from praisonaiagents import Agent, Task, AgentTeam
import time
from typing import Dict

def assess_student_level():
    """Simulates student assessment"""
    levels = ["beginner", "intermediate", "advanced"]
    current_time = int(time.time())
    return levels[current_time % 3]

def generate_content(level: str):
    """Simulates content generation"""
    content_types = {
        "beginner": "basic concepts and examples",
        "intermediate": "practice problems and applications",
        "advanced": "complex scenarios and projects"
    }
    return content_types.get(level, "basic concepts")

def evaluate_performance():
    """Simulates performance evaluation"""
    scores = ["low", "medium", "high"]
    current_time = int(time.time())
    return scores[current_time % 3]

def adapt_difficulty(performance: str):
    """Simulates difficulty adaptation"""
    adaptations = {
        "low": "decrease",
        "medium": "maintain",
        "high": "increase"
    }
    return adaptations.get(performance, "maintain")

# Create specialized agents
assessor = Agent(
    name="Student Assessor",
    role="Level Assessment",
    goal="Assess student's current level",
    instructions="Evaluate student's knowledge and skills",
    tools=[assess_student_level]
)

generator = Agent(
    name="Content Generator",
    role="Content Creation",
    goal="Generate appropriate learning content",
    instructions="Create content based on student's level",
    tools=[generate_content]
)

evaluator = Agent(
    name="Performance Evaluator",
    role="Performance Assessment",
    goal="Evaluate student's performance",
    instructions="Assess learning outcomes",
    tools=[evaluate_performance]
)

adapter = Agent(
    name="Difficulty Adapter",
    role="Content Adaptation",
    goal="Adapt content difficulty",
    instructions="Adjust difficulty based on performance",
    tools=[adapt_difficulty]
)

# Create workflow tasks
assessment_task = Task(
    name="assess_level",
    description="Assess student's current level",
    expected_output="Student's proficiency level",
    agent=assessor,
    is_start=True,
    next_tasks=["generate_content"]
)

generation_task = Task(
    name="generate_content",
    description="Generate appropriate content",
    expected_output="Learning content",
    agent=generator,
    next_tasks=["evaluate_performance"]
)

evaluation_task = Task(
    name="evaluate_performance",
    description="Evaluate student's performance",
    expected_output="Performance assessment",
    agent=evaluator,
    next_tasks=["adapt_difficulty"]
)

adaptation_task = Task(
    name="adapt_difficulty",
    description="Adapt content difficulty",
    expected_output="Difficulty adjustment",
    agent=adapter,
    task_type="decision",
    condition={
        "decrease": ["generate_content"],
        "maintain": "",
        "increase": ["generate_content"]
    }
)

# Create workflow
workflow = AgentTeam(
    agents=[assessor, generator, evaluator, adapter],
    tasks=[assessment_task, generation_task, evaluation_task, adaptation_task],
    process="workflow", output="verbose"
)

def main():
    print("\nStarting Adaptive Learning Workflow...")
    print("=" * 50)
    
    # Run workflow
    results = workflow.start()
    
    # Print results
    print("\nAdaptive Learning Results:")
    print("=" * 50)
    task_results = (
        results.get("task_results", {})
        if isinstance(results, dict)
        else {}
    )
    if not task_results and isinstance(results, str):
        print(f"\nWorkflow output:\n{results}")
    for task_id, result in task_results.items():
        if result:
            print(f"\nTask: {task_id}")
            print(f"Result: {result.raw}")
            print("-" * 50)

if __name__ == "__main__":
    main()