"""
Decision Agent Module
Future implementation for AI-powered decision making using LangChain

This module will handle:
- Intelligent problem analysis
- Strategy selection (brute force vs optimized)
- Approach recommendation based on problem characteristics
- Learning from past submissions
"""

from typing import Dict, Any, Optional, List
from enum import Enum

from core.logger import logger


class ProblemDifficulty(Enum):
    """Problem difficulty levels"""
    EASY = "Easy"
    MEDIUM = "Medium"
    HARD = "Hard"


class ApproachType(Enum):
    """Solution approach types"""
    BRUTE_FORCE = "brute_force"
    OPTIMIZED = "optimized"
    DYNAMIC_PROGRAMMING = "dynamic_programming"
    GREEDY = "greedy"
    GRAPH = "graph"
    TREE = "tree"
    BACKTRACKING = "backtracking"


class DecisionAgent:
    """
    AI-powered decision agent for LeetCode automation
    
    Future implementation will integrate:
    - LangChain for reasoning chains
    - Vector stores for problem similarity search
    - RAG for retrieving similar solved problems
    - Learning from submission feedback
    """
    
    def __init__(self):
        logger.info("Initializing Decision Agent (placeholder)")
        # TODO: Initialize LangChain components
        # self.llm = ChatOpenAI(...)
        # self.vector_store = ...
        # self.memory = ConversationBufferMemory(...)
    
    def analyze_problem(self, problem: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze problem and recommend approach
        
        Args:
            problem: Problem data from scraper
            
        Returns:
            Analysis results with recommended approach
            
        Future implementation:
        - Extract problem patterns (array, string, tree, etc.)
        - Identify algorithmic patterns
        - Search for similar problems
        - Recommend optimal approach
        """
        logger.info(f"Analyzing problem: {problem.get('title')}")
        
        # Placeholder analysis
        difficulty = problem.get('difficulty', 'Unknown')
        topics = [tag['name'] for tag in problem.get('topicTags', [])]
        
        analysis = {
            'difficulty': difficulty,
            'topics': topics,
            'recommended_approach': self._recommend_approach_placeholder(topics, difficulty),
            'estimated_complexity': 'O(n)',
            'confidence': 0.7
        }
        
        logger.info(f"Analysis complete: {analysis['recommended_approach']}")
        return analysis
    
    def _recommend_approach_placeholder(self, topics: List[str], difficulty: str) -> str:
        """
        Placeholder approach recommendation
        
        Future: Use LangChain reasoning chain to analyze topics and recommend
        """
        topic_lower = [t.lower() for t in topics]
        
        if 'dynamic programming' in topic_lower:
            return ApproachType.DYNAMIC_PROGRAMMING.value
        elif 'greedy' in topic_lower:
            return ApproachType.GREEDY.value
        elif any(t in topic_lower for t in ['tree', 'graph']):
            return ApproachType.TREE.value if 'tree' in topic_lower else ApproachType.GRAPH.value
        elif difficulty == 'Easy':
            return ApproachType.BRUTE_FORCE.value
        else:
            return ApproachType.OPTIMIZED.value
    
    def should_retry_with_different_approach(
        self,
        previous_attempts: int,
        feedback: str
    ) -> bool:
        """
        Decide if different approach should be tried
        
        Args:
            previous_attempts: Number of previous attempts
            feedback: Feedback from previous submission
            
        Returns:
            True if should retry with different approach
            
        Future: Use LangChain to analyze feedback and decide strategy
        """
        # Placeholder logic
        if previous_attempts >= 3:
            return False
        
        # Check for specific failure patterns
        failure_keywords = ['timeout', 'time limit', 'too slow']
        if any(keyword in feedback.lower() for keyword in failure_keywords):
            logger.info("Detected performance issue, recommending optimization")
            return True
        
        return previous_attempts < 2
    
    def generate_feedback_prompt(self, error: str, code: str) -> str:
        """
        Generate improved prompt based on error
        
        Args:
            error: Error message from submission
            code: Previously generated code
            
        Returns:
            Enhanced prompt for next attempt
            
        Future: Use LangChain prompt templates and error analysis
        """
        feedback_parts = [
            "The previous solution failed with the following error:",
            f"\n{error}\n",
            "Please provide an improved solution that:",
            "1. Addresses the specific error mentioned",
            "2. Optimizes time and space complexity",
            "3. Handles edge cases properly",
            "4. Follows best practices"
        ]
        
        return "\n".join(feedback_parts)
    
    def learn_from_submission(
        self,
        problem: Dict[str, Any],
        code: str,
        success: bool,
        feedback: Optional[str] = None
    ):
        """
        Learn from submission results
        
        Args:
            problem: Problem data
            code: Submitted code
            success: Whether submission succeeded
            feedback: Optional feedback/error message
            
        Future implementation:
        - Store successful solutions in vector database
        - Build knowledge base of problem patterns
        - Improve approach recommendations over time
        - Use reinforcement learning for strategy selection
        """
        logger.info(f"Recording submission result: {'SUCCESS' if success else 'FAILURE'}")
        
        # TODO: Implement learning mechanism
        # - Save to vector store
        # - Update problem similarity index
        # - Adjust confidence scores
        
        pass


# Placeholder for future LangChain integration examples:

"""
Example LangChain Integration:

from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

class AdvancedDecisionAgent(DecisionAgent):
    def __init__(self):
        super().__init__()
        
        # Initialize LLM
        self.llm = ChatOpenAI(
            model_name="gpt-4",
            temperature=0.3
        )
        
        # Problem analysis chain
        self.analysis_prompt = PromptTemplate(
            input_variables=["problem", "topics"],
            template="Analyze this LeetCode problem and recommend optimal approach..."
        )
        self.analysis_chain = LLMChain(
            llm=self.llm,
            prompt=self.analysis_prompt
        )
        
        # Vector store for similar problems
        self.embeddings = OpenAIEmbeddings()
        self.vector_store = Chroma(
            embedding_function=self.embeddings,
            persist_directory="./problem_db"
        )
        
        # Memory for context
        self.memory = ConversationBufferMemory()
    
    def find_similar_problems(self, problem_description: str, k: int = 3):
        results = self.vector_store.similarity_search(
            problem_description,
            k=k
        )
        return results
"""