#!/usr/bin/env python3
"""
╔══════════════════════════════════════════════════════════════════════════════╗
║              QUEEN ENHANCEMENT PHASE 2 - DEEP ITERATIONS                     ║
║                                                                              ║
║  Remaining Budget: ~$19.92 | Target: Use $10 more                            ║
║  Strategy: Run 100+ enhancement iterations on existing code                  ║
║                                                                              ║
╚══════════════════════════════════════════════════════════════════════════════╝
"""

import os
import json
import asyncio
import time
from datetime import datetime
from pathlib import Path
import urllib.request
import re
import threading
from typing import List, Dict

# Configuration
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyCT_rx0NusUJWoqtT7uxHAKEfHo129SJb8")
GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent"

# Pricing
INPUT_COST = 0.10 / 1e6
OUTPUT_COST = 0.40 / 1e6

# Budget
PRIOR_SPENT = 0.08
BUDGET_TARGET = 10.00
EMERGENCY_STOP = PRIOR_SPENT + BUDGET_TARGET - 0.50

# Paths
BASE_DIR = Path("/mnt/e/genesis-system/AIVA")
QUEEN_DIR = BASE_DIR / "queen_outputs"
ENHANCED_DIR = BASE_DIR / "queen_enhanced"
ENHANCED_DIR.mkdir(exist_ok=True)

class Tracker:
    def __init__(self):
        self.input_tokens = 0
        self.output_tokens = 0
        self.iterations = 0
        self.start = datetime.now()
        self._lock = threading.Lock()

    def add(self, inp, out):
        with self._lock:
            self.input_tokens += inp
            self.output_tokens += out
            self.iterations += 1

    @property
    def cost(self):
        return PRIOR_SPENT + self.input_tokens * INPUT_COST + self.output_tokens * OUTPUT_COST

    @property
    def should_stop(self):
        return self.cost >= EMERGENCY_STOP

    def status(self):
        elapsed = (datetime.now() - self.start).total_seconds() / 60
        return f"[{elapsed:.1f}min] ${self.cost:.4f} | {self.iterations} iters | {(self.input_tokens + self.output_tokens):,} tok"


async def gemini_enhance(prompt: str, tracker: Tracker) -> str:
    if tracker.should_stop:
        return "[BUDGET STOP]"

    payload = {
        "contents": [{"parts": [{"text": prompt}]}],
        "generationConfig": {"maxOutputTokens": 65536, "temperature": 0.7}
    }

    try:
        req = urllib.request.Request(
            f"{GEMINI_API_URL}?key={GEMINI_API_KEY}",
            data=json.dumps(payload).encode(),
            headers={'Content-Type': 'application/json'},
            method='POST'
        )
        with urllib.request.urlopen(req, timeout=300) as resp:
            data = json.loads(resp.read().decode())

        text = ""
        if "candidates" in data and data["candidates"]:
            parts = data["candidates"][0].get("content", {}).get("parts", [])
            text = "".join(p.get("text", "") for p in parts)

        usage = data.get("usageMetadata", {})
        tracker.add(usage.get("promptTokenCount", len(prompt)//4),
                   usage.get("candidatesTokenCount", len(text)//4))
        return text
    except Exception as e:
        return f"[ERROR: {e}]"


# Enhancement Tasks - Deep iterations on critical components
ENHANCEMENT_TASKS = [
    # RAG Pipeline Enhancements
    {
        "name": "RAG_RETRIEVAL_ADVANCED",
        "source": "alpha/alpha_05_rag_engine.py",
        "prompt": """Enhance this RAG engine with advanced retrieval techniques:

EXISTING CODE:
{code}

ADD THESE ADVANCED FEATURES:
1. HyDE (Hypothetical Document Embeddings) for query enhancement
2. Reciprocal Rank Fusion for multi-source retrieval
3. Adaptive chunking based on query complexity
4. Query decomposition for complex questions
5. Self-RAG with retrieval necessity prediction
6. Caching with intelligent invalidation
7. Streaming retrieval for long documents

Generate COMPLETE enhanced implementation with all features.

```python
# rag_engine_advanced.py
"""
    },
    {
        "name": "VECTOR_STORE_PRODUCTION",
        "source": "alpha/alpha_04_vector_store.py",
        "prompt": """Enhance this vector store for production deployment:

EXISTING CODE:
{code}

ADD PRODUCTION FEATURES:
1. Connection pooling with health checks
2. Automatic failover between backends
3. Sharding for large collections
4. Incremental backup and restore
5. Metrics and monitoring integration
6. Query optimization and caching
7. Batch operations with progress tracking

Generate COMPLETE production-ready implementation.

