AI E-Commerce Automation: Descriptions and Dynamic Pricing

import os
import json
import time
import requests
from typing import Dict, Any

# =====================================================================
# E-COMMERCE AI CATALOG ENRICHMENT & DYNAMIC PRICING ENGINE
# =====================================================================

class AutonomousEcommerceEngine:
    def __init__(self, anthropic_key: str, shopify_token: str, gmc_api_key: str):
        self.anthropic_key = anthropic_key
        self.shopify_token = shopify_token
        self.gmc_api_key = gmc_api_key
        self.anthropic_url = "https://api.anthropic.com/v1/messages"

    def enrich_product_copy(self, raw_sku_data: Dict[str, Any]) -> Dict[str, Any]:
        '''
        Executes structured LLM prompt to generate high-converting SEO copy,
        feature-benefit bullets, and FAQ schema from raw technical specifications.
        '''
        prompt = f'''
        You are an elite E-Commerce Merchandiser and Technical SEO Specialist.
        Analyze this raw product data and generate complete, publication-ready copy:
        
        SKU: {raw_sku_data.get('sku')}
        Title: {raw_sku_data.get('raw_title')}
        Brand: {raw_sku_data.get('brand')}
        Raw Specs: {json.dumps(raw_sku_data.get('specs', {}))}
        Target Audience: {raw_sku_data.get('target_audience', 'Modern Consumers')}

        Output strictly a valid JSON object matching this schema:
        {{
            "meta_title": "string (52-58 chars)",
            "meta_description": "string (145-155 chars)",
            "hook_paragraph": "string (compelling 2 sentences)",
            "long_description_html": "string (clean semantic HTML paragraphs and tags)",
            "benefit_bullets": ["bullet 1", "bullet 2", "bullet 3", "bullet 4"],
            "faq_schema": [
                {{"q": "question text", "a": "authoritative answer text"}}
            ]
        }}
        Do not wrap in markdown or backticks. Return raw JSON only.
        '''

        headers = {
            "x-api-key": self.anthropic_key,
            "anthropic-version": "2023-06-01",
            "content-type": "application/json"
        }
        payload = {
            "model": "claude-3-5-sonnet-20241022",
            "max_tokens": 2048,
            "temperature": 0.3,
            "messages": [{"role": "user", "content": prompt}]
        }

        resp = requests.post(self.anthropic_url, headers=headers, json=payload, timeout=30)
        resp_json = resp.json()
        raw_text = resp_json['content'][0]['text'].strip()
        return json.loads(raw_text)

    def calculate_dynamic_price(self, sku_data: Dict[str, Any], competitor_price: float) -> float:
        '''
        Evaluates gross margin floor, stock velocity, and competitor price
        to determine the optimal profit-maximizing price.
        '''
        cogs = sku_data['cogs']
        target_margin_pct = 0.35  # 35% minimum gross margin floor
        margin_floor = cogs / (1.0 - target_margin_pct)
        current_price = sku_data['current_price']
        inventory_days = sku_data.get('inventory_days_remaining', 45)

        # Competitor repricing logic
        if competitor_price > 0:
            # Undercut competitor by $0.05 if above margin floor
            suggested_price = competitor_price - 0.05
            if suggested_price < margin_floor:
                # Protect margin floor
                optimal_price = margin_floor
            else:
                optimal_price = suggested_price
        else:
            optimal_price = current_price

        # Inventory velocity modifier
        if inventory_days > 90:
            # Overstocked: Apply 5% liquidation markdown if above floor
            optimal_price = max(margin_floor, optimal_price * 0.95)
        elif inventory_days < 15:
            # Scarcity premium: Lift price by 8% to capture margin
            optimal_price = optimal_price * 1.08

        return round(optimal_price, 2)

    def execute_atomic_update(self, sku: str, new_copy: Dict[str, Any], new_price: float):
        '''
        Synchronously pushes updates to Shopify Storefront and Google Merchant Center
        to ensure zero price mismatch suspensions.
        '''
        print(f"[SYNC] Pushing SKU {sku} to Shopify: Price=${new_price}")
        # Shopify REST/GraphQL API Call here...
        
        print(f"[SYNC] Pushing SKU {sku} to Google Content API for Shopping: Price=${new_price}")
        # GMC products.patch API Call here...
        
        print(f"✓ SKU {sku} successfully synchronized across storefront and Google Shopping.")

