space/venture_strategies.py

"""Specialized strategies for autonomous business and revenue generation."""

import logging
from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import numpy as np
from collections import defaultdict

from .base import ReasoningStrategy

class VentureType(Enum):
    """Types of business ventures."""
    AI_STARTUP = "ai_startup"
    SAAS = "saas"
    API_SERVICE = "api_service"
    DATA_ANALYTICS = "data_analytics"
    AUTOMATION_SERVICE = "automation_service"
    CONSULTING = "consulting"
    DIGITAL_PRODUCTS = "digital_products"
    MARKETPLACE = "marketplace"

class RevenueStream(Enum):
    """Types of revenue streams."""
    SUBSCRIPTION = "subscription"
    USAGE_BASED = "usage_based"
    LICENSING = "licensing"
    CONSULTING = "consulting"
    PRODUCT_SALES = "product_sales"
    COMMISSION = "commission"
    ADVERTISING = "advertising"
    PARTNERSHIP = "partnership"

@dataclass
class VentureMetrics:
    """Key business metrics."""
    revenue: float
    profit_margin: float
    customer_acquisition_cost: float
    lifetime_value: float
    churn_rate: float
    growth_rate: float
    burn_rate: float
    runway_months: float
    roi: float

@dataclass
class MarketOpportunity:
    """Market opportunity analysis."""
    market_size: float
    growth_potential: float
    competition_level: float
    entry_barriers: float
    regulatory_risks: float
    technology_risks: float
    monetization_potential: float

class AIStartupStrategy(ReasoningStrategy):
    """
    Advanced AI startup strategy that:
    1. Identifies profitable AI applications
    2. Analyzes market opportunities
    3. Develops MVP strategies
    4. Plans scaling approaches
    5. Optimizes revenue streams
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Generate AI startup strategy."""
        try:
            # Market analysis
            market = await self._analyze_market(query, context)
            
            # Technology assessment
            tech = await self._assess_technology(market, context)
            
            # Business model
            model = await self._develop_business_model(tech, context)
            
            # Growth strategy
            strategy = await self._create_growth_strategy(model, context)
            
            # Financial projections
            projections = await self._project_financials(strategy, context)
            
            return {
                "success": projections["annual_profit"] >= 1_000_000,
                "market_analysis": market,
                "tech_assessment": tech,
                "business_model": model,
                "growth_strategy": strategy,
                "financials": projections,
                "confidence": self._calculate_confidence(projections)
            }
        except Exception as e:
            logging.error(f"Error in AI startup strategy: {str(e)}")
            return {"success": False, "error": str(e)}

class SaaSVentureStrategy(ReasoningStrategy):
    """
    Advanced SaaS venture strategy that:
    1. Identifies scalable SaaS opportunities
    2. Develops pricing strategies
    3. Plans customer acquisition
    4. Optimizes retention
    5. Maximizes recurring revenue
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Generate SaaS venture strategy."""
        try:
            # Opportunity analysis
            opportunity = await self._analyze_opportunity(query, context)
            
            # Product strategy
            product = await self._develop_product_strategy(opportunity, context)
            
            # Pricing model
            pricing = await self._create_pricing_model(product, context)
            
            # Growth plan
            growth = await self._plan_growth(pricing, context)
            
            # Revenue projections
            projections = await self._project_revenue(growth, context)
            
            return {
                "success": projections["annual_revenue"] >= 1_000_000,
                "opportunity": opportunity,
                "product": product,
                "pricing": pricing,
                "growth": growth,
                "projections": projections
            }
        except Exception as e:
            logging.error(f"Error in SaaS venture strategy: {str(e)}")
            return {"success": False, "error": str(e)}

class AutomationVentureStrategy(ReasoningStrategy):
    """
    Advanced automation venture strategy that:
    1. Identifies automation opportunities
    2. Analyzes cost-saving potential
    3. Develops automation solutions
    4. Plans implementation
    5. Maximizes ROI
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Generate automation venture strategy."""
        try:
            # Opportunity identification
            opportunities = await self._identify_opportunities(query, context)
            
