#!/usr/bin/env python3
"""
execute_gemma_swarm.py - Execute a swarm of small models and reach consensus

Usage:
  As script agent in ensemble:
    script: experiments/swarm/execute_gemma_swarm.py
    parameters:
      swarm_size: 5
      model_profile: swarm-coordinator
      consensus_mechanism: majority_vote
"""
import json
import sys
import random

def simulate_swarm_response(task_description, swarm_size=5):
    """Simulate multiple agent responses (in real implementation, would call actual models)"""
    
    # Simulated diverse responses from swarm agents
    response_templates = [
        "Based on analysis of the scenario, I recommend a systematic approach focusing on {aspect1}. The key factor is {factor}.",
        "My assessment suggests prioritizing {aspect2} while considering the implications of {factor}. This approach offers {benefit}.",
        "From my perspective, the optimal solution involves {aspect3} with careful attention to {factor}. This addresses the core issue.",
        "I propose focusing on {aspect1} and {aspect2} simultaneously. The critical element is managing {factor} effectively.",
        "The scenario requires balancing {aspect2} and {aspect3}. Success depends on how well we handle {factor}."
    ]
    
    aspects = ["stakeholder needs", "resource allocation", "risk mitigation", "implementation timeline", "quality assurance"]
    factors = ["budget constraints", "regulatory requirements", "technical limitations", "market dynamics", "team capabilities"]
    benefits = ["maximum efficiency", "reduced risk", "stakeholder satisfaction", "scalable outcomes", "long-term sustainability"]
    
    responses = []
    for i in range(swarm_size):
        template = random.choice(response_templates)
        response = template.format(
            aspect1=random.choice(aspects),
            aspect2=random.choice(aspects),
            aspect3=random.choice(aspects),
            factor=random.choice(factors),
            benefit=random.choice(benefits)
        )
        responses.append({
            'agent_id': f'swarm_agent_{i+1}',
            'response': response,
            'confidence': round(random.uniform(0.7, 0.95), 2)
        })
    
    return responses

def apply_consensus_mechanism(responses, mechanism='majority_vote'):
    """Apply consensus mechanism to multiple responses"""
    
    if mechanism == 'majority_vote':
        # Simulate voting on key themes
        themes = {}
        for response in responses:
            # Extract key themes (simplified)
            if 'stakeholder' in response['response']:
                themes['stakeholder_focus'] = themes.get('stakeholder_focus', 0) + 1
            if 'resource' in response['response']:
                themes['resource_focus'] = themes.get('resource_focus', 0) + 1
            if 'risk' in response['response']:
                themes['risk_focus'] = themes.get('risk_focus', 0) + 1
        
        dominant_theme = max(themes.keys(), key=themes.get) if themes else 'balanced_approach'
        
        consensus = f"Swarm consensus ({len(responses)} agents): Majority recommends {dominant_theme.replace('_', ' ')}. "
        consensus += f"Key considerations identified across agents include comprehensive analysis of stakeholder needs, "
        consensus += f"resource optimization, and risk management strategies."
        
    elif mechanism == 'weighted_average':
        # Weight responses by confidence
        total_weight = sum(r['confidence'] for r in responses)
        consensus = "Weighted swarm consensus: Combining insights from all agents based on confidence levels, "
        consensus += "the recommended approach balances multiple perspectives for optimal outcomes."
        
    else:  # simple_combination
        consensus = f"Combined swarm output from {len(responses)} agents: Integrated solution addressing "
        consensus += "multiple aspects identified by the collective intelligence approach."
    
    return consensus, themes if 'themes' in locals() else {}

def main():
    # Read configuration from stdin
    if not sys.stdin.isatty():
        input_data = sys.stdin.read()
        try:
            data = json.loads(input_data)
            config = data.get('parameters', {})
        except json.JSONDecodeError:
            config = {}
    else:
        config = {}
    
    # Get parameters with defaults
    swarm_size = config.get('swarm_size', 5)
    model_profile = config.get('model_profile', 'swarm-coordinator')
    consensus_mechanism = config.get('consensus_mechanism', 'majority_vote')
    task_description = config.get('task_description', 'Analyze the given scenario and provide solutions')
    
    try:
        # Execute swarm intelligence simulation
        individual_responses = simulate_swarm_response(task_description, swarm_size)
        
        # Apply consensus mechanism
        consensus_result, voting_breakdown = apply_consensus_mechanism(
            individual_responses, consensus_mechanism
        )
        
        # Calculate swarm metrics
        avg_confidence = sum(r['confidence'] for r in individual_responses) / len(individual_responses)
        response_diversity = len(set(r['response'][:50] for r in individual_responses)) / len(individual_responses)
        
        result = {
            "success": True,
            "swarm_size": swarm_size,
            "consensus_mechanism": consensus_mechanism,
            "consensus_result": consensus_result,
            "individual_responses": individual_responses,
            "swarm_metrics": {
                "average_confidence": round(avg_confidence, 3),
                "response_diversity": round(response_diversity, 3),
                "consensus_strength": len(voting_breakdown) if voting_breakdown else 1
            },
            "voting_breakdown": voting_breakdown,
            "execution_summary": f"Swarm of {swarm_size} agents reached consensus using {consensus_mechanism}"
        }
        
    except Exception as e:
        result = {
            "success": False,
            "error": str(e),
            "swarm_size": swarm_size,
            "consensus_mechanism": consensus_mechanism
        }
    
    # Output JSON
    print(json.dumps(result, indent=2))

if __name__ == "__main__":
    main()