# Production Run Example
if __name__ == "__main__":
    sample_product = {
        "sku": "PWM-NOISE-CANCEL-PRO",
        "raw_title": "Wireless Active Noise Cancelling Headphones Over Ear 40mm Driver",
        "brand": "AcousticTech Pro",
        "cogs": 42.50,
        "current_price": 129.99,
        "inventory_days_remaining": 12,
        "specs": {
            "driver_size": "40mm Bio-Cellulose",
            "anc_depth": "-42dB Hybrid Dual-Mic",
            "battery_life": "55 Hours ANC On",
            "bluetooth": "5.4 with LDAC & AAC codecs",
            "weight": "245 grams"
        }
    }

    engine = AutonomousEcommerceEngine("test_key", "test_shopify", "test_gmc")
    
    # 1. Compute dynamic price against competitor price of $119.00
    new_price = engine.calculate_dynamic_price(sample_product, competitor_price=119.00)
    print(f"Calculated Optimal Price: ${new_price}")

-- BIGQUERY SQL: SKU PRICE ELASTICITY & CONTRIBUTION MARGIN CALCULATOR
WITH daily_sku_metrics AS (
  SELECT
    sku,
    DATE(order_timestamp) AS order_date,
    AVG(unit_price) AS avg_daily_price,
    SUM(quantity) AS units_sold,
    AVG(unit_cogs) AS avg_unit_cogs,
    SUM(return_quantity) AS units_returned
  FROM `enterprise-ecommerce.warehouse.order_line_items`
  WHERE order_timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 90 DAY)
  GROUP BY 1, 2
),
price_shifts AS (
  SELECT
    sku,
    order_date,
    avg_daily_price,
    units_sold,
    LAG(avg_daily_price, 7) OVER (PARTITION BY sku ORDER BY order_date) AS prev_week_price,
    LAG(units_sold, 7) OVER (PARTITION BY sku ORDER BY order_date) AS prev_week_units
  FROM daily_sku_metrics
)
SELECT
  sku,
  COUNT(order_date) AS active_trading_days,
  ROUND(AVG(avg_daily_price), 2) AS current_avg_price,
  ROUND(AVG(units_sold), 1) AS avg_daily_volume,
  -- Calculate Elasticity: (% Change in Quantity) / (% Change in Price)
  ROUND(AVG(
    SAFE_DIVIDE(
      (units_sold - prev_week_units) / NULLIF(prev_week_units, 0),
      (avg_daily_price - prev_week_price) / NULLIF(prev_week_price, 0)
    )
  ), 2) AS empirical_elasticity_score,
  CASE
    WHEN AVG(SAFE_DIVIDE((units_sold - prev_week_units) / NULLIF(prev_week_units, 0), (avg_daily_price - prev_week_price) / NULLIF(prev_week_price, 0))) < -1.2 THEN 'Highly Elastic (Competitive)'
    WHEN AVG(SAFE_DIVIDE((units_sold - prev_week_units) / NULLIF(prev_week_units, 0), (avg_daily_price - prev_week_price) / NULLIF(prev_week_price, 0))) BETWEEN -1.2 AND -0.8 THEN 'Unitary'
    ELSE 'Inelastic (Margin Expansion Opportunity)'
  END AS pricing_action_category
FROM price_shifts
WHERE prev_week_price IS NOT NULL AND prev_week_units IS NOT NULL AND prev_week_price != avg_daily_price
GROUP BY sku
HAVING active_trading_days >= 30
ORDER BY empirical_elasticity_score ASC;