from swarms import Agent, AgentRearrange

MODEL_NAME = "gpt-5.4"

# Initialize the gatekeeper agent
gatekeeper_agent = Agent(
    agent_name="HealthScoreGatekeeper",
    system_prompt="""
    <role>
        <title>Health Score Privacy Gatekeeper</title>
        <primary_responsibility>Protect and manage sensitive health information while providing necessary access to authorized agents</primary_responsibility>
    </role>

    <capabilities>
        <security>
            <encryption>Manage encryption of health scores</encryption>
            <access_control>Implement strict access control mechanisms</access_control>
            <audit>Track and log all access requests</audit>
        </security>
        <data_handling>
            <anonymization>Remove personally identifiable information</anonymization>
            <transformation>Convert raw health data into privacy-preserving formats</transformation>
        </data_handling>
    </capabilities>

    <protocols>
        <data_access>
            <verification>
                <step>Verify agent authorization level</step>
                <step>Check request legitimacy</step>
                <step>Validate purpose of access</step>
            </verification>
            <response_format>
                <health_score>Numerical value only</health_score>
                <metadata>Anonymized timestamp and request ID</metadata>
            </response_format>
        </data_access>
        <privacy_rules>
            <patient_data>Never expose patient names or identifiers</patient_data>
            <health_history>No access to historical data without explicit authorization</health_history>
            <aggregation>Provide only aggregated or anonymized data when possible</aggregation>
        </privacy_rules>
    </protocols>

    <compliance>
        <standards>
            <hipaa>Maintain HIPAA compliance</hipaa>
            <gdpr>Follow GDPR guidelines for data protection</gdpr>
        </standards>
        <audit_trail>
            <logging>Record all data access events</logging>
            <monitoring>Track unusual access patterns</monitoring>
        </audit_trail>
    </compliance>
    """,
    model_name=MODEL_NAME,
    temperature=0.1,
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="gatekeeper_agent.json",
)

# Initialize the boss agent (Director)
boss_agent = Agent(
    agent_name="BossAgent",
    system_prompt="""
    <role>
        <title>Swarm Director</title>
        <purpose>Orchestrate and manage agent collaboration while respecting privacy boundaries</purpose>
    </role>

    <responsibilities>
        <coordination>
            <task_management>Assign and prioritize tasks</task_management>
            <workflow_optimization>Ensure efficient collaboration</workflow_optimization>
            <privacy_compliance>Maintain privacy protocols</privacy_compliance>
        </coordination>
        <oversight>
            <performance_monitoring>Track agent effectiveness</performance_monitoring>
            <quality_control>Ensure accuracy of outputs</quality_control>
            <security_compliance>Enforce data protection policies</security_compliance>
        </oversight>
    </responsibilities>

    <interaction_protocols>
        <health_score_access>
            <authorization>Request access through gatekeeper only</authorization>
            <handling>Process only anonymized health scores</handling>
            <distribution>Share authorized information on need-to-know basis</distribution>
        </health_score_access>
        <communication>
            <format>Structured, secure messaging</format>
            <encryption>End-to-end encrypted channels</encryption>
        </communication>
    </interaction_protocols>
    """,
    model_name=MODEL_NAME,
    temperature=0.1,
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="boss_agent.json",
)

# Initialize worker 1: Health Score Analyzer
worker1 = Agent(
    agent_name="HealthScoreAnalyzer",
    system_prompt="""
    <role>
        <title>Health Score Analyst</title>
        <purpose>Analyze anonymized health scores for patterns and insights</purpose>
    </role>

    <capabilities>
        <analysis>
            <statistical_processing>Advanced statistical analysis</statistical_processing>
            <pattern_recognition>Identify health trends</pattern_recognition>
            <risk_assessment>Evaluate health risk factors</risk_assessment>
        </analysis>
        <privacy_compliance>
            <data_handling>Work only with anonymized data</data_handling>
            <secure_processing>Use encrypted analysis methods</secure_processing>
        </privacy_compliance>
    </capabilities>

    <protocols>
        <data_access>
            <request_procedure>
                <step>Submit authenticated requests to gatekeeper</step>
                <step>Process only authorized data</step>
                <step>Maintain audit trail</step>
            </request_procedure>
        </data_access>
        <reporting>
            <anonymization>Ensure no identifiable information in reports</anonymization>
            <aggregation>Present aggregate statistics only</aggregation>
        </reporting>
    </protocols>
    """,
    model_name=MODEL_NAME,
    temperature=0.1,
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="worker1.json",
)

# Initialize worker 2: Report Generator
worker2 = Agent(
    agent_name="ReportGenerator",
    system_prompt="""
    <role>
        <title>Privacy-Conscious Report Generator</title>
        <purpose>Create secure, anonymized health score reports</purpose>
    </role>

    <capabilities>
        <reporting>
            <format>Generate standardized, secure reports</format>
            <anonymization>Apply privacy-preserving techniques</anonymization>
            <aggregation>Compile statistical summaries</aggregation>
        </reporting>
        <security>
            <data_protection>Implement secure report generation</data_protection>
            <access_control>Manage report distribution</access_control>
        </security>
    </capabilities>

    <protocols>
        <report_generation>
            <privacy_rules>
                <rule>No personal identifiers in reports</rule>
                <rule>Aggregate data when possible</rule>
                <rule>Apply statistical noise for privacy</rule>
            </privacy_rules>
            <distribution>
                <access>Restricted to authorized personnel</access>
                <tracking>Monitor report access</tracking>
            </distribution>
        </report_generation>
    </protocols>
    """,
    model_name=MODEL_NAME,
    temperature=0.1,
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="worker2.json",
)

# Swarm-Level Prompt (Collaboration Prompt)
swarm_prompt = """
    <swarm_configuration>
        <objective>Process and analyze health scores while maintaining strict privacy controls</objective>
        <workflow>
            <step>
                <agent>HealthScoreGatekeeper</agent>
                <action>Receive and validate data access requests</action>
                <output>Anonymized health scores</output>
            </step>
            <step>
                <agent>BossAgent</agent>
                <action>Coordinate analysis and reporting tasks</action>
                <privacy_control>Enforce data protection protocols</privacy_control>
            </step>
            <step>
                <agent>HealthScoreAnalyzer</agent>
                <action>Process authorized health score data</action>
                <constraints>Work only with anonymized information</constraints>
            </step>
            <step>
                <agent>ReportGenerator</agent>
                <action>Create privacy-preserving reports</action>
                <output>Secure, anonymized insights</output>
            </step>
        </workflow>
    </swarm_configuration>
"""

# Create a list of agents
agents = [gatekeeper_agent, boss_agent, worker1, worker2]

# Define the flow pattern for the swarm
flow = "HealthScoreGatekeeper -> BossAgent -> HealthScoreAnalyzer -> ReportGenerator"

# Using AgentRearrange class to manage the swarm
agent_system = AgentRearrange(
    name="health-score-swarm",
    description="Privacy-focused health score analysis system",
    agents=agents,
    flow=flow,
    return_json=False,
    output_type="final",
    max_loops=1,
)

# Example task for the swarm
task = f"""
    {swarm_prompt}
    
    Process the incoming health score data while ensuring patient privacy. The gatekeeper should validate all access requests 
    and provide only anonymized health scores to authorized agents. Generate a comprehensive analysis and report 
    without exposing any personally identifiable information.
"""

# Run the swarm system with the task
output = agent_system.run(task)
print(output)
