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main.py
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main.py
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import networkx as nx
from sklearn.ensemble import RandomForestRegressor
from nltk.sentiment import SentimentIntensityAnalyzer
import numpy as np
import pandas as pd
from AmazonAPI import AmazonAPI
from notifiers import get_notifier
import time
from threading import Thread
from queue import PriorityQueue
# Load configuration
config = {
'access_key': 'YOUR_ACCESS_KEY',
'secret_key': 'YOUR_SECRET_KEY',
'partner_tag': 'YOUR_PARTNER_TAG',
'country': 'US'
}
# Initialize Amazon API client
amazon = AmazonAPI(**config)
# Initialize notifier
email = get_notifier('email')
class KnowledgeGraph:
def __init__(self):
self.graph = nx.Graph()
def add_node(self, node_type, node_id, attributes):
self.graph.add_node(node_id, type=node_type, **attributes)
def add_edge(self, node1_id, node2_id, relationship):
self.graph.add_edge(node1_id, node2_id, relationship=relationship)
def get_node_attributes(self, node_id):
return self.graph.nodes[node_id]
class TaskManager:
def __init__(self):
self.tasks = PriorityQueue()
def add_task(self, priority, agent_type, task_description):
self.tasks.put((priority, (agent_type, task_description)))
def get_next_task(self):
if not self.tasks.empty():
return self.tasks.get()[1]
return None
class DecisionMakingEngine:
def __init__(self):
self.sales_model = RandomForestRegressor()
self.sentiment_analyzer = SentimentIntensityAnalyzer()
def predict_sales(self, features):
return self.sales_model.predict(features)
def analyze_sentiment(self, text):
return self.sentiment_analyzer.polarity_scores(text)
def make_pricing_decision(self, product, market_trends, competitor_prices, inventory_levels):
# Implement pricing decision logic
# This is a placeholder implementation
avg_competitor_price = sum(competitor_prices.values()) / len(competitor_prices)
if inventory_levels > 100:
return min(avg_competitor_price * 0.9, product['current_price'])
else:
return max(avg_competitor_price * 1.1, product['current_price'])
class Agent:
def __init__(self, agent_type, knowledge_graph, task_manager, decision_engine):
self.agent_type = agent_type
self.kg = knowledge_graph
self.tm = task_manager
self.dme = decision_engine
def execute_task(self, task_description):
# Implementation would depend on the specific agent type
pass
class ProductAgent(Agent):
def execute_task(self, task_description):
if "Product Research" in task_description:
category = task_description.split("in ")[1]
products = self.research_top_selling_products(category)
for product in products:
self.kg.add_node("Product", product['asin'], product)
return f"Researched top-selling products in {category}"
elif "Pricing Analysis" in task_description:
product = task_description.split("of ")[1].split(" across")[0]
pricing_info = self.analyze_prices(product)
self.kg.add_node("PricingInfo", product, pricing_info)
return f"Analyzed prices for {product}"
elif "Inventory Management" in task_description:
product = task_description.split("for ")[1].split(" and")[0]
inventory_level = self.update_inventory(product)
self.tm.add_task(1, "Sales", f"Alert about stock availability for {product}")
return f"Updated inventory for {product}. Current level: {inventory_level}"
def research_top_selling_products(self, category):
# This would typically involve calling the Amazon API
# For now, we'll return dummy data
return [
{'asin': 'B08F7N', 'name': 'Top Product 1', 'category': category, 'price': 99.99},
{'asin': 'C09G8M', 'name': 'Top Product 2', 'category': category, 'price': 149.99},
]
def analyze_prices(self, product):
# This would typically involve calling APIs or web scraping
# For now, we'll return dummy data
return {
'amazon_price': 99.99,
'competitor1_price': 109.99,
'competitor2_price': 89.99,
}
def update_inventory(self, product):
# This would typically involve updating a database
# For now, we'll return a dummy inventory level
return 50
class CustomerAgent(Agent):
def execute_task(self, task_description):
if "Customer Profiling" in task_description:
profiles = self.create_customer_profiles()
for profile in profiles:
self.kg.add_node("CustomerProfile", profile['id'], profile)
return "Created customer profiles and added to knowledge graph"
elif "Sentiment Analysis" in task_description:
product = task_description.split("for ")[1]
sentiment = self.dme.analyze_sentiment(f"This is a great product! I love my new {product}.")
