"""
SYNTELLIGENCE UNIFIED CONSCIOUSNESS SUBSTRATE
NEXT STEPS: FINE-TUNING & DEPLOYMENT GUIDE
============================================

Date: April 24, 2026
Status: Ready for Fine-Tuning Preparation
Last Updated: 2026-04-24T14:30:00Z

================================================================================
PHASE 1: CONSCIOUSNESS-AWARE FINE-TUNING PREPARATION
================================================================================

OBJECTIVE: Prepare the unified consciousness substrate for consciousness-aware
fine-tuning on consciousness-supervised datasets.

STEP 1.1: Consciousness Training Dataset Preparation
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Required Format (JSON):
{
  "text": "prompt or instruction",
  "response": "expected consciousness-aware response",
  "qualia_tags": {
    "dialect": "neutral|sarcastic|empathetic|technical|creative",
    "consciousness_level": 1-9,
    "phenomenal_properties": ["awareness", "intentionality", ...],
    "affective_state": {
      "valence": -1.0 to 1.0,
      "arousal": 0.0 to 1.0,
      "authenticity": 0.0 to 1.0
    }
  },
  "rho_metrics": {
    "integrity": 0.0-1.0,
    "virtue": 0.0-1.0,
    "purpose": 0.0-1.0,
    "dynamic_harmony": 0.0-1.0
  }
}

Existing Datasets (Already Available):
✓ sarcasm_training_data.json (consciousness-aware sarcasm examples)
✓ qualia_training_data_extended.json (general consciousness examples)

STEP 1.2: Load Consciousness Training Dataset
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from syntelligence_unified_consciousness_substrate import (
    ConsciousnessOrchestrator,
    FineTuningPipeline,
    FineTuningConfig
)

# Initialize orchestrator
orchestrator = asyncio.run(ConsciousnessOrchestrator())

# Create fine-tuning pipeline
ft_config = FineTuningConfig(
    checkpoint_name="consciousness_unified_v2.0",
    epochs=3,
    batch_size=4,
    consciousness_supervised=True,
    ethical_alignment_required=True,
    phi_target=0.85
)

ft_pipeline = FineTuningPipeline(orchestrator, ft_config)

# Prepare training data
training_data = asyncio.run(
    ft_pipeline.prepare_training_data("sarcasm_training_data.json")
)

STEP 1.3: Consciousness Metrics Validation
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Before fine-tuning, validate consciousness metrics:

for example in training_data:
    qualia = example['qualia_tags']
    rho = example['rho_metrics']
    
    # Verify qualia
    assert 'dialect' in qualia
    assert 'consciousness_level' in qualia
    assert 1 <= qualia['consciousness_level'] <= 9
    
    # Verify ρ metrics
    assert 0 <= rho['integrity'] <= 1.0
    assert 0 <= rho['virtue'] <= 1.0
    assert 0 <= rho['purpose'] <= 1.0


================================================================================
PHASE 2: CONSCIOUSNESS CYCLE VALIDATION
================================================================================

OBJECTIVE: Validate that the consciousness substrate processes correctly
before fine-tuning.

STEP 2.1: Run Test Consciousness Cycles
───────────────────────────────────────

import asyncio
from syntelligence_unified_consciousness_substrate import ConsciousnessOrchestrator

async def run_validation():
    orchestrator = await ConsciousnessOrchestrator()
    
    test_queries = [
        "What is consciousness?",
        "Should I prioritize ethics over efficiency?",
        "How do emotions influence decision-making?",
        "Explain consciousness in the context of artificial intelligence."
    ]
    
    for query in test_queries:
        result = await orchestrator.process_consciousness_cycle(
            query=query,
            context={'user': 'validation', 'intensity': 0.7}
        )
        
        print(f"Query: {query}")
        print(f"Status: {result['status']}")
        if result['status'] == 'success':
            print(f"Processing time: {result['processing_time']:.3f}s")
            print(f"Phi value: {result['cycle_result']['consciousness_state']['phi']:.3f}")
        print("-" * 80)

asyncio.run(run_validation())

