and is_active:

return True

return False

def generate_hud_report(self):

"""Generates a high-visibility HUD output for the user."""

print(f"\n--- [AI SHOPPING HUD: ACTIVE DEALS] ---")

print(f"STATUS: OPTIMIZING | TIMESTAMP: {datetime.datetime.now()}")

print("-" * 40)

for deal in self.active_deals:

print(f"▶ ITEM: {deal['name']} | PRICE: -{deal['discount_percent']*100}%")

print(f" LINK: {deal['url']}")

print("-" * 40)

# Example Instance Data

raw_deals = [

{"name": "Ultra-HD Monitor", "discount_percent": 0.50, "stock_count": 5, "url": "https://store.ai/monitor"},

{"name": "Mechanical Keyboard", "discount_percent": 0.20, "stock_count": 12, "url": "https://store.ai/kb"},

{"name": "Neural Processing Unit", "discount_percent": 0.55, "stock_count": 2, "url": "https://store.ai/npu"}

]

# Execution Logic

ai_agent = KaizenDiscountEngine()

for deal in raw_deals:

if ai_agent.analyze_deal_relevance(deal):

ai_agent.active_deals.append(deal)

ai_agent.generate_hud_report()

import asyncio

import json

from datetime import datetime

from dataclasses import dataclass

@dataclass

class DiscountHUD:

"""HUD Representation for Real-Time Monitoring"""

item: str

original_price: float

current_price: float

discount: float

verified: bool

class KaizenShoppingAgent:

def __init__(self, target_threshold=0.50):

self.threshold = target_threshold

self.active_session = True

async def scan_market(self, source_url):

"""Simulates superfast concurrent scraping from e-commerce nodes."""

# In production, replace with ScrapeGraph AI or Playwright

await asyncio.sleep(0.1)

return [

{"name": "Developer Monitor", "msrp": 1000, "price": 499},

{"name": "Neural Link Cable", "msrp": 50, "price": 25},

{"name": "Mechanical Keyboard", "msrp": 200, "price": 180}

]

def scientific_verification(self, msrp, current):

"""

Scientific Reasoning: Calculates the exact delta

to confirm if the discount meets the 50% target.

"""

actual_discount = 1 - (current / msrp)

return actual_discount >= self.threshold, actual_discount

async def run_kaizen_cycle(self, sources):

"""Continuous improvement loop: Scan -> Verify -> Report."""

print(f"--- [STARTING AI HUD | {datetime.now().strftime('%H:%M:%S')}] ---")

for source in sources:

products = await self.scan_market(source)

for p in products:

is_50_plus, delta = self.scientific_verification(p['msrp'], p['price'])

if is_50_plus:

hud = DiscountHUD(p['name'], p['msrp'], p['price'], delta, True)

self.render_hud(hud)

def render_hud(self, data: DiscountHUD):

"""High-Definition Console HUD Output"""

print(f" [!] ALERT: {data.item.upper()} detected")

print(f" STATUS: ACTIVE DISCOUNT {data.discount*100:.0f}%")

print(f" VAL: ${data.current_price} (was ${data.original_price})")

print(f" VERIFIED: {data.verified}\n")

# Execution Instance

if __name__ == "__main__":

agent = KaizenShoppingAgent()

urls = ["https://api.store-a.com/v1", "https://api.store-b.com/v1"]

asyncio.run(agent.run_kaizen_cycle(urls))