"""Label Quality Scorer Router"""
from fastapi import APIRouter, UploadFile, File
from pydantic import BaseModel
from typing import Optional

router = APIRouter()


class AgreementMetrics(BaseModel):
    cohens_kappa: Optional[float] = None
    fleiss_kappa: Optional[float] = None
    krippendorff_alpha: Optional[float] = None
    percent_agreement: float
    interpretation: str  # poor, fair, moderate, good, excellent


class DisagreementSample(BaseModel):
    sample_id: str
    labels: dict  # annotator -> label
    majority_label: Optional[str] = None


class QualityReport(BaseModel):
    total_samples: int
    total_annotators: int
    metrics: AgreementMetrics
    disagreements: list[DisagreementSample]
    recommendations: list[str]


@router.post("/analyze", response_model=QualityReport)
async def analyze_labels(
    file: UploadFile = File(...),
    sample_id_column: str = "id",
    annotator_columns: Optional[list[str]] = None
):
    """Analyze labeling quality from annotations file"""
    # TODO: Implement label quality analysis
    return QualityReport(
        total_samples=0,
        total_annotators=0,
        metrics=AgreementMetrics(
            percent_agreement=0.0,
            interpretation="unknown"
        ),
        disagreements=[],
        recommendations=[]
    )


@router.post("/pairwise")
async def pairwise_agreement(
    file: UploadFile = File(...),
    annotator1: str = None,
    annotator2: str = None
):
    """Calculate pairwise agreement between two annotators"""
    # TODO: Implement pairwise analysis
    return {
        "annotator1": annotator1,
        "annotator2": annotator2,
        "agreement": 0.0,
        "kappa": 0.0
    }


@router.get("/thresholds")
async def get_quality_thresholds():
    """Get interpretation thresholds for agreement metrics"""
    return {
        "kappa_interpretation": {
            "poor": "< 0.00",
            "slight": "0.00 - 0.20",
            "fair": "0.21 - 0.40",
            "moderate": "0.41 - 0.60",
            "substantial": "0.61 - 0.80",
            "almost_perfect": "0.81 - 1.00"
        }
    }