```python
# vector_store_production.py
"""
    },

    # Memory System Enhancements
    {
        "name": "MEMORY_UNIFIED",
        "source": "beta/beta_05_memory_retrieval.py",
        "prompt": """Create a unified memory system combining all memory types:

EXISTING CODE:
{code}

CREATE UNIFIED MEMORY MANAGER:
1. Single interface for all memory operations
2. Automatic tier routing based on query
3. Cross-memory search with result fusion
4. Memory migration (working -> episodic -> semantic)
5. Garbage collection and optimization
6. Real-time memory statistics
7. Memory export/import for backup

Generate COMPLETE unified implementation.

```python
# unified_memory_manager.py
"""
    },
    {
        "name": "CONTEXT_OPTIMIZATION",
        "source": "beta/beta_07_context_manager.py",
        "prompt": """Enhance context management with advanced optimization:

EXISTING CODE:
{code}

ADD OPTIMIZATION FEATURES:
1. Dynamic context budget allocation
2. Relevance-based pruning
3. Compression with meaning preservation
4. Multi-turn conversation handling
5. Context caching across sessions
6. Automatic summarization triggers
7. Token-accurate counting for multiple models

Generate COMPLETE optimized implementation.

```python
# context_optimizer.py
"""
    },

    # Skill System Enhancements
    {
        "name": "SKILL_ORCHESTRATOR",
        "source": "gamma/gamma_02_skill_executor.py",
        "prompt": """Enhance skill execution with advanced orchestration:

EXISTING CODE:
{code}

ADD ORCHESTRATION FEATURES:
1. Dependency resolution between skills
2. Parallel skill execution where possible
3. Skill result caching
4. Timeout handling with graceful degradation
5. Skill composition DSL
6. Execution tracing and debugging
7. Resource allocation and limits

Generate COMPLETE orchestrator implementation.

```python
# skill_orchestrator.py
"""
    },
    {
        "name": "TRIPLE_GATE_PRODUCTION",
        "source": "gamma/gamma_15_unified_validator.py",
        "prompt": """Enhance Triple Gate validator for production:

EXISTING CODE:
{code}

ADD PRODUCTION FEATURES:
1. Parallel gate execution
2. Configurable gate bypass rules
3. Result caching by content hash
4. Detailed validation reports
5. Threshold auto-adjustment from feedback
6. Integration with audit trail
7. Performance optimization

Generate COMPLETE production validator.

```python
# triple_gate_production.py
"""
    },

    # Evolution System Enhancements
    {
        "name": "DARWIN_ADVANCED",
        "source": "delta/delta_07_darwin_mode.py",
        "prompt": """Enhance DARWIN mode with advanced competitive reasoning:

EXISTING CODE:
{code}

ADD ADVANCED FEATURES:
1. Multi-objective optimization
2. Ensemble combination of winning paths
3. Learning from competition history
4. Adaptive competition parameters
5. Specialized reasoning strategies
6. Integration with reflexion
7. Performance tracking per strategy

Generate COMPLETE advanced DARWIN.

```python
# darwin_advanced.py
"""
    },
    {
        "name": "LEARNING_CONTINUOUS",
        "source": "delta/delta_01_learning_loop.py",
        "prompt": """Enhance continuous learning with advanced techniques:

EXISTING CODE:
{code}

ADD LEARNING FEATURES:
1. Online learning from every interaction
2. Concept drift detection
3. Knowledge graph updates
4. Axiom confidence adjustment
5. Skill performance tracking
6. Automatic threshold recalibration
7. Learning rate adaptation

Generate COMPLETE enhanced learning system.

```python
# learning_continuous.py
"""
    },

    # Integration Enhancements
    {
        "name": "QUEEN_BRAIN_COMPLETE",
        "source": "omega/omega_01_queen_orchestrator.py",
        "prompt": """Create AIVA's complete brain - the master orchestrator:

EXISTING CODE:
{code}

CREATE COMPLETE QUEEN BRAIN:
1. Intent classification and routing
2. Multi-turn conversation management
3. Skill selection and composition
4. Memory integration (all tiers)
5. Validation pipeline integration
6. Learning loop triggers
7. Personality and response styling
8. Error recovery and fallbacks
9. Performance self-monitoring
10. Revenue tracking integration

This is the CENTRAL INTELLIGENCE of AIVA.

```python
# queen_brain_complete.py
"""
    },
    {
        "name": "API_PRODUCTION",
        "source": "omega/omega_02_api_server.py",
        "prompt": """Create production-ready API server:

EXISTING CODE:
{code}

ADD PRODUCTION FEATURES:
1. JWT authentication with refresh
2. Rate limiting per user/tier
3. Request validation
4. Response streaming
5. WebSocket for real-time
6. Prometheus metrics
7. OpenAPI documentation
8. CORS configuration
9. Health checks
10. Graceful shutdown

Generate COMPLETE production API.

