src/agents/scenario4/award_identification_agent.py

"""
Award Identification Agent for SPARKNET

AI-powered funding opportunity discovery and award nomination assistance.
Part of Scenario 4: Award Identification.

FEATURES:
---------
1. OPPORTUNITY DISCOVERY:
   - Scan funding databases and announcements
   - Match opportunities to research capabilities
   - Track application deadlines

2. NOMINATION ASSISTANCE:
   - Prepare award nomination documents
   - Review and validate submissions
   - Generate supporting materials

3. APPLICATION SUPPORT:
   - Document preparation workflows
   - Compliance checking
   - Reviewer matching

INTEGRATIONS (Planned):
-----------------------
- Horizon Europe CORDIS database
- National funding agency APIs
- ERC portal integration
- Patent databases for innovation evidence

Author: SPARKNET Team
Project: VISTA/Horizon EU
Status: Placeholder - In Development
"""

from typing import Optional, Dict, Any, List
from dataclasses import dataclass, field
from datetime import datetime, date
from enum import Enum
from loguru import logger


class OpportunityType(str, Enum):
    """Type of funding opportunity."""
    GRANT = "grant"
    AWARD = "award"
    FELLOWSHIP = "fellowship"
    PRIZE = "prize"
    INVESTMENT = "investment"
    PARTNERSHIP = "partnership"


class OpportunityStatus(str, Enum):
    """Opportunity tracking status."""
    IDENTIFIED = "identified"
    EVALUATING = "evaluating"
    PREPARING = "preparing"
    SUBMITTED = "submitted"
    AWARDED = "awarded"
    REJECTED = "rejected"
    EXPIRED = "expired"


class EligibilityStatus(str, Enum):
    """Eligibility assessment status."""
    ELIGIBLE = "eligible"
    INELIGIBLE = "ineligible"
    PARTIAL = "partial"  # Some criteria met
    UNKNOWN = "unknown"  # Needs review


@dataclass
class FundingOpportunity:
    """
    Funding opportunity data model.

    Represents a grant, award, or other funding opportunity
    identified by the scanning system.
    """
    opportunity_id: str
    title: str
    description: str
    opportunity_type: OpportunityType
    funder: str
    funder_type: str  # government, foundation, corporate, EU, etc.
    amount_min: Optional[float] = None
    amount_max: Optional[float] = None
    currency: str = "EUR"
    deadline: Optional[date] = None
    url: Optional[str] = None
    eligibility_criteria: List[str] = field(default_factory=list)
    keywords: List[str] = field(default_factory=list)
    status: OpportunityStatus = OpportunityStatus.IDENTIFIED
    match_score: Optional[float] = None  # How well it matches capabilities
    notes: Optional[str] = None
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class OpportunityMatch:
    """
    Match between opportunity and research/technology.

    Represents alignment between a funding opportunity
    and institutional capabilities.
    """
    match_id: str
    opportunity_id: str
    technology_id: Optional[str] = None
    research_area: Optional[str] = None
    match_score: float = 0.0  # 0.0 to 1.0
    match_rationale: str = ""
    eligibility_status: EligibilityStatus = EligibilityStatus.UNKNOWN
    eligibility_notes: List[str] = field(default_factory=list)
    recommended_action: str = ""
    confidence_score: float = 0.0


@dataclass
class NominationDocument:
    """
    Award nomination document.

    Contains structured content for award/grant applications.
    """
    document_id: str
    opportunity_id: str
    document_type: str  # proposal, nomination_letter, cv, budget, etc.
    title: str
    content: str
    version: str = "1.0"
    status: str = "draft"  # draft, review, final
    created_at: datetime = field(default_factory=datetime.now)
    updated_at: datetime = field(default_factory=datetime.now)
    created_by: Optional[str] = None
    reviewer_comments: List[Dict[str, Any]] = field(default_factory=list)
    critic_validation: Optional[Dict[str, Any]] = None


class AwardIdentificationAgent:
    """
    Agent for identifying funding opportunities and assisting nominations.

    This agent:
    - Scans funding databases for opportunities
    - Matches opportunities to research capabilities
    - Assists with nomination document preparation
    - Tracks application deadlines and status

    HUMAN-IN-THE-LOOP WORKFLOW:
    ---------------------------
    Award applications require human judgment. This agent implements:

    1. AUTOMATED SCANNING:
       - Regular scans of funding databases
       - Keyword matching and filtering
       - Initial eligibility screening

    2. AI-ASSISTED MATCHING:
       - Score opportunities against capabilities
       - Generate match rationale
       - Identify gaps and risks

    3. HUMAN DECISION POINTS:
       - Approval to pursue opportunities
       - Review of application documents
       - Final submission authorization

    4. QUALITY ASSURANCE:
       - CriticAgent validation of documents
       - Compliance checking
       - Reviewer feedback integration
    """

    def __init__(
        self,
        llm_client: Optional[Any] = None,
        critic_agent: Optional[Any] = None,
        database_url: Optional[str] = None,
    ):
        """
        Initialize Award Identification Agent.

