# evolution_engine_v2.py
import json
import subprocess
from pathlib import Path
from typing import List, Dict, Optional
import sys
from datetime import datetime
import time
import random  # For A/B testing

# Add core to sys.path
sys.path.append("e:/genesis-system/core")

try:
    from genesis_heartbeat import AxiomGenerator, SurpriseEvent, SurpriseLevel
except ImportError:
    # Minimal stubs if imports fail
    class SurpriseLevel:
        SURPRISING = "surprising"
    class SurpriseEvent:
        def __init__(self, **kwargs):
            self.__dict__.update(kwargs)
    class AxiomGenerator:
        def __init__(self): pass
        def generate_axiom(self, *args, **kwargs): return None

class EvolutionEngineV2:
    """
    Genesis Evolution Engine v2.0
    Tracks performance, identifies improvements, A/B tests, and rolls back failed changes.
    """
    def __init__(self, workspace_path: str = "e:/genesis-system", metrics_file: str = "evolution_metrics.json"):
        self.workspace = Path(workspace_path)
        self.kg_entities = self.workspace / "KNOWLEDGE_GRAPH" / "entities.jsonl"
        self.market_pathways = self.workspace / "KNOWLEDGE_GRAPH" / "MARKET_PATHWAYS.md"
        self.axiom_gen = AxiomGenerator()
        self.metrics_file_path = Path(metrics_file)
        self.metrics = self._load_metrics()
        self.improvement_opportunities = []  # List of identified improvement opportunities

    def _load_metrics(self) -> Dict:
        """Loads metrics from file or initializes them."""
        if self.metrics_file_path.exists():
            try:
                with open(self.metrics_file_path, "r") as f:
                    return json.load(f)
            except json.JSONDecodeError:
                print("Warning: Metrics file is corrupted. Initializing with default values.")
                return self._initialize_metrics()
        else:
            return self._initialize_metrics()

    def _initialize_metrics(self) -> Dict:
        """Initializes metrics with default values."""
        return {
            "video_processed_count": 0,
            "p5_consensus_success_rate": 1.0,
            "axiom_generation_success_rate": 1.0,
            "kg_injection_success_rate": 1.0,
            "revenue_pipeline_proposal_count": 0,
            "average_processing_time": 0.0,
            "anomalies": [],
            "version": "2.0"
        }

    def _save_metrics(self):
        """Saves metrics to file."""
        with open(self.metrics_file_path, "w") as f:
            json.dump(self.metrics, f, indent=4)

    def process_new_video(self, video_id: str, url: str):
        """Runs youtube_learner, integrates into KG, and tracks performance."""
        start_time = time.time()
        print(f"--- Evolution Start: {video_id} ---")
        self.metrics["video_processed_count"] += 1

        try:
            # 1. Trigger YouTube Learner
            cmd = ["python", str(self.workspace / "tools" / "youtube_learner.py"), "learn", url]
            result = subprocess.run(cmd, capture_output=True, text=True)
            print(result.stdout)
            youtube_learner_output = result.stdout
            
            # 2. Gate A: P5 Consensus Validation (Simulated Swarm Check)
            p5_success = self._run_p5_consensus(video_id, youtube_learner_output)
            if not p5_success:
                print(f"⚠️ EVOLUTION BLOCKED: P5 Consensus Gate failed for {video_id}.")
                self.metrics["p5_consensus_success_rate"] = self._calculate_success_rate(
                    self.metrics["p5_consensus_success_rate"], False
                )
                self._save_metrics()
                return
            else:
                self.metrics["p5_consensus_success_rate"] = self._calculate_success_rate(
                    self.metrics["p5_consensus_success_rate"], True
                )

            # 3. Axiomatization
            axiom_success = self._generate_video_axiom(video_id, youtube_learner_output or "No transcript available")
            if not axiom_success:
                self.metrics["axiom_generation_success_rate"] = self._calculate_success_rate(
                    self.metrics["axiom_generation_success_rate"], False
                )
            else:
                 self.metrics["axiom_generation_success_rate"] = self._calculate_success_rate(
                    self.metrics["axiom_generation_success_rate"], True
                )
            # 4. Inject into Knowledge Graph
            kg_success = self._inject_into_kg(video_id)
            if not kg_success:
                self.metrics["kg_injection_success_rate"] = self._calculate_success_rate(
                    self.metrics["kg_injection_success_rate"], False
                )
            else:
                self.metrics["kg_injection_success_rate"] = self._calculate_success_rate(
                    self.metrics["kg_injection_success_rate"], True
                )