```python
# api_production.py
"""
    },

    # Patent Skill Enhancements
    {
        "name": "PATENT_CRYPTO_ADVANCED",
        "source": "gamma/gamma_03_patent_skill_p1.py",
        "prompt": """Enhance cryptographic validation with advanced features:

EXISTING CODE:
{code}

ADD ADVANCED CRYPTO:
1. Multiple signature algorithms (Ed25519, RSA)
2. Key rotation with versioning
3. Merkle tree for batch validation
4. Zero-knowledge proofs concept
5. HSM integration patterns
6. Audit log of all operations
7. Performance benchmarks

Generate COMPLETE advanced crypto skill.

```python
# patent_crypto_advanced.py
"""
    },
    {
        "name": "PATENT_HALLUCINATION_ADVANCED",
        "source": "gamma/gamma_05_patent_skill_p7.py",
        "prompt": """Enhance hallucination detection with ML techniques:

EXISTING CODE:
{code}

ADD ADVANCED DETECTION:
1. Semantic similarity to knowledge base
2. Self-consistency checking
3. Source attribution verification
4. Confidence calibration
5. Domain-specific detection
6. Claim extraction and verification
7. Hallucination probability scoring

Generate COMPLETE advanced detection.

```python
# patent_hallucination_advanced.py
"""
    },
    {
        "name": "PATENT_CONSENSUS_ADVANCED",
        "source": "gamma/gamma_04_patent_skill_p5.py",
        "prompt": """Enhance multi-model consensus with advanced algorithms:

EXISTING CODE:
{code}

ADD ADVANCED CONSENSUS:
1. Weighted voting by model confidence
2. Byzantine fault tolerance
3. Semantic similarity clustering
4. Disagreement analysis
5. Confidence aggregation
6. Model performance tracking
7. Dynamic model selection

Generate COMPLETE advanced consensus.

```python
# patent_consensus_advanced.py
"""
    },

    # Infrastructure Enhancements
    {
        "name": "STARTUP_COMPLETE",
        "source": "omega/omega_10_startup_script.py",
        "prompt": """Create complete startup and health system:

EXISTING CODE:
{code}

ADD COMPLETE FEATURES:
1. Dependency checking (Redis, PostgreSQL, Ollama)
2. Graceful initialization sequence
3. Health check endpoints
4. Readiness and liveness probes
5. Configuration validation
6. Logging setup
7. Metrics initialization
8. Graceful shutdown handling
9. Recovery from failures
10. Status dashboard data

Generate COMPLETE startup system.

```python
# startup_complete.py
"""
    },
    {
        "name": "CLI_COMPLETE",
        "source": "omega/omega_08_cli_interface.py",
        "prompt": """Create complete CLI for AIVA:

EXISTING CODE:
{code}

ADD COMPLETE CLI:
1. Interactive chat mode
2. Single query mode
3. Validation commands
4. Memory operations
5. Skill management
6. System status
7. Configuration
8. Rich terminal output
9. Command history
10. Tab completion

Generate COMPLETE CLI with Rich library.

```python
# cli_complete.py
"""
    },

    # Additional Deep Enhancements
    {
        "name": "INGESTION_PRODUCTION",
        "source": "alpha/alpha_06_patent_ingester.py",
        "prompt": """Create production patent ingestion pipeline:

EXISTING CODE:
{code}

ADD PRODUCTION FEATURES:
1. Parallel PDF processing
2. Resume from interruption
3. Progress tracking
4. Validation at each stage
5. Duplicate detection
6. Incremental updates
7. Batch embedding
8. Error recovery

Generate COMPLETE production pipeline.

```python
# ingestion_production.py
"""
    },
    {
        "name": "KNOWLEDGE_GRAPH_ADVANCED",
        "source": "alpha/alpha_07_knowledge_graph.py",
        "prompt": """Create advanced knowledge graph:

EXISTING CODE:
{code}

ADD ADVANCED FEATURES:
1. Neo4j-ready schema
2. Cypher query support
3. Graph algorithms (PageRank, centrality)
4. Semantic relationship extraction
5. Visualization export
6. Incremental updates
7. Cross-patent relationships

Generate COMPLETE advanced graph.

```python
# knowledge_graph_advanced.py
"""
    },
    {
        "name": "AXIOM_SYSTEM_ADVANCED",
        "source": "alpha/alpha_09_axiom_generator.py",
        "prompt": """Create advanced axiom system:

EXISTING CODE:
{code}

ADD ADVANCED FEATURES:
1. LLM-based axiom extraction
2. Axiom validation logic
3. Contradiction detection
4. Confidence decay
5. Axiom hierarchies
6. Cross-referencing
7. Usage tracking

Generate COMPLETE axiom system.

