src/agents/scenario4/opportunity_scanner.py

"""
Opportunity Scanner for Award Identification

Scans funding databases and announcement sources for opportunities.

PLANNED DATA SOURCES:
---------------------
- Horizon Europe / CORDIS
- European Research Council (ERC)
- National funding agencies (DFG, ANR, UKRI, etc.)
- Foundation databases
- Corporate R&D partnerships
- Innovation prizes and awards

SCANNING STRATEGY:
-----------------
1. KEYWORD MATCHING:
   - Technology-specific terms
   - Research area keywords
   - Institution eligibility terms

2. SEMANTIC SEARCH:
   - Vector similarity to capability descriptions
   - Cross-lingual matching for EU opportunities

3. FILTERING:
   - Deadline filtering (exclude expired)
   - Amount thresholds
   - Eligibility pre-screening

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 DataSource(str, Enum):
    """Funding data sources."""
    CORDIS = "cordis"  # Horizon Europe
    ERC = "erc"  # European Research Council
    NATIONAL = "national"  # National agencies
    FOUNDATION = "foundation"
    CORPORATE = "corporate"
    CUSTOM = "custom"


@dataclass
class ScanConfiguration:
    """
    Configuration for opportunity scanning.

    Defines what and how to scan for opportunities.
    """
    config_id: str
    name: str
    sources: List[DataSource]
    keywords: List[str]
    research_areas: List[str]
    min_amount: Optional[float] = None
    max_amount: Optional[float] = None
    currency: str = "EUR"
    exclude_expired: bool = True
    include_rolling: bool = True  # Include opportunities with no fixed deadline
    scan_frequency_hours: int = 24
    last_scan: Optional[datetime] = None
    enabled: bool = True


@dataclass
class ScanResult:
    """
    Result of an opportunity scan.

    Contains discovered opportunities and scan metadata.
    """
    scan_id: str
    config_id: str
    started_at: datetime
    completed_at: Optional[datetime] = None
    sources_scanned: List[str] = field(default_factory=list)
    opportunities_found: int = 0
    new_opportunities: int = 0
    updated_opportunities: int = 0
    errors: List[str] = field(default_factory=list)
    status: str = "in_progress"


class OpportunityScanner:
    """
    Scans funding databases for opportunities.

    This component:
    - Connects to funding data sources
    - Runs periodic scans
    - Identifies new opportunities
    - Updates existing opportunity data

    INTEGRATION NOTES:
    -----------------
    For production deployment, integrate with:

    1. HORIZON EUROPE (CORDIS):
       - Use CORDIS API for call announcements
       - Parse work programme documents
       - Track topic deadlines

    2. NATIONAL AGENCIES:
       - DFG (Germany): RSS feeds
       - ANR (France): Open data portal
       - UKRI (UK): Gateway API

    3. FOUNDATIONS:
       - Scrape foundation websites
       - Monitor RSS/newsletter feeds
       - Parse PDF announcements

    4. CUSTOM SOURCES:
       - Support for institution-specific sources
       - Private funding networks
       - Industry partnership programs
    """

    def __init__(
        self,
        database_url: Optional[str] = None,
        embedding_client: Optional[Any] = None,
    ):
        """
        Initialize Opportunity Scanner.

        Args:
            database_url: Database for storing opportunities
            embedding_client: Client for semantic search embeddings
        """
        self.database_url = database_url
        self.embedding_client = embedding_client
        self.name = "OpportunityScanner"

        # Registered scan configurations
        self.configurations: Dict[str, ScanConfiguration] = {}

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

    async def register_configuration(
        self,
        config: ScanConfiguration,
    ) -> None:
        """
        Register a scan configuration.

        Args:
            config: Scan configuration to register
        """
        self.configurations[config.config_id] = config
        logger.info(f"Registered scan configuration: {config.name}")

    async def run_scan(
        self,
        config_id: Optional[str] = None,
    ) -> ScanResult:
        """
        Run an opportunity scan.

        Args:
            config_id: Specific configuration to use (or all if None)

        Returns:
            Scan result with discovered opportunities

        TODO: Implement actual scanning logic
        """
        logger.info(f"Running opportunity scan (config: {config_id or 'all'})")

        # Placeholder response
        return ScanResult(
            scan_id=f"scan_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            config_id=config_id or "all",
            started_at=datetime.now(),
            completed_at=datetime.now(),
            sources_scanned=[],
            opportunities_found=0,
            new_opportunities=0,
            updated_opportunities=0,
            status="placeholder",
        )

    async def scan_cordis(
        self,
        keywords: List[str],
    ) -> List[Dict[str, Any]]:
        """
        Scan CORDIS for Horizon Europe opportunities.

        Args:
            keywords: Keywords to search for

        Returns:
            List of opportunities from CORDIS

        TODO: Implement CORDIS API integration
        """
        logger.info(f"Scanning CORDIS with keywords: {keywords}")

        # Placeholder - would use CORDIS API
        return []

    async def scan_erc(
        self,
        research_areas: List[str],
    ) -> List[Dict[str, Any]]:
        """
        Scan ERC for grant opportunities.

        Args:
            research_areas: Research areas to match

        Returns:
            List of ERC opportunities

        TODO: Implement ERC portal integration
        """
        logger.info(f"Scanning ERC for research areas: {research_areas}")

        # Placeholder - would scrape ERC portal
        return []

    async def semantic_search(
        self,
        query: str,
        sources: Optional[List[DataSource]] = None,
        top_k: int = 10,
    ) -> List[Dict[str, Any]]:
        """
        Semantic search for relevant opportunities.

        Uses vector similarity to find opportunities
        matching natural language descriptions.

        Args:
            query: Natural language query
            sources: Data sources to search
            top_k: Number of results to return

        Returns:
            List of matching opportunities with scores

        TODO: Implement embedding-based search
        """
        logger.info(f"Semantic search: {query[:50]}...")

        # Placeholder - would use embedding similarity
        return []

    async def get_scan_history(
        self,
        limit: int = 10,
    ) -> List[ScanResult]:
        """
        Get history of recent scans.

        Args:
            limit: Maximum number of results

        Returns:
            List of recent scan results

        TODO: Implement scan history retrieval
        """
        logger.info(f"Getting scan history (limit: {limit})")

        # Placeholder
        return []