"""
OutreachAgent for Patent Wake-Up Scenario

Generates valorization materials and outreach communications:
- Comprehensive valorization briefs (PDF)
- Executive summaries
- Stakeholder-specific outreach materials
"""

from typing import List
import os
from datetime import datetime
from loguru import logger
from langchain_core.prompts import ChatPromptTemplate

from ..base_agent import BaseAgent, Task
from ...llm.langchain_ollama_client import LangChainOllamaClient
from ...workflow.langgraph_state import (
    PatentAnalysis,
    MarketAnalysis,
    StakeholderMatch,
    ValorizationBrief
)


class OutreachAgent(BaseAgent):
    """
    Specialized agent for generating valorization materials.
    Creates briefs, summaries, and outreach communications.
    """

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

        Args:
            llm_client: LangChain Ollama client
            memory_agent: Optional memory agent
        """
        # Note: OutreachAgent uses LangChain directly
        self.name = "OutreachAgent"
        self.description = "Valorization brief and outreach generation"

        self.llm_client = llm_client
        self.memory_agent = memory_agent

        # Use standard model for document generation
        self.llm = llm_client.get_llm('standard')  # llama3.1:8b

        # Create generation chains
        self.brief_chain = self._create_brief_chain()
        self.summary_chain = self._create_summary_chain()

        # Ensure outputs directory exists
        os.makedirs("outputs", exist_ok=True)

        logger.info("Initialized OutreachAgent")

    def _create_brief_chain(self):
        """Create chain for valorization brief generation"""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an expert in technology commercialization and professional business writing."),
            ("human", """
Create a comprehensive valorization brief for this patent.

PATENT ANALYSIS:
Title: {patent_title}
TRL: {trl_level}/9
Key Innovations:
{key_innovations}
Potential Applications:
{applications}

MARKET OPPORTUNITIES:
{market_opportunities}

TOP STAKEHOLDER MATCHES:
{stakeholder_matches}

Create a professional valorization brief in markdown format with:

# Valorization Brief: [Patent Title]

## Executive Summary
[1-paragraph overview highlighting commercialization potential]

## Technology Overview
### Key Innovations
[Bullet points of key innovations]

### Technology Readiness
[TRL assessment and readiness for commercialization]

### Technical Advantages
[What makes this technology unique]

## Market Opportunity Analysis
### Target Sectors
[Top 3-5 sectors with market size data]

### Market Gaps Addressed
[Specific problems this solves]

### Competitive Positioning
[How to position vs. alternatives]

## Recommended Partners
[Top 5 stakeholders with match rationale]

## Commercialization Roadmap
### Immediate Next Steps (0-6 months)
[Specific actions]

### Medium-term Goals (6-18 months)
[Development milestones]

### Long-term Vision (18+ months)
[Market expansion]

## Key Takeaways
[3-5 bullet points with main insights]

Write professionally but accessibly. Use specific numbers and data where available.
""")
        ])

        return prompt | self.llm

    def _create_summary_chain(self):
        """Create chain for executive summary extraction"""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You extract concise executive summaries from longer documents."),
            ("human", "Extract a 2-3 sentence executive summary from this brief:\n\n{brief_content}")
        ])

        return prompt | self.llm

    async def create_valorization_brief(
        self,
        patent_analysis: PatentAnalysis,
        market_analysis: MarketAnalysis,
        matches: List[StakeholderMatch]
    ) -> ValorizationBrief:
        """
        Generate comprehensive valorization brief.

        Args:
            patent_analysis: Patent technical analysis
            market_analysis: Market opportunities
            matches: Stakeholder matches

        Returns:
            ValorizationBrief with content and PDF path
        """
        logger.info(f"📝 Creating valorization brief for: {patent_analysis.title}")

        # Format data for brief generation
        key_innovations = "\n".join([f"- {inn}" for inn in patent_analysis.key_innovations])
        applications = "\n".join([f"- {app}" for app in patent_analysis.potential_applications])

        market_opps = "\n\n".join([
            f"**{opp.sector}** ({opp.technology_fit} fit)\n"
            f"- Market Size: {f'${opp.market_size_usd/1e9:.1f}B USD' if opp.market_size_usd is not None else 'NaN'}\n"
            f"- Growth: {f'{opp.growth_rate_percent}% annually' if opp.growth_rate_percent is not None else 'NaN'}\n"
            f"- Gap: {opp.market_gap}"
            for opp in market_analysis.opportunities[:5]
        ])

        stakeholder_text = "\n\n".join([
            f"{i+1}. **{m.stakeholder_name}** ({m.stakeholder_type})\n"
            f"   - Location: {m.location}\n"
            f"   - Fit Score: {m.overall_fit_score:.2f}\n"
            f"   - Why: {m.match_rationale[:200]}..."
            for i, m in enumerate(matches[:5])
        ])

