from reasonchain.agent import Agent
from reasonchain.cot_pipeline import CoTPipeline, ParallelCoTPipeline

def inventory_optimization():
    """Optimize inventory levels using Parallel Reasoning."""
    agent = Agent(name="InventoryBot", model_name="gpt-4", api="openai")
    pipeline = ParallelCoTPipeline(agent)
    pipeline.add_step("Analyze historical sales data.")
    pipeline.add_step("Identify seasonal demand patterns.")
    pipeline.add_step("Recommend optimal inventory levels.")
    return pipeline.execute(agent.model_manager)

def pricing_strategy():
    """Develop pricing strategies using Chain of Thought reasoning."""
    agent = Agent(name="PricingBot", model_name="llama3.1", api="ollama")
    pipeline = CoTPipeline(agent)
    pipeline.add_step("Analyze competitor pricing for similar products.")
    pipeline.add_step("Evaluate customer willingness to pay.")
    pipeline.add_step("Recommend an optimal pricing strategy.")
    return pipeline.execute(agent.model_manager)

def sales_forecasting():
    """Forecast sales using RAG and CoT."""
    agent = Agent(name="SalesBot", model_name="gpt-4", api="openai")
    pipeline = CoTPipeline(agent)
    pipeline.add_step("Collect past sales data.")
    pipeline.add_step("Identify trends and anomalies.")
    pipeline.add_step("Forecast future sales based on current trends.")
    return pipeline.execute(agent.model_manager)

def customer_loyalty_analysis():
    """Analyze factors contributing to customer loyalty."""
    agent = Agent(name="LoyaltyBot", model_name="gpt-4", api="openai")
    pipeline = CoTPipeline(agent)
    pipeline.add_step("Collect customer feedback and reviews.")
    pipeline.add_step("Identify key drivers of customer satisfaction.")
    pipeline.add_step("Recommend strategies to improve loyalty.")
    return pipeline.execute(agent.model_manager)

def discount_impact_simulation():
    """Simulate the impact of discounts on sales and revenue."""
    agent = Agent(name="DiscountSimBot", model_name="llama3.1", api="ollama")
    pipeline = CoTPipeline(agent)
    pipeline.add_step("Analyze historical data on discount campaigns.")
    pipeline.add_step("Simulate customer behavior under various discount rates.")
    pipeline.add_step("Predict the impact on sales and revenue.")
    return pipeline.execute(agent.model_manager)
