"""
Nomination Assistant for Award Identification

AI-assisted preparation of award nominations and grant applications.

FEATURES (Planned):
------------------
1. DOCUMENT GENERATION:
   - Executive summaries
   - Project descriptions
   - Budget justifications
   - Team CVs and bios

2. TEMPLATE MATCHING:
   - Match to funder templates
   - Format compliance checking
   - Character/word limit validation

3. QUALITY ASSURANCE:
   - CriticAgent validation
   - Reviewer simulation
   - Gap identification

4. COLLABORATION:
   - Multi-author editing
   - Comment and review workflows
   - Version control

HUMAN-IN-THE-LOOP:
-----------------
Document preparation requires extensive human input:
- Initial content drafting
- Review and revision cycles
- Final approval before submission

This assistant accelerates the process but doesn't replace
human expertise in grant writing.

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
from enum import Enum
from loguru import logger


class DocumentTemplate(str, Enum):
    """Standard document templates."""
    HORIZON_PROPOSAL = "horizon_proposal"
    ERC_APPLICATION = "erc_application"
    NATIONAL_GRANT = "national_grant"
    AWARD_NOMINATION = "award_nomination"
    LETTER_OF_INTENT = "letter_of_intent"
    BUDGET_TEMPLATE = "budget_template"
    CV_EUROPASS = "cv_europass"
    CUSTOM = "custom"


class ReviewStatus(str, Enum):
    """Document review status."""
    DRAFT = "draft"
    INTERNAL_REVIEW = "internal_review"
    REVISION_NEEDED = "revision_needed"
    APPROVED = "approved"
    SUBMITTED = "submitted"


@dataclass
class DocumentSection:
    """
    Section of a nomination document.

    Represents a structured section with content and metadata.
    """
    section_id: str
    title: str
    content: str
    word_limit: Optional[int] = None
    current_words: int = 0
    status: str = "draft"
    ai_generated: bool = False
    human_reviewed: bool = False
    reviewer_comments: List[str] = field(default_factory=list)
    suggestions: List[str] = field(default_factory=list)


@dataclass
class DocumentReview:
    """
    Review of a nomination document.

    Contains feedback from AI and human reviewers.
    """
    review_id: str
    document_id: str
    reviewer_type: str  # "ai", "human", "external"
    reviewer_name: Optional[str] = None
    overall_score: Optional[float] = None
    section_scores: Dict[str, float] = field(default_factory=dict)
    strengths: List[str] = field(default_factory=list)
    weaknesses: List[str] = field(default_factory=list)
    suggestions: List[str] = field(default_factory=list)
    decision: str = "pending"  # approve, revise, reject
    created_at: datetime = field(default_factory=datetime.now)


class NominationAssistant:
    """
    AI assistant for preparing nominations and applications.

    This component:
    - Generates document sections
    - Checks format compliance
    - Simulates reviewer feedback
    - Manages revision workflows

    INTEGRATION WITH CRITICAGENT:
    -----------------------------
    Uses CriticAgent for:
    - Document quality validation
    - Format compliance checking
    - Reviewer perspective simulation
    - Gap and weakness identification

    CONFIDENCE SCORING:
    ------------------
    All AI-generated content includes:
    - Confidence score (0.0-1.0)
    - Source references where applicable
    - Suggestions for improvement
    - Flag for human review

    Generated content with low confidence scores
    is automatically flagged for human review.
    """

    def __init__(
        self,
        llm_client: Optional[Any] = None,
        critic_agent: Optional[Any] = None,
        template_library: Optional[Dict[str, Any]] = None,
    ):
        """
        Initialize Nomination Assistant.

        Args:
            llm_client: LangChain LLM client for content generation
            critic_agent: CriticAgent for validation
            template_library: Library of document templates
        """
        self.llm_client = llm_client
        self.critic_agent = critic_agent
        self.template_library = template_library or {}
        self.name = "NominationAssistant"

        # Threshold for requiring human review
        self.confidence_threshold = 0.7

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

    async def generate_section(
        self,
        document_id: str,
        section_type: str,
        context: Dict[str, Any],
        word_limit: Optional[int] = None,
    ) -> DocumentSection:
        """
        Generate a document section using AI.