        # Generate brief content
        logger.info("Generating brief content...")
        content_response = await self.brief_chain.ainvoke({
            "patent_title": patent_analysis.title,
            "trl_level": patent_analysis.trl_level,
            "key_innovations": key_innovations,
            "applications": applications,
            "market_opportunities": market_opps,
            "stakeholder_matches": stakeholder_text
        })

        content = content_response.content

        # Extract executive summary
        logger.info("Extracting executive summary...")
        summary_response = await self.summary_chain.ainvoke({
            "brief_content": content[:2000]  # First part only
        })
        executive_summary = summary_response.content

        # Generate PDF
        pdf_path = await self._generate_pdf(
            content=content,
            patent_id=patent_analysis.patent_id,
            title=patent_analysis.title
        )

        # Build ValorizationBrief
        brief = ValorizationBrief(
            patent_id=patent_analysis.patent_id,
            content=content,
            pdf_path=pdf_path,
            executive_summary=executive_summary,
            technology_overview=self._extract_section(content, "Technology Overview"),
            market_analysis_summary=self._extract_section(content, "Market Opportunity"),
            partner_recommendations=self._extract_section(content, "Recommended Partners"),
            top_opportunities=market_analysis.top_sectors,
            recommended_partners=[m.stakeholder_name for m in matches[:5]],
            key_takeaways=self._extract_takeaways(content),
            generated_date=datetime.now().strftime("%Y-%m-%d"),
            version="1.0"
        )

        logger.success(f"✅ Valorization brief created: {pdf_path}")

        return brief

    async def _generate_pdf(self, content: str, patent_id: str, title: str) -> str:
        """
        Generate PDF from markdown content.

        Args:
            content: Markdown content
            patent_id: Patent identifier
            title: Brief title

        Returns:
            Path to generated PDF
        """
        try:
            from ...tools.langchain_tools import document_generator_tool

            # Create filename
            filename = f"valorization_brief_{patent_id}_{datetime.now().strftime('%Y%m%d')}.pdf"
            pdf_path = os.path.join("outputs", filename)

            # Generate PDF
            await document_generator_tool.ainvoke({
                "output_path": pdf_path,
                "title": f"Valorization Brief: {title}",
                "content": content,
                "author": "SPARKNET Valorization System"
            })

            return pdf_path

        except Exception as e:
            logger.error(f"PDF generation failed: {e}")
            # Fallback: save as markdown
            md_path = pdf_path.replace('.pdf', '.md')
            with open(md_path, 'w', encoding='utf-8') as f:
                f.write(content)
            logger.warning(f"Saved as markdown instead: {md_path}")
            return md_path

    def _extract_section(self, content: str, section_name: str) -> str:
        """Extract a specific section from markdown content"""
        import re

        # Find section using markdown headers
        pattern = rf'##\s+{section_name}.*?\n(.*?)(?=##|\Z)'
        match = re.search(pattern, content, re.DOTALL | re.IGNORECASE)

        if match:
            return match.group(1).strip()[:500]  # Limit length
        return "Section not found"

    def _extract_takeaways(self, content: str) -> List[str]:
        """Extract key takeaways from content"""
        import re

        # Look for Key Takeaways section
        pattern = r'##\s+Key Takeaways.*?\n(.*?)(?=##|\Z)'
        match = re.search(pattern, content, re.DOTALL | re.IGNORECASE)

        if match:
            takeaways_text = match.group(1)
            # Extract bullet points
            bullets = re.findall(r'[-*]\s+(.+)', takeaways_text)
            return bullets[:5]

        # Fallback: create generic takeaways
        return [
            "Technology demonstrates strong commercialization potential",
            "Multiple market opportunities identified",
            "Strategic partners available for collaboration"
        ]

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

        Args:
            task: Task with patent_analysis, market_analysis, and matches in metadata

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

        try:
            patent_dict = task.metadata.get('patent_analysis')
            market_dict = task.metadata.get('market_analysis')
            matches_list = task.metadata.get('matches', [])

            if not patent_dict or not market_dict:
                raise ValueError("patent_analysis and market_analysis required")

            # Convert dicts to objects
            patent_analysis = PatentAnalysis(**patent_dict)
            market_analysis = MarketAnalysis(**market_dict)
            matches = [StakeholderMatch(**m) for m in matches_list]

            brief = await self.create_valorization_brief(
                patent_analysis,
                market_analysis,
                matches
            )

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

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

        return task