medicine-protocol.py

from praisonaiagents import Agent, Task, PraisonAIAgents
import time
from typing import Dict, List
import asyncio

def analyze_genetic_markers():
    """Simulates genetic marker analysis"""
    markers = {
        "metabolism_genes": {
            "CYP2D6": "rapid_metabolizer",
            "CYP2C19": "normal_metabolizer",
            "DPYD": "poor_metabolizer"
        },
        "risk_factors": {
            "cardiovascular": "elevated",
            "diabetes": "low",
            "autoimmune": "moderate"
        },
        "drug_response_markers": {
            "TPMT": "high_sensitivity",
            "HLA-B*5701": "negative",
            "UGT1A1": "normal"
        }
    }
    return markers

def evaluate_patient_history():
    """Simulates patient history evaluation"""
    history = {
        "conditions": [
            {"name": "hypertension", "duration": "5_years", "severity": "moderate"},
            {"name": "arthritis", "duration": "2_years", "severity": "mild"}
        ],
        "medications": [
            {"name": "lisinopril", "response": "good", "side_effects": "minimal"},
            {"name": "methotrexate", "response": "moderate", "side_effects": "mild"}
        ],
        "allergies": ["penicillin", "sulfa"],
        "family_history": ["diabetes", "heart_disease"]
    }
    return history

def analyze_drug_interactions(genetic_data: Dict, patient_history: Dict):
    """Analyzes potential drug interactions"""
    interactions = []
    medications = [med["name"] for med in patient_history["medications"]]
    
    for med in medications:
        interactions.append({
            "medication": med,
            "genetic_impact": "significant" if genetic_data["metabolism_genes"]["CYP2D6"] == "rapid_metabolizer" else "normal",
            "risk_level": "high" if med in patient_history["allergies"] else "low",
            "monitoring_needed": True if genetic_data["metabolism_genes"]["DPYD"] == "poor_metabolizer" else False
        })
    return interactions

def generate_protocol(genetic_data: Dict, interactions: List[Dict]):
    """Generates treatment protocol"""
    protocol = {
        "primary_treatment": {
            "medication": "custom_formulation_A",
            "dosage": "adjusted_based_on_metabolism",
            "frequency": "twice_daily",
            "monitoring": {
                "type": "blood_levels",
                "frequency": "weekly"
            }
        },
        "alternatives": [
            {
                "medication": "custom_formulation_B",
                "conditions": ["genetic_compatibility", "no_interactions"]
            }
        ],
        "precautions": []
    }
    
    if genetic_data["metabolism_genes"]["CYP2D6"] == "rapid_metabolizer":
        protocol["precautions"].append("increase_monitoring_frequency")
    
    if any(i["risk_level"] == "high" for i in interactions):
        protocol["precautions"].append("alternative_medication_needed")
    
    return protocol

def simulate_effectiveness(protocol: Dict):
    """Simulates treatment effectiveness"""
    simulation = {
        "efficacy_prediction": 0.85 + (time.time() % 2) / 10,
        "side_effect_risk": 0.15 + (time.time() % 1) / 10,
        "adherence_probability": 0.9 + (time.time() % 1) / 10,
        "expected_outcomes": [
            "symptom_reduction_80_percent",
            "minimal_side_effects",
            "good_tolerance"
        ]
    }
    return simulation

# Create specialized agents
genetic_analyzer = Agent(
    name="Genetic Analyzer",
    role="Genetic Analysis",
    goal="Analyze genetic markers",
    instructions="Evaluate genetic markers and variants",
    tools=[analyze_genetic_markers]
)

history_analyzer = Agent(
    name="History Analyzer",
    role="History Analysis",
    goal="Analyze patient history",
    instructions="Evaluate medical history and conditions",
    tools=[evaluate_patient_history]
)

interaction_analyzer = Agent(
    name="Interaction Analyzer",
    role="Drug Interaction",
    goal="Analyze drug interactions",
    instructions="Evaluate potential drug interactions",
    tools=[analyze_drug_interactions]
)

protocol_generator = Agent(
    name="Protocol Generator",
    role="Protocol Generation",
    goal="Generate treatment protocol",
    instructions="Create personalized treatment protocol",
    tools=[generate_protocol]
)

effectiveness_simulator = Agent(
    name="Effectiveness Simulator",
    role="Treatment Simulation",
    goal="Simulate treatment effectiveness",
    instructions="Predict treatment outcomes",
    tools=[simulate_effectiveness]
)

# Create workflow tasks
genetic_task = Task(
    name="analyze_genetics",
    description="Analyze genetic markers",
    expected_output="Genetic analysis",
    agent=genetic_analyzer,
    is_start=True,
    next_tasks=["analyze_history"]
)

history_task = Task(
    name="analyze_history",
    description="Analyze patient history",
    expected_output="History analysis",
    agent=history_analyzer,
    next_tasks=["analyze_interactions"]
)

interaction_task = Task(
    name="analyze_interactions",
    description="Analyze drug interactions",
    expected_output="Interaction analysis",
    agent=interaction_analyzer,
    context=[genetic_task, history_task],
    next_tasks=["generate_protocol"]
)

protocol_task = Task(
    name="generate_protocol",
    description="Generate treatment protocol",
    expected_output="Treatment protocol",
    agent=protocol_generator,
    context=[genetic_task, interaction_task],
    next_tasks=["simulate_effectiveness"]
)

simulation_task = Task(
    name="simulate_effectiveness",
    description="Simulate treatment effectiveness",
    expected_output="Effectiveness simulation",
    agent=effectiveness_simulator,
    task_type="decision",
    condition={
        "high": "",  # End workflow if high effectiveness
        "medium": ["generate_protocol"],  # Adjust protocol if medium
        "low": ["analyze_interactions"]  # Reanalyze if low
    }
)

# Create workflow
workflow = PraisonAIAgents(
    agents=[genetic_analyzer, history_analyzer, interaction_analyzer,
            protocol_generator, effectiveness_simulator],
    tasks=[genetic_task, history_task, interaction_task,
           protocol_task, simulation_task],
    process="workflow",
    verbose=True
)

async def main():
    print("\nStarting Personalized Medicine Protocol Generation...")
    print("=" * 50)
    
    # Run workflow
    results = await workflow.astart()
    
    # Print results
    print("\nProtocol Generation Results:")
    print("=" * 50)
    for task_id, result in results["task_results"].items():
        if result:
            print(f"\nTask: {task_id}")
            print(f"Result: {result.raw}")
            print("-" * 50)

if __name__ == "__main__":
    asyncio.run(main())