self.kg.add_node("Sentiment", product, sentiment)
return f"Sentiment analysis for {product}: {sentiment}"
elif "Personalized Marketing" in task_description:
segment = task_description.split("for ")[1].split(" based")[0]
campaign = self.generate_marketing_campaign(segment)
self.kg.add_node("MarketingCampaign", segment, campaign)
return f"Generated personalized marketing campaign for {segment}"
def create_customer_profiles(self):
# This would typically involve analyzing customer data
# For now, we'll return dummy profiles
return [
{'id': 'C001', 'name': 'John Doe', 'preferences': ['electronics', 'books']},
{'id': 'C002', 'name': 'Jane Smith', 'preferences': ['fashion', 'home decor']},
]
def generate_marketing_campaign(self, segment):
# This would typically involve creating targeted marketing content
# For now, we'll return a dummy campaign
return f"Special offers on top products for {segment} customers!"
class MarketAgent(Agent):
def execute_task(self, task_description):
if "Market Research" in task_description:
industry = task_description.split("for ")[1].split(" and")[0]
trends = self.research_market_trends(industry)
self.kg.add_node("MarketTrends", industry, trends)
return f"Researched market trends for {industry}"
elif "Competitor Monitoring" in task_description:
category = task_description.split("for ")[1]
competitor_info = self.monitor_competitors(category)
self.kg.add_node("CompetitorInfo", category, competitor_info)
return f"Monitored competitors for {category}"
elif "Trend Analysis" in task_description:
trends = self.analyze_trends()
self.kg.add_node("TrendAnalysis", "global", trends)
return "Analyzed global market trends"
def research_market_trends(self, industry):
# This would typically involve analyzing market data
# For now, we'll return dummy trends
return {
'growing_segments': ['Smart Home', 'Wearables'],
'declining_segments': ['Traditional PCs', 'Basic Cell Phones'],
}
def monitor_competitors(self, category):
# This would typically involve gathering competitor data
# For now, we'll return dummy competitor info
return {
'Competitor A': {'market_share': 0.3, 'top_product': 'Product X'},
'Competitor B': {'market_share': 0.25, 'top_product': 'Product Y'},
}
def analyze_trends(self):
# This would typically involve analyzing various data sources
# For now, we'll return dummy trend analysis
return {
'emerging_technologies': ['AI', '5G', 'Quantum Computing'],
'consumer_behavior_shifts': ['Increased online shopping', 'Focus on sustainability'],
}
class SalesAgent(Agent):
def execute_task(self, task_description):
if "Lead Generation" in task_description:
product = task_description.split("for ")[1]
leads = self.generate_leads(product)
for lead in leads:
self.kg.add_node("Lead", lead['id'], lead)
return f"Generated leads for {product}"
elif "Price Negotiation" in task_description:
product = task_description.split("for ")[1].split(" based")[0]
negotiation_result = self.negotiate_price(product)
self.kg.add_node("NegotiationResult", product, negotiation_result)
return f"Negotiated price for {product}"
elif "Deal Closing" in task_description:
product = task_description.split("for ")[1].split(" and")[0]
deal_result = self.close_deal(product)
self.kg.add_node("DealResult", product, deal_result)
self.update_sales_metrics(deal_result)
return f"Closed deal for {product}"
def generate_leads(self, product):
# This would typically involve analyzing customer data and market trends
# For now, we'll return dummy leads
return [
{'id': 'L001', 'name': 'Company A', 'interest_level': 'High'},
{'id': 'L002', 'name': 'Company B', 'interest_level': 'Medium'},
]
def negotiate_price(self, product):
# This would typically involve a back-and-forth process
# For now, we'll return a dummy negotiation result
return {'final_price': 89.99, 'discount': 0.1}
def close_deal(self, product):
# This would typically involve finalizing the sale
# For now, we'll return a dummy deal result
return {'product': product, 'quantity': 100, 'total_value': 8999}
def update_sales_metrics(self, deal_result):