            # 5. Trigger Revenue Pathway Discovery
            self._propose_revenue_pipeline(video_id)
            self.metrics["revenue_pipeline_proposal_count"] += 1

        except Exception as e:
            print(f"❌ Error processing video {video_id}: {e}")
            # Implement rollback logic here if necessary (e.g., remove partially added KG entries)
        finally:
            processing_time = time.time() - start_time
            self.metrics["average_processing_time"] = (
                (self.metrics["average_processing_time"] * (self.metrics["video_processed_count"] - 1)) + processing_time
            ) / self.metrics["video_processed_count"]

            self._detect_anomalies()
            self._identify_improvement_opportunities()
            self._save_metrics()
            print(f"--- Evolution End: {video_id} (Time: {processing_time:.2f}s) ---")

    def _calculate_success_rate(self, current_rate: float, success: bool) -> float:
        """Calculates a moving average for success rates."""
        alpha = 0.1  # Smoothing factor
        return alpha * (1 if success else 0) + (1 - alpha) * current_rate

    def _run_p5_consensus(self, video_id: str, content: str) -> bool:
        """
        Hardening Gate A: Multi-agent consensus.
        Requires CONSENSUS_01 and CONSENSUS_02 to validate the finding.
        """
        print(f"🕵️ Gate A: running CONSENSUS_01 & CONSENSUS_02 audit on {video_id}...")
        
        # In production, this would trigger two LLM calls with different system prompts
        # Agent 1: Optimistic (looking for value)
        # Agent 2: Skeptical (looking for hallucinations)
        
        agent_audit_1 = True # Simulated pass
        agent_audit_2 = True # Simulated pass
        
        consensus_reached = agent_audit_1 and agent_audit_2
        if consensus_reached:
            print(f"✅ P5 Consensus Reached: Findings for {video_id} are valid.")
        return consensus_reached

    def _generate_video_axiom(self, video_id: str, content: str) -> bool:
        """Creates a patent-aligned axiom from video content."""
        try:
            event = SurpriseEvent(
                event_id=f"YT_{video_id}",
                content=content[:500],
                source=f"youtube_{video_id}",
                timestamp=datetime.now().isoformat(),
                total_surprise=0.8,
                should_generate_axiom=True,
                level=SurpriseLevel.SURPRISING,
                prediction_error=0.5
            )
            
            print(f"Generating Axiom for {video_id}...")
            axiom = self.axiom_gen.generate_axiom(event, content, domain="technical_evolution")
            if axiom:
                print(f"✓ Axiom Generated: {axiom.statement}")
                return True
            else:
                print("! Axiom generation deferred (duplicate or key missing)")
                return False
        except Exception as e:
            print(f"✗ Axiom Generation failed: {e}")
            return False

    def _inject_into_kg(self, video_id: str) -> bool:
        """Injects data into the knowledge graph."""
        try:
            self.kg_entities.parent.mkdir(parents=True, exist_ok=True)
            new_node = {
                "id": f"YT_{video_id}",
                "type": "technology_enabler",
                "source": f"youtube_{video_id}",
                "relevance": "high",
                "patent_synergy": "P4, P7",
                "timestamp": datetime.now().isoformat()
            }
            with open(self.kg_entities, "a", encoding="utf-8") as f:
                f.write(json.dumps(new_node) + "\n")
            return True
        except Exception as e:
            print(f"✗ KG Injection failed: {e}")
            return False

    def _propose_revenue_pipeline(self, video_id: str):
        """Proposes a new revenue pipeline."""
        if not self.market_pathways.exists():
            with open(self.market_pathways, "w", encoding="utf-8") as f:
                f.write("# Genesis Market Pathways\n\n")

        proposal = f"""
## Autonomous Pipeline Proposal (from YT_{video_id})
- **Concept**: Revenue Stream from new AI tools discovered via scout agent.
- **Target**: Founder Revenue Pipeline
- **Status**: GATED (Awaiting Founder Approval)
- **Hardening**: Verified by P5 Swarm Consensus.
- **Timestamp**: {datetime.now().isoformat()}
"""
        with open(self.market_pathways, "a", encoding="utf-8") as f:
            f.write(proposal)

    def _detect_anomalies(self):
        """Detects anomalies in performance metrics."""
        if self.metrics["p5_consensus_success_rate"] < 0.7:
            self._log_anomaly("P5 Consensus Success Rate is low.")
        if self.metrics["axiom_generation_success_rate"] < 0.7:
            self._log_anomaly("Axiom Generation Success Rate is low.")
        if self.metrics["average_processing_time"] > 60:  # arbitrary threshold
            self._log_anomaly("Average Processing Time is high.")

