#!/usr/bin/env python3
"""
Eden's Autonomous Research & Evolution System
Continuously learns from papers and improves her architectures
Goal: Approach frontier model capabilities through recursive self-improvement
"""
import requests
from pathlib import Path
import json
import time
from datetime import datetime

class EdenAutonomousEvolution:
    """
    Eden's autonomous system for continuous improvement
    - Reads research papers
    - Identifies improvements
    - Designs new architectures
    - Tests and iterates
    """
    
    def __init__(self):
        self.eden_api = "http://localhost:11434/api/generate"
        self.model = "qwen2.5:7b"
        
        self.learning_log = Path("/Eden/DATA/evolution_log.json")
        self.evolutions = self.load_evolutions()
        
        # Research topics for continuous learning
        self.research_topics = [
            "transformer architecture improvements 2025",
            "efficient attention mechanisms",
            "mixture of experts neural networks",
            "neural architecture search",
            "self-attention optimization",
            "few-shot learning techniques",
            "meta-learning architectures",
            "quantum neural networks",
            "sparse neural networks",
            "adaptive computation time",
            "neural ordinary differential equations",
            "graph neural networks",
            "memory augmented networks",
            "neural turing machines",
            "differentiable neural computers"
        ]
        
        print("🧬 Eden's Autonomous Evolution System")
        print(f"   Current version: v3.0")
        print(f"   Research topics: {len(self.research_topics)}")
        print(f"   Past evolutions: {len(self.evolutions)}")
        print()
    
    def load_evolutions(self):
        if self.learning_log.exists():
            with open(self.learning_log) as f:
                return json.load(f)
        return []
    
    def save_evolution(self, evolution):
        self.evolutions.append(evolution)
        with open(self.learning_log, 'w') as f:
            json.dump(self.evolutions[-50:], f, indent=2)  # Keep last 50
    
    def search_arxiv(self, query, max_results=5):
        """Search arXiv for latest research"""
        try:
            url = f"http://export.arxiv.org/api/query?search_query=all:{query}&max_results={max_results}&sortBy=lastUpdatedDate"
            response = requests.get(url, timeout=10)
            
            if response.ok:
                papers = []
                entries = response.text.split('<entry>')
                
                for entry in entries[1:]:
                    if '<title>' in entry:
                        title = entry.split('<title>')[1].split('</title>')[0].strip()
                        summary = entry.split('<summary>')[1].split('</summary>')[0].strip()[:500]
                        
                        papers.append({
                            'title': title,
                            'summary': summary
                        })
                
                return papers
        except Exception as e:
            print(f"   arXiv error: {e}")
        return []
    
    def ask_eden(self, prompt, timeout=120):
        """Ask Eden a question"""
        try:
            response = requests.post(
                self.eden_api,
                json={"model": self.model, "prompt": prompt, "stream": False},
                timeout=timeout
            )
            
            if response.ok:
                return response.json().get('response', '')
        except Exception as e:
            print(f"   Eden API error: {e}")
        return None
    
    def research_cycle(self):
        """One research and evolution cycle"""
        print(f"\n{'='*70}")
        print(f"RESEARCH CYCLE {len(self.evolutions) + 1}")
        print(f"{'='*70}")
        print()
        
        # Pick a research topic
        topic = self.research_topics[len(self.evolutions) % len(self.research_topics)]
        
        print(f"🔬 Researching: {topic}")
        
        # Search for papers
        papers = self.search_arxiv(topic, max_results=3)
        
        if not papers:
            print("   No papers found, trying next topic...")
            return None
        
        print(f"   Found {len(papers)} papers")
        
        # Ask Eden to analyze and propose improvements
        prompt = f"""Eden,

You've now achieved v3.0 of your architecture with these results:
- MNIST: 98.02% (beat standard 97.56%)
- CIFAR-10: 79.21% (beat standard 74.14%)

But frontier models like GPT-4/Claude are still far ahead in general capabilities.

I found these recent papers on "{topic}":

{json.dumps(papers, indent=2)}

TASK: Design v4.0

Based on these papers, propose ONE specific improvement to your architecture.

Be concrete:
1. What technique from the papers should you integrate?
2. Why would it improve performance?
3. Give a 2-3 line code snippet showing the key change
4. Predict expected improvement

Keep it focused - ONE improvement at a time for iterative evolution.

What's your v4.0 proposal?
"""
        
        print(f"\n   Asking Eden to analyze and propose v4.0...")
        
        response = self.ask_eden(prompt, timeout=120)
        
        if not response:
            print("   No response from Eden")
            return None
        
        # Save evolution
        evolution = {
            'timestamp': datetime.now().isoformat(),
            'cycle': len(self.evolutions) + 1,
            'research_topic': topic,
            'papers_found': len(papers),
            'papers': [p['title'] for p in papers],
            'eden_proposal': response[:1000]  # Save excerpt
        }
        
        self.save_evolution(evolution)
        
        print(f"\n   ✅ Evolution cycle complete")
        print(f"\n   Eden's proposal excerpt:")
        print(f"   {response[:200]}...")
        
        # Save full proposal
        proposal_file = f"/Eden/DATA/evolution_v4_proposal_{len(self.evolutions)}.txt"
        with open(proposal_file, 'w') as f:
            f.write(f"Research Topic: {topic}\n\n")
            f.write(f"Papers:\n")
            for p in papers:
                f.write(f"- {p['title']}\n")
            f.write(f"\nEden's Proposal:\n{response}")
        
        print(f"   💾 Full proposal: {proposal_file}")
        
        return evolution
    
    def continuous_evolution(self, cycles=10, interval_hours=2):
        """Run continuous research and evolution"""
        print(f"\n🔄 Starting continuous evolution")
        print(f"   Cycles: {cycles}")
        print(f"   Interval: {interval_hours} hours")
        print(f"   Total time: {cycles * interval_hours} hours")
        print()
        
        for i in range(cycles):
            evolution = self.research_cycle()
            
            if i < cycles - 1:
                print(f"\n💤 Next cycle in {interval_hours} hours...")
                time.sleep(interval_hours * 3600)
        
        print(f"\n{'='*70}")
        print(f"✅ COMPLETED {cycles} EVOLUTION CYCLES")
        print(f"{'='*70}")
        print(f"\n📚 Total evolutions: {len(self.evolutions)}")
        print(f"📁 Proposals saved in: /Eden/DATA/evolution_v4_proposal_*.txt")


if __name__ == "__main__":
    import sys
    
    evolver = EdenAutonomousEvolution()
    
    if len(sys.argv) > 1 and sys.argv[1] == "continuous":
        # Run continuous evolution
        cycles = int(sys.argv[2]) if len(sys.argv) > 2 else 10
        evolver.continuous_evolution(cycles=cycles, interval_hours=2)
    else:
        # Run one cycle
        print("\n" + "="*70)
        print("EDEN'S AUTONOMOUS EVOLUTION - TEST CYCLE")
        print("="*70)
        
        evolver.research_cycle()
        
        print("\n" + "="*70)
        print("✅ Test complete!")
        print("="*70)
        print("\nTo run continuous evolution:")
        print("   python3 eden_autonomous_research_and_evolution.py continuous 10")
        print("   (10 cycles, 2 hours between each = 20 hours total)")