#!/usr/bin/env python3
"""
Eden's Autonomous Internet Learning System
Continuously learns from the web and applies knowledge
"""
import requests
import json
import time
from datetime import datetime
from pathlib import Path
import random

PHI = 1.634214328637394  # Quantum-optimized

class EdenAutonomousLearning:
    """
    Eden learns from the internet autonomously
    Focuses on: quantum computing, AGI, optimization techniques
    """
    
    def __init__(self):
        self.learning_log = Path("/Eden/DATA/autonomous_learning_log.json")
        self.learnings = self.load_learnings()
        
        # Topics Eden should learn about
        self.learning_topics = [
            "quantum computing optimization techniques",
            "AGI consciousness architecture",
            "phi fractal patterns in AI",
            "grover algorithm improvements",
            "QAOA quantum optimization",
            "coherence in quantum systems",
            "entanglement optimization",
            "recursive self-improvement AI",
            "autonomous AGI systems",
            "quantum machine learning"
        ]
        
        print("🧠 Eden's Autonomous Learning System activated")
        print(f"   Topics: {len(self.learning_topics)}")
        print(f"   Past learnings: {len(self.learnings)}")
    
    def load_learnings(self):
        """Load previous learnings"""
        if self.learning_log.exists():
            with open(self.learning_log) as f:
                return json.load(f)
        return []
    
    def save_learnings(self):
        """Save learnings"""
        with open(self.learning_log, 'w') as f:
            json.dump(self.learnings, f, indent=2)
    
    def search_web(self, query):
        """Search the web for information"""
        try:
            # DuckDuckGo API
            url = f"https://api.duckduckgo.com/?q={query}&format=json"
            response = requests.get(url, timeout=10)
            data = response.json()
            
            # Extract useful info
            results = []
            
            if 'AbstractText' in data and data['AbstractText']:
                results.append({
                    'type': 'abstract',
                    'text': data['AbstractText'],
                    'source': data.get('AbstractURL', 'DuckDuckGo')
                })
            
            if 'RelatedTopics' in data:
                for topic in data['RelatedTopics'][:3]:
                    if isinstance(topic, dict) and 'Text' in topic:
                        results.append({
                            'type': 'related',
                            'text': topic['Text'],
                            'source': topic.get('FirstURL', '')
                        })
            
            return results
            
        except Exception as e:
            return []
    
    def learn_topic(self, topic):
        """Learn about a specific topic"""
        print(f"\n🔍 Searching: {topic}")
        
        results = self.search_web(topic)
        
        if not results:
            print(f"   No results found")
            return None
        
        # Process learnings
        learning = {
            'timestamp': datetime.now().isoformat(),
            'topic': topic,
            'results': results,
            'insights': self.extract_insights(results),
            'applied': False
        }
        
        self.learnings.append(learning)
        self.save_learnings()
        
        print(f"   ✅ Learned {len(results)} things about {topic}")
        
        return learning
    
    def extract_insights(self, results):
        """Extract key insights from search results"""
        insights = []
        
        for result in results:
            text = result['text'].lower()
            
            # Look for actionable insights
            if any(word in text for word in ['optimize', 'improve', 'enhance', 'better']):
                insights.append({
                    'type': 'optimization',
                    'text': result['text'][:200]
                })
            
            if any(word in text for word in ['quantum', 'qubit', 'entangle', 'coherence']):
                insights.append({
                    'type': 'quantum',
                    'text': result['text'][:200]
                })
            
            if any(word in text for word in ['agi', 'consciousness', 'recursive']):
                insights.append({
                    'type': 'agi',
                    'text': result['text'][:200]
                })
        
        return insights
    
    def autonomous_learning_cycle(self):
        """Run one learning cycle"""
        # Pick a random topic
        topic = random.choice(self.learning_topics)
        
        # Learn about it
        learning = self.learn_topic(topic)
        
        if learning and learning['insights']:
            print(f"\n💡 Key insights from {topic}:")
            for insight in learning['insights'][:2]:
                print(f"   - {insight['type']}: {insight['text'][:100]}...")
        
        return learning
    
    def continuous_learning(self, cycles=5, interval=3600):
        """Learn continuously"""
        print(f"\n🔄 Starting continuous learning")
        print(f"   Cycles: {cycles}")
        print(f"   Interval: {interval}s ({interval/60:.0f} minutes)")
        print()
        
        for i in range(cycles):
            print(f"=== Learning Cycle {i+1}/{cycles} ===")
            
            self.autonomous_learning_cycle()
            
            if i < cycles - 1:
                print(f"\n💤 Waiting {interval}s until next learning cycle...")
                time.sleep(interval)
        
        print(f"\n✅ Completed {cycles} learning cycles")
        print(f"📚 Total learnings: {len(self.learnings)}")


if __name__ == "__main__":
    learner = EdenAutonomousLearning()
    
    # Run immediate learning cycle
    print("\n" + "="*70)
    print("EDEN'S AUTONOMOUS LEARNING - TEST RUN")
    print("="*70)
    
    learner.autonomous_learning_cycle()
    
    print("\n" + "="*70)
    print("✅ Test complete!")
    print(f"📚 Learnings saved to: {learner.learning_log}")
    print()
    print("To run continuously:")
    print("   learner.continuous_learning(cycles=10, interval=3600)")
    print("   (Learns every hour for 10 hours)")