"""
AI routes for deck building assistance and intelligent queries
"""

from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.orm import Session
from pydantic import BaseModel
from typing import List, Optional, Dict, Any

from app.database import get_db
from app.models.user import User
from app.models.deck import Deck
from app.routers.auth import get_current_user

router = APIRouter()


# Pydantic models
class DeckSuggestion(BaseModel):
    card_name: str
    card_id: int
    reason: str
    confidence: float
    category: str  # e.g., "removal", "ramp", "win-condition"


class DeckAnalysis(BaseModel):
    deck_id: int
    deck_name: str
    overall_rating: float
    strengths: List[str]
    weaknesses: List[str]
    suggestions: List[DeckSuggestion]
    mana_curve_analysis: Dict[str, Any]
    color_balance: Dict[str, float]


class QueryResponse(BaseModel):
    response: str
    relevant_cards: List[Dict[str, Any]]
    suggested_actions: List[str]


class NaturalLanguageQuery(BaseModel):
    query: str
    context: Optional[str] = None


# Routes
@router.post("/analyze-deck/{deck_id}", response_model=DeckAnalysis)
async def analyze_deck(
    deck_id: int,
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db)
):
    """
    Analyze a deck using AI and provide suggestions
    """
    # Verify deck ownership
    deck = db.query(Deck).filter(
        Deck.id == deck_id,
        Deck.owner_id == current_user.id
    ).first()
    
    if not deck:
        raise HTTPException(status_code=404, detail="Deck not found")
    
    # TODO: Implement AI deck analysis
    # 1. Analyze mana curve
    # 2. Check color balance
    # 3. Identify synergies and anti-synergies
    # 4. Suggest improvements
    
    return DeckAnalysis(
        deck_id=deck_id,
        deck_name=deck.name,
        overall_rating=0.0,
        strengths=[],
        weaknesses=[],
        suggestions=[],
        mana_curve_analysis={},
        color_balance={}
    )


@router.post("/suggest-cards/{deck_id}")
async def suggest_cards_for_deck(
    deck_id: int,
    limit: int = 10,
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db)
):
    """
    Get AI-powered card suggestions for a deck
    """
    # Verify deck ownership
    deck = db.query(Deck).filter(
        Deck.id == deck_id,
        Deck.owner_id == current_user.id
    ).first()
    
    if not deck:
        raise HTTPException(status_code=404, detail="Deck not found")
    
    # TODO: Implement AI card suggestions
    # 1. Analyze current deck composition
    # 2. Identify gaps in strategy
    # 3. Suggest cards from user's collection or available cards
    # 4. Rank suggestions by relevance
    
    return {"suggestions": []}


@router.post("/query", response_model=QueryResponse)
async def natural_language_query(
    query_data: NaturalLanguageQuery,
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db)
):
    """
    Answer natural language queries about cards, decks, and collection
    """
    # TODO: Implement natural language processing
    # 1. Parse user query
    # 2. Identify intent (search cards, deck building, etc.)
    # 3. Query database based on intent
    # 4. Generate natural language response
    
    return QueryResponse(
        response="Natural language queries not implemented yet.",
        relevant_cards=[],
        suggested_actions=[]
    )


@router.post("/optimize-deck/{deck_id}")
async def optimize_deck(
    deck_id: int,
    optimization_goals: List[str] = ["consistency", "power_level"],
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db)
):
    """
    Optimize a deck based on specified goals
    """
    # Verify deck ownership
    deck = db.query(Deck).filter(
        Deck.id == deck_id,
        Deck.owner_id == current_user.id
    ).first()
    
    if not deck:
        raise HTTPException(status_code=404, detail="Deck not found")
    
    # TODO: Implement deck optimization
    # 1. Analyze current deck
    # 2. Apply optimization algorithms based on goals
    # 3. Suggest card swaps and quantity changes
    # 4. Preserve deck theme and strategy
    
    return {"message": "Deck optimization not implemented yet"}