        Args:
            document_id: Parent document ID
            section_type: Type of section to generate
            context: Context information for generation
            word_limit: Optional word limit

        Returns:
            Generated section with confidence score

        TODO: Implement actual LLM generation
        """
        logger.info(f"Generating {section_type} section for document: {document_id}")

        # Placeholder response
        return DocumentSection(
            section_id=f"sec_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            title=section_type.replace("_", " ").title(),
            content="[AI-generated content placeholder]",
            word_limit=word_limit,
            current_words=0,
            status="draft",
            ai_generated=True,
            human_reviewed=False,
            suggestions=["Complete implementation with actual LLM generation"],
        )

    async def check_format_compliance(
        self,
        document_id: str,
        template: DocumentTemplate,
    ) -> Dict[str, Any]:
        """
        Check document compliance with template requirements.

        Args:
            document_id: Document to check
            template: Template to check against

        Returns:
            Compliance report with issues

        TODO: Implement actual compliance checking
        """
        logger.info(f"Checking format compliance for document: {document_id}")

        # Placeholder response
        return {
            "document_id": document_id,
            "template": template.value,
            "compliant": False,
            "issues": [
                {
                    "type": "placeholder",
                    "message": "Compliance checking not yet implemented",
                    "severity": "info",
                }
            ],
            "word_counts": {},
            "missing_sections": [],
        }

    async def simulate_review(
        self,
        document_id: str,
        reviewer_perspective: str = "general",
    ) -> DocumentReview:
        """
        Simulate reviewer feedback using AI.

        Generates feedback from the perspective of a grant
        reviewer to identify potential weaknesses.

        Args:
            document_id: Document to review
            reviewer_perspective: Type of reviewer to simulate

        Returns:
            Simulated review with scores and feedback

        TODO: Implement actual review simulation
        """
        logger.info(f"Simulating {reviewer_perspective} review for document: {document_id}")

        # Placeholder response
        return DocumentReview(
            review_id=f"rev_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            document_id=document_id,
            reviewer_type="ai",
            reviewer_name=f"AI ({reviewer_perspective})",
            overall_score=0.0,
            strengths=["Review simulation not yet implemented"],
            weaknesses=["Cannot assess without implementation"],
            suggestions=["Complete the AI review simulation feature"],
            decision="pending",
        )

    async def suggest_improvements(
        self,
        section: DocumentSection,
    ) -> List[str]:
        """
        Suggest improvements for a document section.

        Uses CriticAgent to analyze section and generate
        actionable improvement suggestions.

        Args:
            section: Section to analyze

        Returns:
            List of improvement suggestions

        TODO: Implement CriticAgent integration
        """
        logger.info(f"Generating improvement suggestions for section: {section.section_id}")

        # Placeholder response
        return [
            "Improvement suggestions not yet implemented",
            "Will integrate with CriticAgent for validation",
        ]

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

        Performs comprehensive validation including:
        - Content quality assessment
        - Format compliance
        - Logical consistency
        - Citation verification

        Args:
            document_id: Document to validate

        Returns:
            Validation result with scores and issues

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

        # Placeholder response
        return {
            "document_id": document_id,
            "valid": False,
            "overall_score": 0.0,
            "dimension_scores": {
                "completeness": 0.0,
                "clarity": 0.0,
                "accuracy": 0.0,
                "compliance": 0.0,
            },
            "issues": ["CriticAgent validation not yet implemented"],
            "suggestions": ["Complete CriticAgent integration"],
            "human_review_required": True,
            "confidence": 0.0,
        }

    def requires_human_review(
        self,
        confidence_score: float,
        section_type: str,
    ) -> bool:
        """
        Determine if content requires human review.

        Human review is required when:
        - Confidence is below threshold
        - Section is critical (executive summary, budget)
        - Content makes claims about capabilities

        Args:
            confidence_score: AI confidence score
            section_type: Type of section

        Returns:
            True if human review required
        """
        # Low confidence always requires review
        if confidence_score < self.confidence_threshold:
            return True

        # Critical sections always require review
        critical_sections = [
            "executive_summary",
            "budget",
            "team_qualifications",
            "methodology",
        ]
        if section_type.lower() in critical_sections:
            return True

        return False