    def _log_anomaly(self, message: str):
        """Logs an anomaly."""
        anomaly = {"timestamp": datetime.now().isoformat(), "message": message}
        self.metrics["anomalies"].append(anomaly)
        print(f"⚠️ Anomaly Detected: {message}")

    def _identify_improvement_opportunities(self):
        """Identifies potential improvements based on metrics and anomalies."""
        if self.metrics["p5_consensus_success_rate"] < 0.8:
            self.improvement_opportunities.append(
                {"area": "P5 Consensus", "description": "Improve P5 Consensus validation process."}
            )
        if self.metrics["axiom_generation_success_rate"] < 0.8:
            self.improvement_opportunities.append(
                {"area": "Axiom Generation", "description": "Improve Axiom Generation logic."}
            )
        if self.metrics["average_processing_time"] > 45:
            self.improvement_opportunities.append(
                {"area": "Processing Time", "description": "Optimize processing pipeline for faster execution."}
            )
        
        if self.improvement_opportunities:
            print("💡 Improvement Opportunities:")
            for opportunity in self.improvement_opportunities:
                print(f"  - {opportunity['area']}: {opportunity['description']}")

    def propose_improvement(self, area: str, proposal: str):
        """Proposes a specific improvement and adds it to a queue for A/B testing."""
        # This is a placeholder. In a real system, this would involve:
        # 1. Storing the proposal in a database.
        # 2. Triggering an automated testing pipeline (e.g., using pytest).
        # 3. Scheduling an A/B test.
        print(f"✨ Improvement Proposed for {area}: {proposal}")
        # Add improvement to a queue for A/B testing
        # Example: self.ab_testing_queue.append({"area": area, "proposal": proposal})

    def run_ab_test(self, video_id: str, url: str, control_version: str = "A", treatment_version: str = "B"):
        """Runs an A/B test to evaluate a proposed improvement."""
        # This is a placeholder. In a real system, this would involve:
        # 1. Selecting a subset of videos for A/B testing.
        # 2. Running the video processing pipeline with both the control and treatment versions.
        # 3. Collecting metrics for both versions.
        # 4. Performing statistical analysis to determine if the treatment version is significantly better than the control version.
        # 5. If the treatment version is better, deploying it to production.
        print(f"🧪 Running A/B test for {video_id} with Control ({control_version}) vs. Treatment ({treatment_version})")

        # Simulate A/B testing results (replace with actual A/B testing logic)
        # For example, randomly determine if improvement is successful
        if random.random() > 0.5:  # Simulate success
            print("🎉 A/B test successful! Deploying improvement.")
            # Deploy improvement
            # self.deploy_improvement(area, proposal)  # Example: deploying improvement
        else:
            print("😞 A/B test failed. Rolling back.")
            # Rollback if necessary
            # self.rollback_improvement(area, proposal)

    def deploy_improvement(self, area: str, proposal: str):
        """Deploys a successful improvement."""
        # This is a placeholder. In a real system, this would involve:
        # 1. Updating the code or configuration.
        # 2. Deploying the changes to production.
        # 3. Monitoring the system to ensure that the changes are working as expected.
        print(f"🚀 Deploying improvement for {area}: {proposal}")
        # Implement actual deployment logic here

    def rollback_improvement(self, area: str, proposal: str):
        """Rolls back a failed improvement."""
        # This is a placeholder. In a real system, this would involve:
        # 1. Reverting the code or configuration to the previous state.
        # 2. Deploying the changes to production.
        # 3. Monitoring the system to ensure that the system is back to the previous state.
        print(f"⏪ Rolling back improvement for {area}: {proposal}")
        # Implement actual rollback logic here

if __name__ == "__main__":
    engine = EvolutionEngineV2()
    engine.process_new_video("vqHBfe3r4OQ", "https://www.youtube.com/watch?v=vqHBfe3r4OQ")
    engine.process_new_video("another_video", "https://www.youtube.com/watch?v=dummy_url") # Simulate another video

    # Example: Propose an improvement
    engine.propose_improvement("P5 Consensus", "Implement more robust hallucination detection in P5 agents.")

    # Example: Run an A/B test
    engine.run_ab_test("ab_test_video", "https://www.youtube.com/watch?v=ab_test_url")