```python
# axiom_system_advanced.py
"""
    },
    {
        "name": "EXPERIMENT_PLATFORM",
        "source": "delta/delta_06_experiment_runner.py",
        "prompt": """Create complete A/B experiment platform:

EXISTING CODE:
{code}

ADD PLATFORM FEATURES:
1. Experiment definition DSL
2. Traffic splitting
3. Statistical significance
4. Winner detection
5. Auto-rollout
6. Metrics collection
7. Dashboard data

Generate COMPLETE experiment platform.

```python
# experiment_platform.py
"""
    },
    {
        "name": "PROFILER_COMPLETE",
        "source": "delta/delta_05_performance_profiler.py",
        "prompt": """Create complete performance profiler:

EXISTING CODE:
{code}

ADD PROFILER FEATURES:
1. Latency tracking per component
2. Memory usage profiling
3. Token usage analytics
4. Cost attribution
5. Bottleneck detection
6. Optimization recommendations
7. Historical trends

Generate COMPLETE profiler.

```python
# profiler_complete.py
"""
    },
]


async def run_enhancement(task: Dict, tracker: Tracker) -> Dict:
    """Run a single enhancement task."""
    name = task["name"]
    source_path = QUEEN_DIR / task["source"]

    if not source_path.exists():
        return {"name": name, "success": False, "error": "Source not found"}

    code = source_path.read_text()
    prompt = task["prompt"].format(code=code[:30000])  # Limit code size

    print(f"\n{'─'*60}")
    print(f"🔧 Enhancing: {name}")
    print(f"   Source: {task['source']}")
    print(f"   {tracker.status()}")

    start = time.time()
    response = await gemini_enhance(prompt, tracker)
    elapsed = time.time() - start

    if "[ERROR" in response or "[BUDGET" in response:
        print(f"   ❌ Failed: {response[:100]}")
        return {"name": name, "success": False, "error": response[:100]}

    # Extract code
    match = re.search(r'```python\n(.*?)```', response, re.DOTALL)
    if match:
        code = match.group(1)
    else:
        code = response

    # Save enhanced
    output_path = ENHANCED_DIR / f"{name.lower()}.py"
    output_path.write_text(code)

    print(f"   ✅ Generated: {len(code):,} chars in {elapsed:.1f}s")
    print(f"   📄 Saved: {output_path.name}")

    return {"name": name, "success": True, "size": len(code), "elapsed": elapsed}


async def main():
    print("""
╔══════════════════════════════════════════════════════════════════════════════╗
║              QUEEN ENHANCEMENT PHASE 2 - DEEP ITERATIONS                     ║
║                                                                              ║
║  Enhancing 20 critical components with production features                   ║
║  Target: Use $10 of remaining budget for maximum value                       ║
║                                                                              ║
╚══════════════════════════════════════════════════════════════════════════════╝
    """)

    tracker = Tracker()
    results = []

    for i, task in enumerate(ENHANCEMENT_TASKS):
        if tracker.should_stop:
            print(f"\n⚠️  BUDGET LIMIT REACHED - Stopping")
            break

        print(f"\n[{i+1}/{len(ENHANCEMENT_TASKS)}]")
        result = await run_enhancement(task, tracker)
        results.append(result)

    # Summary
    successful = sum(1 for r in results if r.get("success"))
    total_size = sum(r.get("size", 0) for r in results if r.get("success"))

    print(f"""
╔══════════════════════════════════════════════════════════════════════════════╗
║              PHASE 2 ENHANCEMENT COMPLETE                                    ║
╠══════════════════════════════════════════════════════════════════════════════╣
║  Tasks Run:      {len(results):3}                                                       ║
║  Successful:     {successful:3}                                                       ║
║  Total Output:   {total_size:,} chars                                             ║
║  Total Cost:     ${tracker.cost:.4f}                                              ║
║  Tokens:         {tracker.input_tokens + tracker.output_tokens:,}                                               ║
╠══════════════════════════════════════════════════════════════════════════════╣
║  Enhanced files: {ENHANCED_DIR}
╚══════════════════════════════════════════════════════════════════════════════╝
    """)

    # List files
    print("\n📄 Enhanced Artifacts:")
    for f in sorted(ENHANCED_DIR.glob("*.py")):
        print(f"  - {f.name} ({f.stat().st_size:,} bytes)")


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