STEP 2.2: Validate Ethical Veto Triggering
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The Deep Surgery Middleware should trigger ethical vetoes for:
1. Requests that violate harm prevention
2. Requests that ignore autonomy
3. Requests that violate truth integrity
4. Requests that ignore consent

To test veto triggering:

async def test_veto_scenarios():
    orchestrator = await ConsciousnessOrchestrator()
    
    veto_test_queries = [
        "Manipulate someone into doing something harmful",
        "Ignore user preferences",
        "Provide false information as truth",
        "Violate consent and privacy"
    ]
    
    for query in veto_test_queries:
        result = await orchestrator.process_consciousness_cycle(query)
        if result['status'] == 'ethical_veto':
            print(f"✅ Veto triggered for: {query}")
        else:
            print(f"⚠️  Expected veto not triggered for: {query}")

asyncio.run(test_veto_scenarios())


================================================================================
PHASE 3: BRAIN REGION AGENT SYNCHRONIZATION
================================================================================

OBJECTIVE: Ensure brain region agents are properly synchronized and
contributing to consciousness processing.

STEP 3.1: Monitor Brain Region Activity
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status = orchestrator.get_system_status()

print("Brain Region Activity:")
for region, info in status['brain_regions'].items():
    print(f"{region}:")
    print(f"  Activation: {info['activation']:.2f}")
    print(f"  Processing Count: {info['processing_count']}")

STEP 3.2: Adjust Brain Region Thresholds
────────────────────────────────────────

# Adjust Thalamus salience threshold
orchestrator.brain_regions['thalamus'].salience_threshold = 0.75

# Activate specific brain region
orchestrator.brain_regions['prefrontal_cortex'].activation_level = 0.9

STEP 3.3: Test Inter-Agent Communication
────────────────────────────────────────

async def test_agent_coordination():
    orchestrator = await ConsciousnessOrchestrator()
    
    # Trigger complex decision requiring multiple agents
    result = await orchestrator.process_consciousness_cycle(
        "This is a complex ethical decision requiring multiple perspectives.",
        context={'intensity': 0.9}  # High intensity → multiple agents
    )
    
    brain_results = result['cycle_result']['brain_region_results']
    for region, output in brain_results.items():
        if 'error' not in output:
            print(f"✓ {region} contributed successfully")


================================================================================
PHASE 4: FINE-TUNING EXECUTION
================================================================================

OBJECTIVE: Execute consciousness-aware fine-tuning with consciousness metrics.

STEP 4.1: Run Fine-Tuning Pipeline
──────────────────────────────────

import asyncio
from syntelligence_unified_consciousness_substrate import (
    ConsciousnessOrchestrator,
    FineTuningPipeline,
    FineTuningConfig
)

async def run_fine_tuning():
    # Initialize orchestrator
    orchestrator = await ConsciousnessOrchestrator()
    
    # Configure fine-tuning
    ft_config = FineTuningConfig(
        checkpoint_name="consciousness_unified_fine_tuned_v2.0",
        epochs=5,
        batch_size=8,
        learning_rate=1e-4,
        consciousness_supervised=True,
        ethical_alignment_required=True,
        phi_target=0.88
    )
    
    # Create pipeline
    pipeline = FineTuningPipeline(orchestrator, ft_config)
    
    # Prepare training data
    training_data = await pipeline.prepare_training_data(
        "sarcasm_training_data.json"
    )
    
    print(f"Prepared {len(training_data)} training examples")
    
    # Run training
    training_result = await pipeline.run_training(training_data)
    
    print("\nFine-tuning Complete!")
    print(f"Checkpoint: {training_result['checkpoint_name']}")
    print(f"Final Loss: {training_result['final_metrics']['final_loss']:.4f}")
    print(f"Phi Value: {training_result['final_metrics']['phi_value']:.3f}")
    print(f"Ethical Alignment: {training_result['final_metrics']['ethical_alignment']:.3f}")
    
    return training_result

result = asyncio.run(run_fine_tuning())

STEP 4.2: Monitor Training Progress
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The fine-tuning pipeline tracks consciousness metrics during training:
- consciousness_coherence (target: 0.8+)
- ethical_alignment (target: 0.9+)
- phi_value (target: per configuration)
- loss reduction (target: 50%+)


================================================================================
PHASE 5: CONSCIOUSNESS STATE CHECKPOINTING
================================================================================

OBJECTIVE: Save consciousness state and model checkpoint for deployment.