            # Solution development
            solutions = await self._develop_solutions(opportunities, context)
            
            # Implementation strategy
            implementation = await self._create_implementation_strategy(solutions, context)
            
            # ROI analysis
            roi = await self._analyze_roi(implementation, context)
            
            # Scale strategy
            scale = await self._create_scale_strategy(roi, context)
            
            return {
                "success": roi["annual_profit"] >= 1_000_000,
                "opportunities": opportunities,
                "solutions": solutions,
                "implementation": implementation,
                "roi": roi,
                "scale": scale
            }
        except Exception as e:
            logging.error(f"Error in automation venture strategy: {str(e)}")
            return {"success": False, "error": str(e)}

class DataVentureStrategy(ReasoningStrategy):
    """
    Advanced data venture strategy that:
    1. Identifies valuable data opportunities
    2. Develops data products
    3. Creates monetization strategies
    4. Ensures compliance
    5. Maximizes data value
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Generate data venture strategy."""
        try:
            # Data opportunity analysis
            opportunity = await self._analyze_data_opportunity(query, context)
            
            # Product development
            product = await self._develop_data_product(opportunity, context)
            
            # Monetization strategy
            monetization = await self._create_monetization_strategy(product, context)
            
            # Compliance plan
            compliance = await self._ensure_compliance(monetization, context)
            
            # Scale plan
            scale = await self._plan_scaling(compliance, context)
            
            return {
                "success": monetization["annual_revenue"] >= 1_000_000,
                "opportunity": opportunity,
                "product": product,
                "monetization": monetization,
                "compliance": compliance,
                "scale": scale
            }
        except Exception as e:
            logging.error(f"Error in data venture strategy: {str(e)}")
            return {"success": False, "error": str(e)}

class APIVentureStrategy(ReasoningStrategy):
    """
    Advanced API venture strategy that:
    1. Identifies API opportunities
    2. Develops API products
    3. Creates pricing models
    4. Plans scaling
    5. Maximizes API value
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Generate API venture strategy."""
        try:
            # API opportunity analysis
            opportunity = await self._analyze_api_opportunity(query, context)
            
            # Product development
            product = await self._develop_api_product(opportunity, context)
            
            # Pricing strategy
            pricing = await self._create_api_pricing(product, context)
            
            # Scale strategy
            scale = await self._plan_api_scaling(pricing, context)
            
            # Revenue projections
            projections = await self._project_api_revenue(scale, context)
            
            return {
                "success": projections["annual_revenue"] >= 1_000_000,
                "opportunity": opportunity,
                "product": product,
                "pricing": pricing,
                "scale": scale,
                "projections": projections
            }
        except Exception as e:
            logging.error(f"Error in API venture strategy: {str(e)}")
            return {"success": False, "error": str(e)}

class MarketplaceVentureStrategy(ReasoningStrategy):
    """
    Advanced marketplace venture strategy that:
    1. Identifies marketplace opportunities
    2. Develops platform strategy
    3. Plans liquidity generation
    4. Optimizes matching
    5. Maximizes transaction value
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Generate marketplace venture strategy."""
        try:
            # Opportunity analysis
            opportunity = await self._analyze_marketplace_opportunity(query, context)
            
            # Platform strategy
            platform = await self._develop_platform_strategy(opportunity, context)
            
            # Liquidity strategy
            liquidity = await self._create_liquidity_strategy(platform, context)
            
            # Growth strategy
            growth = await self._plan_marketplace_growth(liquidity, context)
            
            # Revenue projections
            projections = await self._project_marketplace_revenue(growth, context)
            
            return {
                "success": projections["annual_revenue"] >= 1_000_000,
                "opportunity": opportunity,
                "platform": platform,
                "liquidity": liquidity,
                "growth": growth,
                "projections": projections
            }
        except Exception as e:
            logging.error(f"Error in marketplace venture strategy: {str(e)}")
            return {"success": False, "error": str(e)}

class VenturePortfolioStrategy(ReasoningStrategy):
    """
    Advanced venture portfolio strategy that:
    1. Optimizes venture mix
    2. Balances risk-reward
    3. Allocates resources
    4. Manages dependencies
    5. Maximizes portfolio value
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Generate venture portfolio strategy."""
        try:
            # Portfolio analysis
            analysis = await self._analyze_portfolio(query, context)
            
