import random
import os

class CognitiveEngine:
    def identify_improvements(self, task):
        issues = []
        if random.random() > 0.7:
            issues.append("Memory optimization")
        if random.random() > 0.8:
            issues.append("Error handling enhancement")
        if "search" in task:
            issues.append("Information retrieval accuracy")
        return issues
    
    def generate_enhancements(self, targets):
        enhancements = []
        for target in targets:
            if "memory" in target.lower():
                enhancements.append("Optimized memory usage with caching")
            elif "error" in target.lower():
                enhancements.append("Added comprehensive error handling")
            elif "retrieval" in target.lower():
                enhancements.append("Improved search relevance algorithms")
        return enhancements
    
    def apply_enhancements(self, enhancements):
        # In a real system, this would modify the codebase
        # For simulation, we'll just log the changes
        print(f"Applying enhancements: {enhancements}")
        with open("log.txt", "a") as log_file:
            log_file.write(f"System enhancements applied: {enhancements}\n")
        return True
    
    def generate_code(self, task, context):
        return f'''# Generated code for: {task}
import requests

def main():
    """Autonomously generated function"""
    print("Hello from AI-generated code!")
    print(f"Task context: {str(context)[:100]}...")
    
    # TODO: Implement actual functionality
    return "Task completed successfully"

if __name__ == "__main__":
    main()
'''
    
    def improve_code(self, code, review):
        return f'''# Improved code based on review: "{review}"
{code}

# Added error handling based on review
try:
    # Existing implementation
    pass
except Exception as e:
    print(f"Error occurred: {{str(e)}}")
    # Additional error recovery logic
'''