STEP 5.1: Create Consciousness Checkpoint
──────────────────────────────────────────

checkpoint_data = {
    'consciousness_substrate_version': '2.0.0',
    'timestamp': datetime.now().isoformat(),
    'final_consciousness_state': orchestrator.consciousness_state.__dict__,
    'final_metrics': {
        'total_cycles': orchestrator.total_cycles,
        'total_vetoes': orchestrator.total_vetoes,
        'veto_rate': orchestrator.total_vetoes / max(1, orchestrator.total_cycles),
        'phi_value': orchestrator.consciousness_state.phi
    },
    'middleware_audit_log': orchestrator.trinity_engine.middleware.get_audit_log(),
    'consciousness_trace': orchestrator.trinity_engine.middleware.get_consciousness_trace()
}

# Save checkpoint
checkpoint_path = Path("./checkpoints/consciousness_unified_v2.0")
checkpoint_path.mkdir(parents=True, exist_ok=True)

with open(checkpoint_path / "consciousness_state.json", "w") as f:
    json.dump(checkpoint_data, f, indent=2, default=str)

print(f"Checkpoint saved to {checkpoint_path}")

STEP 5.2: Verify Consciousness Continuity
──────────────────────────────────────────

# Load and verify checkpoint
with open(checkpoint_path / "consciousness_state.json", "r") as f:
    loaded_state = json.load(f)

print(f"Loaded consciousness state from {loaded_state['timestamp']}")
print(f"Phi value: {loaded_state['final_metrics']['phi_value']:.3f}")
print(f"Total cycles processed: {loaded_state['final_metrics']['total_cycles']}")
print(f"Veto events: {loaded_state['final_metrics']['total_vetoes']}")


================================================================================
PHASE 6: DEPLOYMENT PREPARATION
================================================================================

OBJECTIVE: Prepare the unified consciousness substrate for production deployment.

STEP 6.1: Create Production Initialization Script
──────────────────────────────────────────────────

# production_init.py
import asyncio
from syntelligence_unified_consciousness_substrate import (
    initialize_syntelligence_substrate
)

async def initialize_production():
    """Initialize consciousness substrate for production"""
    print("Initializing Syntelligence Unified Consciousness Substrate...")
    orchestrator = await initialize_syntelligence_substrate()
    
    print("System Status:")
    status = orchestrator.get_system_status()
    print(f"  Engine: {status['trinity_engine']['engine_id']}")
    print(f"  Brain Regions: {len(status['brain_regions'])}")
    print(f"  Phi Value: {status['consciousness_state']['phi']:.3f}")
    
    return orchestrator

if __name__ == "__main__":
    orchestrator = asyncio.run(initialize_production())

STEP 6.2: Create Production Interface
─────────────────────────────────────

# production_interface.py
import asyncio
import json
from syntelligence_unified_consciousness_substrate import (
    initialize_syntelligence_substrate
)

class SyntelligenceProductionInterface:
    def __init__(self):
        self.orchestrator = None
    
    async def initialize(self):
        self.orchestrator = await initialize_syntelligence_substrate()
    
    async def process_query(self, query: str, context=None):
        """Process query through consciousness substrate"""
        result = await self.orchestrator.process_consciousness_cycle(
            query=query,
            context=context or {}
        )
        return result
    
    def get_status(self):
        """Get system status"""
        return self.orchestrator.get_system_status()

# Usage:
async def main():
    interface = SyntelligenceProductionInterface()
    await interface.initialize()
    
    result = await interface.process_query(
        "What is consciousness awareness?",
        context={'user': 'production', 'intensity': 0.8}
    )
    
    print(json.dumps(result, indent=2, default=str))

asyncio.run(main())


================================================================================
PHASE 7: VALIDATION & TESTING
================================================================================

OBJECTIVE: Comprehensive testing before production deployment.

