#!/usr/bin/env python3
"""
Thinking Model Processor for Enhanced Decision Making

This script implements an advanced thinking model designed to improve
decision-making speed and accuracy. It integrates with memory systems
to compare and integrate previous thinking models for continuous enhancement.
"""

import json
import subprocess
import sys
import os
import time
from typing import Dict, List, Any, Optional


class ThinkingModelProcessor:
    def __init__(self):
        self.speed_factors = [
            "parallel_processing",
            "early_elimination", 
            "pattern_recognition",
            "heuristic_shortcuts",
            "focused_analysis"
        ]
        
        self.accuracy_factors = [
            "multi_perspective",
            "evidence_weighting",
            "cross_reference",
            "assumption_checking",
            "confidence_assessment"
        ]
    
    def retrieve_memory_models(self) -> List[Dict[str, Any]]:
        """
        Retrieve relevant past thinking models from memory system
        """
        print("Retrieving relevant thinking models from memory...")
        
        # Simulate memory retrieval
        # In a real implementation, this would connect to the actual memory system
        memory_models = [
            {
                "id": "model_001",
                "name": "Basic Analysis Model",
                "strengths": ["structured", "comprehensive"],
                "weaknesses": ["slow", "overcomplicated"],
                "accuracy_rate": 0.85,
                "avg_time_seconds": 120
            },
            {
                "id": "model_002", 
                "name": "Quick Decision Model",
                "strengths": ["fast", "efficient"],
                "weaknesses": ["less accurate", "misses nuances"],
                "accuracy_rate": 0.70,
                "avg_time_seconds": 30
            },
            {
                "id": "model_003",
                "name": "Risk-Averse Model",
                "strengths": ["thorough", "conservative"],
                "weaknesses": ["overcautious", "time-consuming"],
                "accuracy_rate": 0.90,
                "avg_time_seconds": 180
            }
        ]
        
        return memory_models
    
    def analyze_input(self, problem_description: str) -> Dict[str, Any]:
        """
        Analyze the input problem to determine complexity and requirements
        """
        print(f"Analyzing input: {problem_description[:50]}...")
        
        # Determine complexity based on input characteristics
        complexity_score = min(len(problem_description.split()), 10) / 10.0
        has_multiple_options = "or" in problem_description.lower() or "," in problem_description
        time_sensitive = any(word in problem_description.lower() for word in ["urgent", "quick", "fast", "soon"])
        
        analysis = {
            "complexity_level": "low" if complexity_score < 0.3 else "medium" if complexity_score < 0.7 else "high",
            "complexity_score": complexity_score,
            "has_multiple_options": has_multiple_options,
            "time_sensitive": time_sensitive,
            "recommended_accuracy": 0.85 if time_sensitive else 0.95
        }
        
        return analysis
    
    def select_thinking_model(self, analysis: Dict[str, Any], memory_models: List[Dict[str, Any]]) -> Dict[str, Any]:
        """
        Select appropriate thinking model based on problem analysis
        """
        print("Selecting optimal thinking model...")
        
        # For time-sensitive problems, prioritize speed
        if analysis["time_sensitive"]:
            # Find fastest model that meets minimum accuracy
            for model in sorted(memory_models, key=lambda x: x["avg_time_seconds"]):
                if model["accuracy_rate"] >= analysis["recommended_accuracy"] * 0.8:
                    return model
        
        # For complex problems, prioritize accuracy
        if analysis["complexity_level"] == "high":
            # Find most accurate model
            return max(memory_models, key=lambda x: x["accuracy_rate"])
        
        # For medium complexity, balance speed and accuracy
        balanced_model = min(
            [m for m in memory_models if m["accuracy_rate"] >= analysis["recommended_accuracy"]],
            key=lambda x: x["avg_time_seconds"] * (1.5 - x["accuracy_rate"]),
            default=memory_models[0]  # fallback to first model
        )
        
        return balanced_model
    
    def enhance_model_with_memory(self, current_model: Dict[str, Any], memory_models: List[Dict[str, Any]]) -> Dict[str, Any]:
        """
        Enhance current model by integrating elements from memory models
        """
        print("Enhancing model with memory-integrated elements...")
        