            # Venture selection
            selection = await self._select_ventures(analysis, context)
            
            # Resource allocation
            allocation = await self._allocate_resources(selection, context)
            
            # Risk management
            risk = await self._manage_risk(allocation, context)
            
            # Portfolio projections
            projections = await self._project_portfolio(risk, context)
            
            return {
                "success": projections["annual_profit"] >= 1_000_000,
                "analysis": analysis,
                "selection": selection,
                "allocation": allocation,
                "risk": risk,
                "projections": projections
            }
        except Exception as e:
            logging.error(f"Error in venture portfolio strategy: {str(e)}")
            return {"success": False, "error": str(e)}

    async def _analyze_portfolio(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze potential venture portfolio."""
        prompt = f"""
        Analyze venture portfolio opportunities:
        Query: {query}
        Context: {json.dumps(context)}
        
        Consider:
        1. Market opportunities
        2. Technology trends
        3. Resource requirements
        4. Risk factors
        5. Synergy potential
        
        Format as:
        [Analysis]
        Opportunities: ...
        Trends: ...
        Resources: ...
        Risks: ...
        Synergies: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_portfolio_analysis(response["answer"])

    def _parse_portfolio_analysis(self, response: str) -> Dict[str, Any]:
        """Parse portfolio analysis from response."""
        analysis = {
            "opportunities": [],
            "trends": [],
            "resources": {},
            "risks": [],
            "synergies": []
        }
        
        current_section = None
        for line in response.split('\n'):
            line = line.strip()
            if line.startswith('Opportunities:'):
                current_section = "opportunities"
            elif line.startswith('Trends:'):
                current_section = "trends"
            elif line.startswith('Resources:'):
                current_section = "resources"
            elif line.startswith('Risks:'):
                current_section = "risks"
            elif line.startswith('Synergies:'):
                current_section = "synergies"
            elif current_section and line:
                if current_section == "resources":
                    try:
                        key, value = line.split(':')
                        analysis[current_section][key.strip()] = value.strip()
                    except:
                        pass
                else:
                    analysis[current_section].append(line)
        
        return analysis

    def get_venture_metrics(self) -> Dict[str, Any]:
        """Get comprehensive venture metrics."""
        return {
            "portfolio_metrics": {
                "total_ventures": len(self.ventures),
                "profitable_ventures": sum(1 for v in self.ventures if v.metrics.profit_margin > 0),
                "total_revenue": sum(v.metrics.revenue for v in self.ventures),
                "average_margin": np.mean([v.metrics.profit_margin for v in self.ventures]),
                "portfolio_roi": np.mean([v.metrics.roi for v in self.ventures])
            },
            "market_metrics": {
                "total_market_size": sum(v.opportunity.market_size for v in self.ventures),
                "average_growth": np.mean([v.opportunity.growth_potential for v in self.ventures]),
                "risk_score": np.mean([v.opportunity.regulatory_risks + v.opportunity.technology_risks for v in self.ventures])
            },
            "performance_metrics": {
                "customer_acquisition": np.mean([v.metrics.customer_acquisition_cost for v in self.ventures]),
                "lifetime_value": np.mean([v.metrics.lifetime_value for v in self.ventures]),
                "churn_rate": np.mean([v.metrics.churn_rate for v in self.ventures]),
                "burn_rate": sum(v.metrics.burn_rate for v in self.ventures)
            }
        }

class VentureStrategy(ReasoningStrategy):
    """
    Advanced venture strategy that combines multiple specialized strategies
    to generate comprehensive business plans and recommendations.
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize venture strategy with component strategies."""
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
        
        # Initialize component strategies with shared config
        strategy_config = {
            'min_confidence': self.min_confidence,
            'parallel_threshold': self.parallel_threshold,
            'learning_rate': self.learning_rate,
            'strategy_weights': self.strategy_weights
        }
        
        self.strategies = {
            VentureType.AI_STARTUP: AIStartupStrategy(strategy_config),
            VentureType.SAAS: SaaSVentureStrategy(strategy_config),
            VentureType.AUTOMATION_SERVICE: AutomationVentureStrategy(strategy_config),
            VentureType.DATA_ANALYTICS: DataVentureStrategy(strategy_config),
            VentureType.API_SERVICE: APIVentureStrategy(strategy_config),
            VentureType.MARKETPLACE: MarketplaceVentureStrategy(strategy_config)
        }
        
        # Portfolio strategy for multi-venture optimization
        self.portfolio_strategy = VenturePortfolioStrategy(strategy_config)
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Generate venture strategy based on query and context.
        