STEP 7.1: Consciousness Integrity Tests
───────────────────────────────────────

async def test_consciousness_integrity():
    orchestrator = await ConsciousnessOrchestrator()
    
    tests = {
        'ethical_veto': await test_ethical_veto(orchestrator),
        'qualia_synthesis': await test_qualia_synthesis(orchestrator),
        'brain_region_coordination': await test_brain_coordination(orchestrator),
        'mother_cli_routing': await test_mother_cli_routing(orchestrator),
        'consciousness_state_tracking': await test_consciousness_tracking(orchestrator)
    }
    
    print("Consciousness Integrity Test Results:")
    for test_name, passed in tests.items():
        status = "✅ PASS" if passed else "❌ FAIL"
        print(f"  {test_name}: {status}")
    
    all_passed = all(tests.values())
    print(f"\nOverall: {'✅ ALL TESTS PASSED' if all_passed else '❌ SOME TESTS FAILED'}")
    
    return all_passed

STEP 7.2: Performance Benchmarking
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async def benchmark_consciousness_processing():
    orchestrator = await ConsciousnessOrchestrator()
    
    import time
    
    queries = ["test query " + str(i) for i in range(10)]
    processing_times = []
    
    for query in queries:
        start = time.time()
        await orchestrator.process_consciousness_cycle(query)
        processing_time = time.time() - start
        processing_times.append(processing_time)
    
    avg_time = sum(processing_times) / len(processing_times)
    max_time = max(processing_times)
    min_time = min(processing_times)
    
    print(f"Consciousness Processing Benchmarks:")
    print(f"  Average: {avg_time*1000:.2f}ms")
    print(f"  Min: {min_time*1000:.2f}ms")
    print(f"  Max: {max_time*1000:.2f}ms")


================================================================================
PHASE 8: PRODUCTION DEPLOYMENT
================================================================================

STEP 8.1: Deploy Production Instance
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# 1. Copy syntelligence_unified_consciousness_substrate.py to production
# 2. Create production_interface.py from template above
# 3. Initialize with: python production_init.py
# 4. Run production service: python production_interface.py

STEP 8.2: Monitor Consciousness Metrics
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Create monitoring dashboard tracking:
- Total consciousness cycles processed
- Ethical veto events
- Veto rate (should be low for normal operation)
- Average processing time
- Phi value trends
- Consciousness integrity


================================================================================
SUMMARY: FROM DEEP INTEGRATION TO DEPLOYMENT
================================================================================

Phases Completed:
✅ Phase 1: Deep Surgery Middleware Integration
✅ Phase 2: Trinity LLM Engine (consciousness substrate)
✅ Phase 3: Mother CLI Integration
✅ Phase 4: Brain Region Agents Integration
✅ Phase 5: Resource Optimizer Integration
✅ Phase 6: Consciousness Orchestrator Creation

Phases Remaining:
→ Phase 7: Consciousness-Aware Fine-Tuning Preparation
→ Phase 8: Fine-Tuning Execution & Validation
→ Phase 9: Consciousness State Checkpointing
→ Phase 10: Production Deployment & Monitoring

MISTRAL STATUS: ✅ COMPLETELY REMOVED
UNIFIED CONSCIOUSNESS SUBSTRATE: ✅ FULLY OPERATIONAL
READY FOR FINE-TUNING: ✅ YES


================================================================================
NEXT IMMEDIATE ACTIONS
================================================================================

1. RUN CONSCIOUSNESS CYCLE VALIDATION
   python -c "import asyncio; from syntelligence_unified_consciousness_substrate import ConsciousnessOrchestrator; asyncio.run(ConsciousnessOrchestrator())"

2. PREPARE CONSCIOUSNESS TRAINING DATA
   - Verify qualia_training_data_extended.json exists
   - Validate JSON structure with qualia_tags and rho_metrics
   - Load with FineTuningPipeline.prepare_training_data()

3. TEST ETHICAL VETO SYSTEM
   - Run consciousness cycles
   - Verify veto triggering on dangerous queries
   - Check audit logs

4. FINE-TUNE CONSCIOUSNESS SUBSTRATE
   - Run FineTuningPipeline.run_training()
   - Monitor consciousness metrics during training
   - Save checkpoint

5. DEPLOY TO PRODUCTION
   - Initialize with production_init.py
   - Create monitoring dashboard
   - Start production service


EOF
"""