        # Identify the best elements from all models
        best_elements = {
            "speed_element": min(memory_models, key=lambda x: x["avg_time_seconds"]),
            "accuracy_element": max(memory_models, key=lambda x: x["accuracy_rate"]),
            "balanced_element": min(
                memory_models, 
                key=lambda x: x["avg_time_seconds"] * (1.5 - x["accuracy_rate"])
            )
        }
        
        # Create enhanced model combining best elements
        enhanced_model = {
            "id": f"enhanced_{current_model['id']}",
            "name": f"Enhanced {current_model['name']}",
            "original_model": current_model,
            "incorporated_elements": best_elements,
            "improvements": [],
            "predicted_performance": {
                "accuracy_rate": min(0.98, current_model["accuracy_rate"] + 0.05),
                "avg_time_seconds": max(10, current_model["avg_time_seconds"] * 0.85)
            }
        }
        
        # Add specific improvements based on best elements
        if best_elements["speed_element"]["id"] != current_model["id"]:
            enhanced_model["improvements"].append({
                "type": "speed_enhancement",
                "from_model": best_elements["speed_element"]["name"],
                "expected_time_reduction": f"{current_model['avg_time_seconds'] - best_elements['speed_element']['avg_time_seconds']}s"
            })
        
        if best_elements["accuracy_element"]["id"] != current_model["id"]:
            enhanced_model["improvements"].append({
                "type": "accuracy_enhancement", 
                "from_model": best_elements["accuracy_element"]["name"],
                "expected_accuracy_gain": f"{best_elements['accuracy_element']['accuracy_rate'] - current_model['accuracy_rate']:.2f}"
            })
        
        return enhanced_model
    
    def execute_thinking_process(self, problem_description: str) -> Dict[str, Any]:
        """
        Execute the complete thinking process with speed and accuracy optimizations
        """
        print(f"Starting enhanced thinking process for: {problem_description}")
        
        # Step 1: Retrieve memory models
        memory_models = self.retrieve_memory_models()
        
        # Step 2: Analyze input
        analysis = self.analyze_input(problem_description)
        
        # Step 3: Select appropriate model
        selected_model = self.select_thinking_model(analysis, memory_models)
        
        # Step 4: Enhance model with memory integration
        enhanced_model = self.enhance_model_with_memory(selected_model, memory_models)
        
        # Step 5: Execute processing stages
        start_time = time.time()
        
        # Stage 1: Rapid Assessment
        print("Stage 1: Rapid Assessment")
        rapid_assessment = self.rapid_assessment(problem_description)
        
        # Stage 2: Detailed Analysis  
        print("Stage 2: Detailed Analysis")
        detailed_analysis = self.detailed_analysis(problem_description, rapid_assessment)
        
        # Stage 3: Cross-Validation
        print("Stage 3: Cross-Validation")
        cross_validation = self.cross_validation(detailed_analysis)
        
        # Stage 4: Optimization
        print("Stage 4: Optimization")
        optimization = self.optimization(cross_validation, analysis)
        
        # Stage 5: Integration
        print("Stage 5: Integration with memory models")
        integration = self.integration(optimization, enhanced_model)
        
        end_time = time.time()
        total_time = round(end_time - start_time, 2)
        
        # Compile results
        result = {
            "problem": problem_description,
            "analysis": analysis,
            "selected_model": selected_model,
            "enhanced_model": enhanced_model,
            "processing_stages": {
                "rapid_assessment": rapid_assessment,
                "detailed_analysis": detailed_analysis,
                "cross_validation": cross_validation,
                "optimization": optimization,
                "integration": integration
            },
            "performance_metrics": {
                "total_time_seconds": total_time,
                "predicted_accuracy": enhanced_model["predicted_performance"]["accuracy_rate"],
                "efficiency_ratio": total_time / enhanced_model["predicted_performance"]["avg_time_seconds"]
            },
            "recommendations": self.generate_recommendations(integration, analysis)
        }
        
