# 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

# 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(metrics_file)
        self.metrics = self._load_metrics()

    def _load_metrics(self) -> Dict:
        """Loads performance metrics from file."""
        if self.metrics_file.exists():
            with open(self.metrics_file, "r") as f:
                return json.load(f)
        else:
            return {"video_processing_time": [], "p5_consensus_success": [], "axiom_generation_success": []}

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

    def process_new_video(self, video_id: str, url: str):
        """Runs youtube_learner, validates, axiomatizes, and integrates into the KG."""
        print(f"--- Evolution Start: {video_id} ---")
        start_time = time.time()

        # 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)

        # 2. Gate A: P5 Consensus Validation (Simulated Swarm Check)
        p5_success = self._run_p5_consensus(video_id, result.stdout)
        if not p5_success:
            print(f"⚠️ EVOLUTION BLOCKED: P5 Consensus Gate failed for {video_id}.")
            self.metrics["p5_consensus_success"].append(0)
            self._save_metrics()
            return
        self.metrics["p5_consensus_success"].append(1) # Record success

        # 3. Axiomatization
        axiom_success = self._generate_video_axiom(video_id, result.stdout or "No transcript available")
        self.metrics["axiom_generation_success"].append(int(axiom_success))

        # 4. Inject into Knowledge Graph
        self._inject_into_kg(video_id)

        # 5. Trigger Revenue Pathway Discovery
        self._propose_revenue_pipeline(video_id)

        # Performance Tracking
        processing_time = time.time() - start_time
        self.metrics["video_processing_time"].append(processing_time)
        self._save_metrics()
        print(f"✓ Video processed in {processing_time:.2f} seconds.")

        # 6. Improvement Opportunities (Simple Anomaly Detection)
        self._identify_improvement_opportunities()

    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}...")

        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):
        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")

    def _propose_revenue_pipeline(self, video_id: str):
        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 _identify_improvement_opportunities(self):
        """Identifies potential areas for improvement based on performance metrics."""
        if not self.metrics["video_processing_time"]:
            return

        avg_processing_time = sum(self.metrics["video_processing_time"]) / len(self.metrics["video_processing_time"])
        if self.metrics["video_processing_time"][-1] > 1.5 * avg_processing_time:  # Simple anomaly detection
            print("⚠️ Improvement Opportunity: Recent video processing time significantly higher than average.")
            self._propose_improvement("Optimize youtube_learner.py for faster processing.")

        p5_success_rate = sum(self.metrics["p5_consensus_success"]) / len(self.metrics["p5_consensus_success"])
        if p5_success_rate < 0.9:
            print("⚠️ Improvement Opportunity: P5 Consensus success rate is below 90%.")
            self._propose_improvement("Review P5 Consensus agents for potential improvements.")

        axiom_success_rate = sum(self.metrics["axiom_generation_success"]) / len(self.metrics["axiom_generation_success"])
        if axiom_success_rate < 0.8:
            print("⚠️ Improvement Opportunity: Axiom generation success rate is below 80%.")
            self._propose_improvement("Improve Axiom generation logic or data quality.")

    def _propose_improvement(self, description: str):
        """Proposes a potential improvement to the system."""
        improvement_id = f"IMP_{datetime.now().strftime('%Y%m%d%H%M%S')}"
        print(f"💡 Improvement Proposal: {improvement_id} - {description}")
        # Store improvement proposal (e.g., in a file or database)
        with open("improvement_proposals.txt", "a") as f:
            f.write(f"{improvement_id}: {description}\n")

        # Trigger A/B testing (simulated)
        self._run_ab_test(improvement_id, description)

    def _run_ab_test(self, improvement_id: str, description: str):
        """Simulates A/B testing of a proposed improvement."""
        print(f"🧪 Starting A/B test for {improvement_id}: {description}")
        # In reality, this would involve deploying the improved version to a subset of users/videos

        # Simulate A/B test results
        success_probability = random.random()  # Simulate the probability of the improvement being successful
        if success_probability > 0.5:  # Simulate a successful A/B test
            print(f"✅ A/B test for {improvement_id} successful! Deploying improvement.")
            self._deploy_improvement(improvement_id, description)
        else:
            print(f"❌ A/B test for {improvement_id} failed. Rolling back.")
            self._rollback_improvement(improvement_id, description)

    def _deploy_improvement(self, improvement_id: str, description: str):
        """Deploys a successful improvement to the system."""
        print(f"🚀 Deploying improvement {improvement_id}: {description}")
        # Implement the actual deployment logic here (e.g., update code, configuration)

        # Simulate learning from success
        self._learn_from_success(improvement_id, description)

    def _rollback_improvement(self, improvement_id: str, description: str):
        """Rolls back a failed improvement."""
        print(f"⏪ Rolling back improvement {improvement_id}: {description}")
        # Implement the rollback logic here (e.g., revert to previous code, configuration)

        # Simulate learning from failure
        self._learn_from_failure(improvement_id, description)

    def _learn_from_success(self, improvement_id: str, description: str):
        """Records and learns from a successful improvement."""
        print(f"🧠 Learning from success: {improvement_id} - {description}")
        # Update models, adjust parameters, etc.
        with open("learning_log.txt", "a") as f:
            f.write(f"SUCCESS: {improvement_id} - {description}\n")


    def _learn_from_failure(self, improvement_id: str, description: str):
        """Records and learns from a failed improvement."""
        print(f"🧠 Learning from failure: {improvement_id} - {description}")
        # Analyze the reasons for failure, adjust strategies, etc.
        with open("learning_log.txt", "a") as f:
            f.write(f"FAILURE: {improvement_id} - {description}\n")


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") # Add another video for metric tracking