        Args:
            llm_client: LangChain LLM client for AI analysis
            critic_agent: CriticAgent for document validation
            database_url: Database connection URL
        """
        self.llm_client = llm_client
        self.critic_agent = critic_agent
        self.database_url = database_url
        self.name = "AwardIdentificationAgent"
        self.description = "Funding opportunity discovery and nomination assistance"

        logger.info(f"Initialized {self.name} (placeholder)")

    async def scan_opportunities(
        self,
        keywords: Optional[List[str]] = None,
        opportunity_types: Optional[List[OpportunityType]] = None,
        min_amount: Optional[float] = None,
        max_deadline_days: Optional[int] = None,
    ) -> List[FundingOpportunity]:
        """
        Scan for funding opportunities matching criteria.

        Args:
            keywords: Keywords to search for
            opportunity_types: Types of opportunities to find
            min_amount: Minimum funding amount
            max_deadline_days: Maximum days until deadline

        Returns:
            List of matching opportunities

        TODO: Implement actual opportunity scanning
        """
        logger.info(f"Scanning for opportunities with keywords: {keywords}")

        # Placeholder - would integrate with funding databases
        return []

    async def match_opportunity(
        self,
        opportunity_id: str,
        technology_ids: Optional[List[str]] = None,
        research_areas: Optional[List[str]] = None,
    ) -> OpportunityMatch:
        """
        Evaluate match between opportunity and capabilities.

        Args:
            opportunity_id: Opportunity to evaluate
            technology_ids: Technologies to consider
            research_areas: Research areas to consider

        Returns:
            Match result with score and rationale

        TODO: Implement actual matching logic
        """
        logger.info(f"Matching opportunity: {opportunity_id}")

        # Placeholder response
        return OpportunityMatch(
            match_id=f"match_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            opportunity_id=opportunity_id,
            match_score=0.0,
            match_rationale="Matching not yet implemented",
            eligibility_status=EligibilityStatus.UNKNOWN,
            recommended_action="Review manually",
            confidence_score=0.0,
        )

    async def check_eligibility(
        self,
        opportunity_id: str,
        applicant_profile: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Check eligibility for a funding opportunity.

        Args:
            opportunity_id: Opportunity to check
            applicant_profile: Profile of potential applicant

        Returns:
            Eligibility assessment with details

        TODO: Implement actual eligibility checking
        """
        logger.info(f"Checking eligibility for opportunity: {opportunity_id}")

        # Placeholder response
        return {
            "opportunity_id": opportunity_id,
            "status": EligibilityStatus.UNKNOWN.value,
            "criteria_met": [],
            "criteria_not_met": [],
            "criteria_unknown": [],
            "recommendation": "Manual review required",
            "confidence": 0.0,
        }

    async def prepare_nomination(
        self,
        opportunity_id: str,
        document_type: str,
        context: Dict[str, Any],
    ) -> NominationDocument:
        """
        Prepare a nomination/application document.

        Args:
            opportunity_id: Target opportunity
            document_type: Type of document to prepare
            context: Context information for document generation

        Returns:
            Generated nomination document

        TODO: Implement actual document preparation with LLM
        """
        logger.info(f"Preparing {document_type} for opportunity: {opportunity_id}")

        # Placeholder response
        return NominationDocument(
            document_id=f"doc_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            opportunity_id=opportunity_id,
            document_type=document_type,
            title=f"{document_type.replace('_', ' ').title()} - Draft",
            content="[Document content to be generated]",
            status="draft",
        )

    async def validate_document(
        self,
        document: NominationDocument,
    ) -> Dict[str, Any]:
        """
        Validate nomination document using CriticAgent.

        Args:
            document: Document to validate

        Returns:
            Validation result with suggestions

        TODO: Implement CriticAgent integration
        """
        logger.info(f"Validating document: {document.document_id}")

        # Placeholder response
        return {
            "document_id": document.document_id,
            "valid": False,
            "overall_score": 0.0,
            "dimension_scores": {},
            "issues": ["Validation not yet implemented"],
            "suggestions": ["Complete document implementation"],
            "human_review_required": True,
        }

    async def get_upcoming_deadlines(
        self,
        days_ahead: int = 30,
    ) -> List[FundingOpportunity]:
        """
        Get opportunities with upcoming deadlines.

        Args:
            days_ahead: Number of days to look ahead

        Returns:
            List of opportunities with deadlines

        TODO: Implement actual deadline tracking
        """
        logger.info(f"Getting deadlines for next {days_ahead} days")

        # Placeholder response
        return []

    def get_vista_quality_criteria(self) -> Dict[str, Any]:
        """
        Get VISTA quality criteria for award identification.

        Returns quality thresholds for opportunity matching and
        document preparation aligned with VISTA objectives.
        """
        return {
            "opportunity_relevance": {
                "weight": 0.30,
                "threshold": 0.75,
                "description": "Opportunities must be relevant to research capabilities",
            },
            "eligibility_accuracy": {
                "weight": 0.25,
                "threshold": 0.90,
                "description": "Eligibility assessments must be accurate",
            },
            "document_quality": {
                "weight": 0.25,
                "threshold": 0.85,
                "description": "Nomination documents must meet quality standards",
            },
            "deadline_tracking": {
                "weight": 0.20,
                "threshold": 0.95,
                "description": "Deadlines must be tracked accurately",
            },
        }