"""
Router for geocoded case data and grid aggregated data
"""
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from typing import List, Optional
import logging
from functools import lru_cache
import pandas as pd
from pathlib import Path

logger = logging.getLogger("cbpoa.geocoded")

router = APIRouter(prefix="/api/geocoded", tags=["geocoded"])

PROJECT_ROOT = Path(__file__).parent.parent.parent
DATA_DIR = PROJECT_ROOT / "outputs"


@lru_cache(maxsize=1)
def _load_csv(path: Path) -> pd.DataFrame:
    return pd.read_csv(path)

class GridCaseData(BaseModel):
    """Grid case data for visualization"""
    grid_id: int
    latitude: float
    longitude: float
    total_cases: int
    outpatient_cases: int
    inpatient_cases: int
    case_density: float
    risk_index: float
    risk_level: str

class GridCaseResponse(BaseModel):
    grids: List[GridCaseData]
    total_count: int
    total_cases: int

class GeocodedCaseData(BaseModel):
    """Individual geocoded case"""
    case_id: str
    case_type: str
    latitude: float
    longitude: float
    district: str
    street: Optional[str]
    geocode_method: str
    confidence: float

class GeocodedResponse(BaseModel):
    cases: List[GeocodedCaseData]
    total_count: int

@router.get("/grid", response_model=GridCaseResponse, summary="Get aggregated grid case data")
async def get_grid_cases():
    """
    Get 100x100m grid aggregated case data for high-resolution visualization.
    
    Returns grid cells with case counts, density, and risk indices.
    """
    grid_file = DATA_DIR / "grid_risk_summary.csv"
    
    if not grid_file.exists():
        raise HTTPException(status_code=404, detail="Grid data not found")
    
    try:
        df = _load_csv(grid_file)
        
        grids = []
        for _, row in df.iterrows():
            grids.append(GridCaseData(
                grid_id=int(row['grid_id']),
                latitude=float(row['center_y']),
                longitude=float(row['center_x']),
                total_cases=int(row['total_cases']),
                outpatient_cases=int(row['outpatient_cases']),
                inpatient_cases=int(row['inpatient_cases']),
                case_density=float(row['cases_per_km2']),
                risk_index=float(row['risk_index']),
                risk_level=str(row['risk_level'])
            ))
        
        total_cases = int(df['total_cases'].sum())
        
        return GridCaseResponse(
            grids=grids,
            total_count=len(grids),
            total_cases=total_cases
        )
    except Exception as e:
        logger.exception("Error loading grid case data")
        raise HTTPException(status_code=500, detail="Internal server error")

@router.get("/geocoded", response_model=GeocodedResponse, summary="Get geocoded case data")
async def get_geocoded_cases(
    limit: int = 1000,
    district: Optional[str] = None,
):
    """
    Get individual geocoded case data.
    
    Args:
        limit: Maximum number of cases to return (for performance)
        district: Filter by district name
    """
    cases_file = DATA_DIR / "geocoded_all_cases.csv"
    
    if not cases_file.exists():
        raise HTTPException(status_code=404, detail="Geocoded data not found")
    
    try:
        df = _load_csv(cases_file)

        # Drop rows with missing coordinates
        df = df.dropna(subset=['latitude', 'longitude'])

        # Fix swapped lat/lon (Wuhan: lat ~29.9-31.4, lon ~113.7-115.1)
        swapped = df['latitude'] > 50  # longitude values are >113
        df.loc[swapped, ['latitude', 'longitude']] = df.loc[swapped, ['longitude', 'latitude']].values

        # Filter by district if specified
        if district:
            df = df[df['district'] == district]

        # Limit for performance
        df = df.head(limit)

        cases = []
        for row in df.to_dict('records'):
            street_val = row.get('street')
            if pd.isna(street_val):
                street_val = None
            district_val = row.get('district', '')
            if pd.isna(district_val):
                district_val = '未知'
            cases.append(GeocodedCaseData(
                case_id=str(row['case_id']),
                case_type=str(row['case_type']),
                latitude=float(row['latitude']),
                longitude=float(row['longitude']),
                district=str(district_val),
                street=street_val,
                geocode_method=str(row.get('geocode_method', 'unknown')),
                confidence=float(row.get('confidence', 0) or 0) if not pd.isna(row.get('confidence')) else 0.0
            ))

        return GeocodedResponse(
            cases=cases,
            total_count=len(cases)
        )
    except Exception as e:
        logger.exception("Error loading geocoded case data")
        raise HTTPException(status_code=500, detail="Internal server error")

@router.get("/geocoded/count", summary="Get geocoded case count")
async def get_geocoded_count():
    """Get total count of geocoded cases."""
    cases_file = DATA_DIR / "geocoded_all_cases.csv"
    
    if not cases_file.exists():
        raise HTTPException(status_code=404, detail="Geocoded data not found")
    
    try:
        df = _load_csv(cases_file)
        street_matched = len(df[df['geocode_method'] == 'street'])
        district_fallback = len(df[df['geocode_method'] == 'district'])
        
        return {
            "total": len(df),
            "street_matched": street_matched,
            "district_fallback": district_fallback,
            "match_rate": round(street_matched / len(df) * 100, 1)
        }
    except Exception as e:
        logger.exception("Error counting geocoded cases")
        raise HTTPException(status_code=500, detail="Internal server error")