import requests
from datetime import datetime

class ProductResearcher:
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://api.shopify.com/storefront"

    def get_trending_products(self, niche, limit=50):
        """Fetch trending products in a specific niche"""
        query = """
        {
          products(first: %d, query: "tag:%s") {
            edges {
              node {
                title
                priceRange {
                  minVariantPrice {
                    amount
                    currencyCode
                  }
                }
                images(first: 1) {
                  edges {
                    node {
                      url
                    }
                  }
                }
              }
            }
          }
        }
        """ % (limit, niche)

        headers = {
            "X-Shopify-Storefront-Access-Token": self.api_key,
            "Content-Type": "application/json"
        }

        response = requests.post(
            f"{self.base_url}/graphql.json",
            json={"query": query},
            headers=headers
        )

        return response.json()

# Usage
researcher = ProductResearcher("your_storefront_token")
products = researcher.get_trending_products("pet-supplies")

import schedule
import time
from dataclasses import dataclass
from typing import List

@dataclass
class PriceAlert:
    product_name: str
    current_price: float
    target_price: float
    url: str

class PriceMonitor:
    def __init__(self):
        self.alerts: List[PriceAlert] = []

    def add_alert(self, product_name, target_price, url):
        self.alerts.append(PriceAlert(
            product_name=product_name,
            current_price=0,
            target_price=target_price,
            url=url
        ))

    def check_prices(self):
        """Run every hour to check prices"""
        for alert in self.alerts:
            current = self.fetch_price(alert.url)
            alert.current_price = current

            if current <= alert.target_price:
                self.send_notification(alert)
                print(f"🚨 PRICE DROP: {alert.product_name}")
                print(f"   Now: ${current} (Target: ${alert.target_price})")

    def fetch_price(self, url):
        # Implement your scraping/API logic here
        pass

    def send_notification(self, alert):
        # Send Slack/email notification
        pass

# Setup
monitor = PriceMonitor()
monitor.add_alert("Pet Grooming Kit", 15.00, "https://supplier.com/product/123")

# Run every hour
schedule.every(1).hours.do(monitor.check_prices)

while True:
    schedule.run_pending()
    time.sleep(60)

import hashlib
import hmac
import json
from datetime import datetime

class OrderAutomation:
    def __init__(self, supplier_api_key, supplier_secret):
        self.api_key = supplier_api_key
        self.secret = supplier_secret

    def process_order(self, order_data):
        """Process incoming Shopify order and forward to supplier"""

        # Validate order
        if not self.validate_order(order_data):
            return {"status": "error", "message": "Invalid order"}

        # Format for supplier API
        supplier_order = {
            "order_id": order_data["id"],
            "customer": {
                "name": order_data["shipping_address"]["name"],
                "email": order_data["email"],
                "address": order_data["shipping_address"]
            },
            "items": [
                {
                    "sku": item["sku"],
                    "quantity": item["quantity"],
                    "price": item["price"]
                }
                for item in order_data["line_items"]
            ],
            "timestamp": datetime.now().isoformat()
        }

        # Send to supplier
        response = self.send_to_supplier(supplier_order)

        # Update Shopify with tracking
        if response["status"] == "success":
            self.update_shopify_tracking(
                order_data["id"],
                response["tracking_number"]
            )

        return response

    def validate_order(self, order):
        required_fields = ["id", "email", "shipping_address", "line_items"]
        return all(field in order for field in required_fields)

    def send_to_supplier(self, order):
        # Implement API call to your supplier
        print(f"📦 Forwarding order {order['order_id']} to supplier")
        return {"status": "success", "tracking_number": "TRACK123"}

    def update_shopify_tracking(self, order_id, tracking_number):
        # Update order in Shopify with tracking info
        print(f"✅ Updated order {order_id} with tracking: {tracking_number}")

import requests
from collections import Counter

class NicheAnalyzer:
    def __init__(self):
        self.trends_api = "https://trends.google.com/trends/api"

    def analyze_niche(self, keyword):
        """Analyze search trends for a niche"""

        # Get search volume data
        search_data = self.get_search_volume(keyword)

        # Calculate score
        score = self.calculate_profitability_score(search_data)

        return {
            "keyword": keyword,
            "search_volume": search_data["volume"],
            "competition": search_data["competition"],
            "trend": search_data["trend"],
            "profitability_score": score,
            "recommendation": self.get_recommendation(score)
        }

    def calculate_profitability_score(self, data):
        """Score from 0-100 based on multiple factors"""
        score = 0

        # Search volume (0-30 points)
        if data["volume"] > 100000:
            score += 30
        elif data["volume"] > 50000:
            score += 20
        elif data["volume"] > 10000:
            score += 10

        # Competition (0-30 points, lower is better)
        if data["competition"] < 0.3:
            score += 30
        elif data["competition"] < 0.5:
            score += 20
        elif data["competition"] < 0.7:
            score += 10

        # Trend direction (0-40 points)
        if data["trend"] == "rising":
            score += 40
        elif data["trend"] == "stable":
            score += 20

        return score

    def get_recommendation(self, score):
        if score >= 70:
            return "🟢 STRONG - High potential niche"
        elif score >= 50:
            return "🟡 MODERATE - Worth testing"
        elif score >= 30:
            return "🟠 WEAK - Consider alternatives"
        else:
            return "🔴 AVOID - Too competitive or low demand"

# Usage
analyzer = NicheAnalyzer()
results = [
    analyzer.analyze_niche("pet supplies"),
    analyzer.analyze_niche("home fitness"),
    analyzer.analyze_niche("smart home"),
]

for result in results:
    print(f"\n{result['keyword'].upper()}")
    print(f"Score: {result['profitability_score']}/100")
    print(f"Recommendation: {result['recommendation']}")

class DropshippingAutomation:
    def __init__(self):
        self.researcher = ProductResearcher("token")
        self.monitor = PriceMonitor()
        self.orders = OrderAutomation("key", "secret")
        self.analyzer = NicheAnalyzer()

    def daily_routine(self):
        """Run daily automation tasks"""
        print("🌅 Starting daily automation...")

        # 1. Research new products
        print("\n📊 Researching trending products...")
        products = self.researcher.get_trending_products("pet-supplies")

        # 2. Analyze niches
        print("\n🔍 Analyzing niche profitability...")
        niches = ["pet supplies", "home fitness", "sustainable goods"]
        for niche in niches:
            result = self.analyzer.analyze_niche(niche)
            print(f"  {result['keyword']}: {result['profitability_score']}/100")

        # 3. Check prices
        print("\n💰 Checking price alerts...")
        self.monitor.check_prices()

        print("\n✅ Daily automation complete!")

# Run the system
automation = DropshippingAutomation()
automation.daily_routine()