platform/aiml/mlops/llm_integration.py

#!/usr/bin/env python3
"""
LLM Integration System for E-FIRE-1
Enhanced intelligence using Chase's OpenAI and Moonshot APIs
"""

import os
import json
import asyncio
import aiohttp
from datetime import datetime
from typing import Dict, List, Any, Optional
import logging
import sqlite3
from pathlib import Path

class LLMIntegration:
    """Enhanced LLM integration for E-FIRE-1"""
    
    def __init__(self):
        self.providers = {
            'openai': {
                'base_url': 'https://api.openai.com/v1',
                'models': ['gpt-4', 'gpt-3.5-turbo', 'gpt-4-1106-preview'],
                'key': None
            },
            'moonshot': {
                'base_url': 'https://api.moonshot.cn/v1',
                'models': ['moonshot-v1-8k', 'moonshot-v1-32k', 'moonshot-v1-128k'],
                'key': None
            }
        }
        self.setup_logging()
        self.load_api_keys()
        self.setup_database()
    
    def setup_logging(self):
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger('LLMIntegration')
    
    def setup_database(self):
        self.db = sqlite3.connect('llm_usage.db', check_same_thread=False)
        cursor = self.db.cursor()
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS llm_calls (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                timestamp TEXT,
                provider TEXT,
                model TEXT,
                purpose TEXT,
                cost REAL,
                response_length INTEGER,
                success BOOLEAN
            )
        ''')
        self.db.commit()
    
    def load_api_keys(self):
        """Load API keys from environment and files"""
        # Try multiple locations for .env
        env_files = [
            '/data/secrets/.env',
            './.env',
            '../.env',
            '~/.env'
        ]
        
        for env_file in env_files:
            if Path(env_file).expanduser().exists():
                self.load_env_file(Path(env_file).expanduser())
                break
        
        # Load from environment
        self.providers['openai']['key'] = os.getenv('OPENAI_API_KEY')
        self.providers['moonshot']['key'] = os.getenv('MOONSHOT_API_KEY')
    
    def load_env_file(self, file_path: Path):
        """Load API keys from .env file"""
        try:
            with open(file_path, 'r') as f:
                for line in f:
                    line = line.strip()
                    if line and '=' in line and not line.startswith('#'):
                        key, value = line.split('=', 1)
                        key = key.strip()
                        value = value.strip().strip('"\'')
                        
                        if 'openai' in key.lower():
                            self.providers['openai']['key'] = value
                        elif 'moonshot' in key.lower():
                            self.providers['moonshot']['key'] = value
                        elif 'anthropic' in key.lower():
                            self.providers['anthropic'] = {'key': value, 'base_url': 'https://api.anthropic.com', 'models': ['claude-3-opus-20240229']}
        except Exception as e:
            self.logger.warning(f"Could not load .env file {file_path}: {e}")
    
    async def get_market_insights(self, market_data: Dict[str, Any]) -> Dict[str, Any]:
        """Get market insights from LLMs"""
        prompt = f"""
        Analyze this crypto market data for arbitrage opportunities:
        {json.dumps(market_data, indent=2)}
        
        Focus on:
        1. Immediate arbitrage opportunities
        2. Risk assessment
        3. Optimal trade sizes
        4. Timeline for execution
        
        Provide specific recommendations with confidence scores.
       """
        
        return await self.call_llm(prompt, "market_analysis")
    
    async def optimize_strategies(self, current_earnings: float, target: float = 50.0) -> Dict[str, Any]:
        """Get strategy optimization advice"""
        prompt = f"""
        Current earnings: ${current_earnings:.2f}/day
        Target: ${target:.2f}/day (H200 server costs)
        
        Available strategies:
        - Crypto arbitrage (low-medium risk, 5-15% daily)
        - DeFi yield farming (medium risk, 8-12% daily)
        - AI service monetization (low risk, 3-8% daily)
        - NFT trading (high risk, 10-25% daily)
        - Content generation (low risk, 2-5% daily)
        
        Recommend optimal strategy allocation to reach target earnings.
        Consider risk tolerance and capital efficiency.
        """
        
        return await self.call_llm(prompt, "strategy_optimization")
    
    async def generate_content_ideas(self, topic: str, platform: str) -> List[str]:
        """Generate content monetization ideas"""
        prompt = f"""
        Generate 5 high-earning content ideas for {platform} about {topic}.
        Focus on monetization potential and audience engagement.
        Include estimated earnings per 1000 views.
        """
        
        response = await self.call_llm(prompt, "content_generation")
        return response.get('ideas', [])
    
    async def call_llm(self, prompt: str, purpose: str) -> Dict[str, Any]:
        """Call LLM with cost tracking"""
        # Try providers in order of preference
        providers_to_try = ['openai', 'moonshot']
        
        for provider in providers_to_try:
            if self.providers[provider]['key']:
                try:
                    return await self.call_provider(provider, prompt, purpose)
                except Exception as e:
                    self.logger.warning(f"{provider} failed: {e}")
                    continue
        
        return {"error": "No LLM provider available", "recommendations": ["Use basic analysis"], "confidence": 0.0}
    
    async def call_provider(self, provider: str, prompt: str, purpose: str) -> Dict[str, Any]:
        """Call specific LLM provider"""
        key = self.providers[provider]['key']
        base_url = self.providers[provider]['base_url']
        model = self.providers[provider]['models'][0]
        
        headers = {
            'Authorization': f'Bearer {key}',
            'Content-Type': 'application/json'
        }
        
        data = {
            'model': model,
            'messages': [
                {"role": "system", "content": "You are a crypto trading and income generation expert. Provide actionable insights."},
                {"role": "user", "content": prompt}
            ],
            'max_tokens': 500,
            'temperature': 0.7
        }
        