# This would typically involve updating sales databases
# For now, we'll just print the result
print(f"Updated sales metrics: Sold {deal_result['quantity']} units of {deal_result['product']}")
class ASDSystem:
def __init__(self):
self.kg = KnowledgeGraph()
self.tm = TaskManager()
self.dme = DecisionMakingEngine()
self.agents = {
"Product": ProductAgent("Product", self.kg, self.tm, self.dme),
"Customer": CustomerAgent("Customer", self.kg, self.tm, self.dme),
"Market": MarketAgent("Market", self.kg, self.tm, self.dme),
"Sales": SalesAgent("Sales", self.kg, self.tm, self.dme)
}
def run(self):
while True:
task = self.tm.get_next_task()
if task is None:
break
agent_type, task_description = task
result = self.agents[agent_type].execute_task(task_description)
print(f"Task completed: {result}")
def collaborate(self, prompt):
if "Product-Price-Customer (PPC) Analysis" in prompt:
product = prompt.split("analyze ")[1].split(" features")[0]
pa_task = f"Pricing Analysis: Analyze prices of {product} across different online retailers"
ca_task = f"Sentiment Analysis: Analyze customer reviews for {product}"
ma_task = f"Market Research: Research market trends for {product.split()[0]}"
self.tm.add_task(1, "Product", pa_task)
self.tm.add_task(2, "Customer", ca_task)
self.tm.add_task(3, "Market", ma_task)
return f"Initiated PPC Analysis for {product}"
elif "Sales Strategy Development" in prompt:
product = prompt.split("for ")[1]
sa_task = f"Lead Generation: Generate leads for {product}"
ma_task = f"Competitor Monitoring: Monitor competitor prices and product offerings for {product}"
ca_task = f"Customer Profiling: Create customer profiles based on purchase history and provide personalized product recommendations"
self.tm.add_task(1, "Sales", sa_task)
self.tm.add_task(2, "Market", ma_task)
self.tm.add_task(3, "Customer", ca_task)
return f"Initiated Sales Strategy Development for {product}"
elif "Autonomous Decision-Making" in prompt:
product = prompt.split("for ")[1]
pa_task = f"Inventory Management: Update inventory levels for {product} and alert Sales Agent (SA) about stock availability"
sa_task = f"Price Negotiation: Negotiate prices with customers for {product} based on inventory levels and market trends"
dme_task = f"Pricing Decisions: Make autonomous pricing decisions for {product} based on market trends, competitor prices, and inventory levels"
self.tm.add_task(1, "Product", pa_task)
self.tm.add_task(2, "Sales", sa_task)
# Execute DME task immediately
product_info = self.kg.get_node_attributes(product)
market_trends = self.kg.get_node_attributes("global")['TrendAnalysis']
competitor_prices = self.kg.get_node_attributes(product.split()[0])['CompetitorInfo']
inventory_levels = 50 # Assuming this value from earlier dummy data
price_decision = self.dme.make_pricing_decision(product_info, market_trends, competitor_prices, inventory_levels)
print(f"Autonomous pricing decision for {product}: ${price_decision}")
return f"Made autonomous decisions for {product}"
# Usage example
asd_system = ASDSystem()
# Add some tasks
asd_system.tm.add_task(1, "Product", "Product Research: Research top-selling products in electronics")
asd_system.tm.add_task(2, "Customer", "Sentiment Analysis: Analyze customer reviews for iPhone 12")
asd_system.tm.add_task(3, "Market", "Market Research: Research market trends for smartphones")
asd_system.tm.add_task(4, "Sales", "Lead Generation: Generate leads for iPhone 12")
# Run the system
asd_system.run()
# Initiate collaborations
print(asd_system.collaborate("Product-Price-Customer (PPC) Analysis: Collaborate to analyze iPhone 12 features, pricing, and customer preferences"))
print(asd_system.collaborate("Sales Strategy Development: Develop sales strategies for iPhone 12 based on market trends, customer profiles, and competitor analysis"))
print(asd_system.collaborate("Autonomous Decision-Making: Make autonomous decisions on pricing, inventory management, and sales strategies for iPhone 12"))
# Price monitoring functionality