"""
MarketAnalysisAgent for Patent Wake-Up Scenario

Analyzes market opportunities for patented technologies:
- Identifies relevant industry sectors
- Assesses market size and growth potential
- Analyzes competitive landscape
- Recommends geographic focus (EU, Canada)
"""

from typing import Optional
from loguru import logger
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import JsonOutputParser

from ..base_agent import BaseAgent, Task
from ...llm.langchain_ollama_client import LangChainOllamaClient
from ...workflow.langgraph_state import PatentAnalysis, MarketAnalysis, MarketOpportunity


class MarketAnalysisAgent(BaseAgent):
    """
    Specialized agent for market opportunity analysis.
    Uses research tools and LLM reasoning to assess commercialization potential.
    """

    def __init__(self, llm_client: LangChainOllamaClient, memory_agent=None):
        """
        Initialize MarketAnalysisAgent.

        Args:
            llm_client: LangChain Ollama client
            memory_agent: Optional memory agent for context retrieval
        """
        # Note: MarketAnalysisAgent uses LangChain directly
        self.name = "MarketAnalysisAgent"
        self.description = "Market opportunity analysis and assessment"

        self.llm_client = llm_client
        self.memory_agent = memory_agent

        # Use analysis complexity for market research
        self.llm = llm_client.get_llm('analysis')  # mistral:latest

        # Create analysis chain
        self.analysis_chain = self._create_analysis_chain()

        logger.info("Initialized MarketAnalysisAgent")

    def _create_analysis_chain(self):
        """Create chain for market analysis"""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an expert market analyst specializing in technology commercialization."),
            ("human", """
Analyze market opportunities for this technology:

Title: {title}
Technical Domains: {technical_domains}
Key Innovations: {key_innovations}
TRL: {trl_level}
Potential Applications: {potential_applications}

Provide comprehensive market analysis in JSON format:
{{
    "opportunities": [
        {{
            "sector": "Healthcare/Pharmaceuticals",
            "sector_description": "Brief description",
            "market_size_usd": 50000000000,
            "growth_rate_percent": 8.5,
            "technology_fit": "Excellent/Good/Fair",
            "market_gap": "Specific gap this fills",
            "competitive_advantage": "Key advantages",
            "geographic_focus": ["EU", "Canada", "US"],
            "time_to_market_months": 18,
            "risk_level": "Low/Medium/High",
            "priority_score": 0.9
        }}
    ],
    "total_addressable_market_usd": 150000000000,
    "market_readiness": "Ready/Emerging/Early",
    "competitive_landscape": "Assessment of competition",
    "regulatory_considerations": ["FDA approval required", "GDPR compliance"],
    "recommended_focus": "Primary market focus",
    "strategic_positioning": "How to position technology",
    "go_to_market_strategy": "Recommended strategy",
    "confidence_score": 0.85,
    "research_depth": 5
}}

Requirements:
1. Identify 3-5 relevant market opportunities
2. Prioritize EU and Canada markets (VISTA focus)
3. Assess realistic market sizes and growth
4. Consider regulatory environment
5. Evaluate time-to-market for each opportunity
6. Rank by priority_score (0-1)

Return ONLY valid JSON.
""")
        ])

        parser = JsonOutputParser()
        return prompt | self.llm | parser

    async def analyze_market(self, patent_analysis: PatentAnalysis) -> MarketAnalysis:
        """
        Analyze market opportunities based on patent analysis.

        Args:
            patent_analysis: Output from DocumentAnalysisAgent

        Returns:
            MarketAnalysis with identified opportunities
        """
        logger.info(f"📊 Analyzing market for: {patent_analysis.title}")

        # Retrieve relevant market data from memory if available
        context = None
        if self.memory_agent:
            try:
                context = await self.memory_agent.retrieve_relevant_context(
                    query=f"market analysis {' '.join(patent_analysis.technical_domains)}",
                    context_type="semantic",
                    top_k=3
                )
                if context:
                    logger.debug(f"Retrieved {len(context)} market context documents")
            except Exception as e:
                logger.warning(f"Memory retrieval failed: {e}")

        # Optionally perform web searches for market data
        # (Would use web_search_tool here in production)

        # Execute market analysis
        logger.info("Generating market opportunity analysis...")
        result = await self.analysis_chain.ainvoke({
            "title": patent_analysis.title,
            "technical_domains": ", ".join(patent_analysis.technical_domains),
            "key_innovations": ", ".join(patent_analysis.key_innovations),
            "trl_level": patent_analysis.trl_level,
            "potential_applications": ", ".join(patent_analysis.potential_applications)
        })

        # Build MarketAnalysis object
        analysis = self._build_market_analysis(result)

        logger.success(f"✅ Market analysis complete: {len(analysis.opportunities)} opportunities identified")

        return analysis

    def _build_market_analysis(self, result: dict) -> MarketAnalysis:
        """
        Build MarketAnalysis object from LLM output.

        Args:
            result: JSON output from analysis chain

        Returns:
            Complete MarketAnalysis object
        """
        # Convert opportunities to MarketOpportunity objects
        # Set market_size_usd to None for demo (early stage)
        opportunities = []
        for opp in result.get('opportunities', []):
            opp_dict = dict(opp)
            opp_dict['market_size_usd'] = None  # Placeholder for demo
            opportunities.append(MarketOpportunity(**opp_dict))

        # Sort by priority score
        opportunities.sort(key=lambda x: x.priority_score, reverse=True)

        # Extract top sectors
        top_sectors = [opp.sector for opp in opportunities[:3]]

        return MarketAnalysis(
            opportunities=opportunities,
            top_sectors=top_sectors,
            total_addressable_market_usd=None,  # Set to None for demo (early stage)
            market_readiness=result.get('market_readiness', 'Emerging'),
            competitive_landscape=result.get('competitive_landscape', 'Competitive analysis unavailable'),
            regulatory_considerations=result.get('regulatory_considerations', []),
            recommended_focus=result.get('recommended_focus', top_sectors[0] if top_sectors else 'General Market'),
            strategic_positioning=result.get('strategic_positioning', 'Position as innovative solution'),
            go_to_market_strategy=result.get('go_to_market_strategy', 'Partner with established players'),
            confidence_score=result.get('confidence_score', 0.8),
            research_depth=result.get('research_depth', 3)
        )

    async def process_task(self, task: Task) -> Task:
        """
        Process task using agent interface.

        Args:
            task: Task with patent_analysis in metadata

        Returns:
            Task with MarketAnalysis result
        """
        task.status = "in_progress"

        try:
            patent_analysis_dict = task.metadata.get('patent_analysis')
            if not patent_analysis_dict:
                raise ValueError("patent_analysis required in task metadata")

            # Convert dict to PatentAnalysis
            patent_analysis = PatentAnalysis(**patent_analysis_dict)

            analysis = await self.analyze_market(patent_analysis)

            task.result = analysis.model_dump()
            task.status = "completed"

        except Exception as e:
            logger.error(f"Market analysis failed: {e}")
            task.status = "failed"
            task.error = str(e)

        return task