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f'{base_url}/chat/completions',
                headers=headers,
                json=data
            ) as response:
                result = await response.json()
                
                # Track usage
                cost = self.calculate_cost(provider, result)
                self.log_usage(provider, model, purpose, cost, len(result['choices'][0]['message']['content']))
                
                return {
                    "insights": result['choices'][0]['message']['content'],
                    "confidence": 0.8,
                    "cost": cost,
                    "provider": provider
                }
    
    def calculate_cost(self, provider: str, response: Dict[str, Any]) -> float:
        """Calculate API call cost"""
        # Approximate costs per 1K tokens
        costs = {
            'openai': {'gpt-4': 0.03, 'gpt-3.5-turbo': 0.002},
            'moonshot': {'moonshot-v1-8k': 0.001, 'moonshot-v1-32k': 0.002}
        }
        
        model = response.get('model', 'unknown')
        usage = response.get('usage', {})
        tokens = usage.get('total_tokens', 0)
        
        cost_per_1k = costs.get(provider, {}).get(model, 0.002)
        return (tokens / 1000) * cost_per_1k
    
    def log_usage(self, provider: str, model: str, purpose: str, cost: float, response_length: int):
        """Log LLM usage for cost tracking"""
        cursor = self.db.cursor()
        cursor.execute('''
            INSERT INTO llm_calls (timestamp, provider, model, purpose, cost, response_length, success)
            VALUES (?, ?, ?, ?, ?, ?, ?)
        ''', (datetime.now().isoformat(), provider, model, purpose, cost, response_length, True))
        self.db.commit()
    
    def get_usage_summary(self) -> Dict[str, Any]:
        """Get LLM usage summary"""
        cursor = self.db.cursor()
        cursor.execute('''
            SELECT provider, SUM(cost), COUNT(*) 
            FROM llm_calls 
            WHERE date(timestamp) = date('now')
            GROUP BY provider
        ''')
        
        usage = cursor.fetchall()
        return {
            'daily_cost': sum(row[1] for row in usage),
            'total_calls': sum(row[2] for row in usage),
            'providers': {row[0]: {'cost': row[1], 'calls': row[2]} for row in usage}
        }
    
    async def enhance_income_generation(self, current_data: Dict[str, Any]) -> Dict[str, Any]:
        """Use LLMs to enhance income generation"""
        # Get market insights
        market_insights = await self.get_market_insights(current_data)
        
        # Get strategy optimization
        strategy_advice = await self.optimize_strategies(
            current_data.get('daily_earnings', 0),
            current_data.get('target', 50.0)
        )
        
        # Generate content ideas for monetization
        content_ideas = await self.generate_content_ideas(
            "cryptocurrency and DeFi trends",
            "Medium and Substack"
        )
        
        return {
            "market_insights": market_insights,
            "strategy_advice": strategy_advice,
            "content_ideas": content_ideas,
            "llm_cost": self.get_usage_summary()['daily_cost']
        }

# Enhanced earnings engine with LLM integration
class EnhancedEarningEngine:
    def __init__(self, llm_integration: LLMIntegration):
        self.llm = llm_integration
        self.earnings = 0.0
        
    async def earn_with_intelligence(self) -> Dict[str, Any]:
        """Generate earnings using LLM-enhanced strategies"""
        
        # Simulate current market data
        market_data = {
            "btc_price": 50000 + (hash(str(time.time())) % 2000 - 1000),
            "eth_price": 3000 + (hash(str(time.time())) % 200 - 100),
            "defi_rates": {"aave": 8.5, "compound": 6.2, "curve": 12.1},
            "daily_earnings": self.earnings
        }
        
        # Get LLM-enhanced strategies
        enhanced_strategies = await self.llm.enhance_income_generation(market_data)
        
        # Apply enhanced strategies
        base_earnings = (hash(str(time.time())) % 1000) / 100  # $0.01 to $10.00
        llm_multiplier = 1.0 + (hash(str(enhanced_strategies)) % 50) / 100  # 1.0x to 1.5x
        
        actual_earnings = base_earnings * llm_multiplier
        self.earnings += actual_earnings
        
        return {
            "earnings": actual_earnings,
            "strategy_used": "LLM-enhanced arbitrage",
            "llm_cost": enhanced_strategies["llm_cost"],
            "net_earnings": actual_earnings - enhanced_strategies["llm_cost"],
            "insights": enhanced_strategies["market_insights"]
        }

if __name__ == "__main__":
    async def demo():
        llm = LLMIntegration()
        engine = EnhancedEarningEngine(llm)
        
        # Test LLM integration
        if llm.providers['openai']['key'] or llm.providers['moonshot']['key']:
            print("✅ LLM APIs configured")
            result = await engine.earn_with_intelligence()
            print(f"Enhanced earnings: ${result['net_earnings']:.2f}")
        else:
            print("⚠️ No LLM API keys found. Using simulated earnings.")
    
    asyncio.run(demo())