from agents import Agent, Runner
from evaluation import EvaluationFramework

# Initialize evaluation framework
evaluator = EvaluationFramework()

async def evaluate_agent_version(agent_id: str, version: str, 
                                 benchmark_id: str) -> dict:
    """Evaluate a specific agent version against a benchmark."""
    # Load agent
    agent = await load_agent(agent_id, version)
    
    # Load benchmark
    benchmark = evaluator.load_benchmark(benchmark_id)
    
    # Run evaluation
    results = await evaluator.run_benchmark(
        agent=agent,
        benchmark=benchmark,
        iterations=100,
        parallel=True
    )
    
    # Analyze results
    analysis = evaluator.analyze_results(results)
    
    # Store evaluation record
    evaluation_record = evaluator.store_evaluation(
        agent_id=agent_id,
        version=version,
        benchmark_id=benchmark_id,
        results=results,
        analysis=analysis
    )
    
    return evaluation_record

# Example evaluation
results = await evaluate_agent_version(
    agent_id="mood_mnky_001",
    version="v1.2.3",
    benchmark_id="benchmark_core_capabilities"
)

from agents import Agent, Runner
from training import AgentVersionManager

# Initialize version manager
version_manager = AgentVersionManager()

# Create new agent version
new_version = await version_manager.create_version(
    agent_id="mood_mnky_001",
    base_version="v1.2.3",
    changes=[
        {"type": "instruction_update", "content": "Improved product recommendation guidance"},
        {"type": "parameter_update", "name": "temperature", "value": 0.7},
        {"type": "tool_addition", "tool_id": "scent_analyzer"}
    ],
    metadata={
        "author": "ai_team_lead",
        "purpose": "Enhance recommendation quality",
        "ticket": "MNKY-452"
    }
)

# Load specific agent version
agent = await version_manager.load_version(
    agent_id="mood_mnky_001",
    version="v1.3.0"
)

# Run the agent
runner = Runner()
result = await runner.run(agent, "I'm looking for a gift for someone who loves relaxing baths")

from agents import Agent, Runner
from training import ABTestManager
import random

# Initialize A/B test manager
ab_test = ABTestManager()

# Create A/B test
test_id = await ab_test.create_test(
    name="product_recommendation_approach",
    variants={
        "control": "mood_mnky_001:v1.2.3",
        "variant_a": "mood_mnky_001:v1.3.0",
        "variant_b": "mood_mnky_001:v1.3.1"
    },
    allocation={
        "control": 0.4,
        "variant_a": 0.3,
        "variant_b": 0.3
    },
    metrics=[
        "conversion_rate",
        "satisfaction_score",
        "average_response_time"
    ],
    start_date="2024-05-15",
    end_date="2024-05-29"
)

async def get_ab_test_agent(user_id: str, test_id: str) -> Agent:
    """Get the appropriate agent variant for a user based on A/B test."""
    # Determine which variant to show this user
    variant = await ab_test.get_user_variant(user_id, test_id)
    
    # Record exposure to this variant
    await ab_test.record_exposure(test_id, user_id, variant)
    
    # Load the agent variant
    agent_id, version = variant.split(":")
    agent = await version_manager.load_version(agent_id, version)
    
    return agent

# Example usage
user_id = "usr_425b3c"
agent = await get_ab_test_agent(user_id, test_id)
runner = Runner()
result = await runner.run(agent, "What would you recommend for stress relief?")

# Record conversion if applicable
if user_added_product_to_cart:
    await ab_test.record_conversion(test_id, user_id)