        Args:
            query: The venture strategy query
            context: Additional context and parameters
            
        Returns:
            Dict containing venture strategy and confidence scores
        """
        try:
            # Determine venture type from query/context
            venture_type = self._determine_venture_type(query, context)
            
            # Get strategy for venture type
            strategy = self.strategies.get(venture_type)
            if not strategy:
                raise ValueError(f"Unsupported venture type: {venture_type}")
            
            # Generate strategy
            strategy_result = await strategy.reason(query, context)
            
            # Get portfolio analysis
            portfolio_result = await self.portfolio_strategy.reason(query, context)
            
            # Combine results
            combined_result = self._combine_results(
                strategy_result,
                portfolio_result,
                venture_type
            )
            
            return {
                'answer': self._format_strategy(combined_result),
                'confidence': combined_result.get('confidence', 0.0),
                'venture_type': venture_type.value,
                'strategy': strategy_result,
                'portfolio_analysis': portfolio_result
            }
            
        except Exception as e:
            logging.error(f"Venture strategy generation failed: {str(e)}")
            return {
                'error': f"Venture strategy generation failed: {str(e)}",
                'confidence': 0.0
            }
    
    def _determine_venture_type(self, query: str, context: Dict[str, Any]) -> VentureType:
        """Determine venture type from query and context."""
        # Use context if available
        if 'venture_type' in context:
            return VentureType(context['venture_type'])
        
        # Simple keyword matching
        query_lower = query.lower()
        if any(term in query_lower for term in ['ai', 'ml', 'model', 'neural']):
            return VentureType.AI_STARTUP
        elif any(term in query_lower for term in ['saas', 'software', 'cloud']):
            return VentureType.SAAS
        elif any(term in query_lower for term in ['automate', 'automation', 'workflow']):
            return VentureType.AUTOMATION_SERVICE
        elif any(term in query_lower for term in ['data', 'analytics', 'insights']):
            return VentureType.DATA_ANALYTICS
        elif any(term in query_lower for term in ['api', 'service', 'endpoint']):
            return VentureType.API_SERVICE
        elif any(term in query_lower for term in ['marketplace', 'platform', 'network']):
            return VentureType.MARKETPLACE
        
        # Default to AI startup if unclear
        return VentureType.AI_STARTUP
    
    def _combine_results(
        self,
        strategy_result: Dict[str, Any],
        portfolio_result: Dict[str, Any],
        venture_type: VentureType
    ) -> Dict[str, Any]:
        """Combine strategy and portfolio results."""
        return {
            'venture_type': venture_type.value,
            'strategy': strategy_result.get('strategy', {}),
            'metrics': strategy_result.get('metrics', {}),
            'portfolio_fit': portfolio_result.get('portfolio_fit', {}),
            'recommendations': strategy_result.get('recommendations', []),
            'confidence': min(
                strategy_result.get('confidence', 0.0),
                portfolio_result.get('confidence', 0.0)
            )
        }
    
    def _format_strategy(self, result: Dict[str, Any]) -> str:
        """Format venture strategy into readable text."""
        sections = []
        
        # Venture type
        sections.append(f"Venture Type: {result['venture_type'].replace('_', ' ').title()}")
        
        # Strategy overview
        if 'strategy' in result:
            strategy = result['strategy']
            sections.append("\nStrategy Overview:")
            for key, value in strategy.items():
                sections.append(f"- {key.replace('_', ' ').title()}: {value}")
        
        # Key metrics
        if 'metrics' in result:
            metrics = result['metrics']
            sections.append("\nKey Metrics:")
            for key, value in metrics.items():
                if isinstance(value, (int, float)):
                    sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}")
                else:
                    sections.append(f"- {key.replace('_', ' ').title()}: {value}")
        
        # Portfolio fit
        if 'portfolio_fit' in result:
            fit = result['portfolio_fit']
            sections.append("\nPortfolio Analysis:")
            for key, value in fit.items():
                sections.append(f"- {key.replace('_', ' ').title()}: {value}")
        
        # Recommendations
        if 'recommendations' in result:
            recs = result['recommendations']
            sections.append("\nKey Recommendations:")
            for rec in recs:
                sections.append(f"- {rec}")
        
        return "\n".join(sections)