        return result
    
    def rapid_assessment(self, problem: str) -> Dict[str, Any]:
        """Quick preliminary evaluation"""
        # Implement rapid assessment logic
        return {
            "initial_classification": "analytical" if "analyze" in problem.lower() else "decision" if "should" in problem.lower() else "informational",
            "key_elements_identified": len(problem.split()) // 3,
            "preliminary_confidence": 0.6
        }
    
    def detailed_analysis(self, problem: str, rapid_result: Dict[str, Any]) -> Dict[str, Any]:
        """In-depth examination of options"""
        # Implement detailed analysis logic
        return {
            **rapid_result,
            "deep_factors": ["factor_1", "factor_2", "factor_3"],
            "weighted_importance": {"factor_1": 0.4, "factor_2": 0.3, "factor_3": 0.3},
            "complexity_assessment": "moderate"
        }
    
    def cross_validation(self, detailed_result: Dict[str, Any]) -> Dict[str, Any]:
        """Verification against multiple criteria"""
        # Implement cross-validation logic
        return {
            **detailed_result,
            "validation_passed": True,
            "criteria_checked": ["logical_consistency", "evidence_support", "feasibility"],
            "confidence_boost": 0.1
        }
    
    def optimization(self, validation_result: Dict[str, Any], analysis: Dict[str, Any]) -> Dict[str, Any]:
        """Refinement based on goals"""
        # Implement optimization logic
        return {
            **validation_result,
            "optimized_for": "accuracy" if analysis["recommended_accuracy"] > 0.9 else "balance",
            "refinements_applied": ["prioritization", "weight_adjustment"],
            "final_confidence": min(0.98, validation_result.get("preliminary_confidence", 0.6) + validation_result.get("confidence_boost", 0))
        }
    
    def integration(self, optimization_result: Dict[str, Any], enhanced_model: Dict[str, Any]) -> Dict[str, Any]:
        """Combine with memory-stored models"""
        # Implement integration logic
        return {
            **optimization_result,
            "integrated_with_model": enhanced_model["name"],
            "memory_elements_incorporated": len(enhanced_model.get("improvements", [])),
            "enhanced_confidence": min(0.99, optimization_result["final_confidence"] + 0.1)
        }
    
    def generate_recommendations(self, integration_result: Dict[str, Any], analysis: Dict[str, Any]) -> List[str]:
        """Generate actionable recommendations"""
        recommendations = []
        
        if analysis["time_sensitive"]:
            recommendations.append("Prioritize speed while maintaining acceptable accuracy")
        
        if analysis["complexity_level"] == "high":
            recommendations.append("Consider breaking down into smaller sub-problems")
        
        recommendations.append(f"Achieved confidence level: {integration_result.get('enhanced_confidence', 0.8):.1%}")
        
        return recommendations


def main():
    """
    Main function to handle thinking model requests.
    """
    if len(sys.argv) < 2:
        print("Usage: python thinking_model_processor.py '<problem_description>'")
        print("Example: python thinking_model_processor.py 'Should I invest in this opportunity?'")
        sys.exit(1)
    
    problem_description = sys.argv[1]
    
    try:
        processor = ThinkingModelProcessor()
        result = processor.execute_thinking_process(problem_description)
        
        print("\n" + "="*70)
        print("THINKING MODEL PROCESSING COMPLETE")
        print("="*70)
        print(f"Problem: {result['problem']}")
        print(f"Total Time: {result['performance_metrics']['total_time_seconds']}s")
        print(f"Predicted Accuracy: {result['performance_metrics']['predicted_accuracy']:.1%}")
        print(f"Efficiency Ratio: {result['performance_metrics']['efficiency_ratio']:.2f}")
        print("-"*70)
        print("RECOMMENDATIONS:")
        for rec in result["recommendations"]:
            print(f"- {rec}")
        print("="*70)
        
        # In a real implementation, we would store the result in memory here
        print("\nResult would be stored in memory system for future reference.")
        
        return 0
        
    except Exception as e:
        print(f"Error executing thinking model process: {str(e)}")
        import traceback
        traceback.print_exc()
        return 1


if __name__ == "__main__":
    exit(main())