# src/fmatch/saas/api/v2/quality.py
"""Data quality scoring API endpoints."""

from fastapi import APIRouter
from pydantic import BaseModel
from typing import List, Dict, Any, Optional
import random

router = APIRouter(prefix="/api/v2/quality", tags=["quality"])


class QualityScoreRequest(BaseModel):
    job_id: str
    object: str = "Lead"
    weights: Dict[str, float] = {"completeness": 0.7, "validity": 0.3}
    required_fields: List[str] = []
    personal_domains: List[str] = [
        "gmail.com",
        "yahoo.com",
        "hotmail.com",
        "outlook.com",
        "aol.com",
    ]
    threshold: float = 0.8


class QualityScoreResponse(BaseModel):
    records_scored: int
    below_threshold: int
    avg_score: float
    alerts_emitted: bool
    samples: Optional[List[Dict[str, Any]]] = None


@router.post("/score")
async def score_quality(request: QualityScoreRequest):
    """Score data quality for a job."""

    # Mock implementation - in production, analyze actual job data
    records_scored = random.randint(500, 2000)

    # Calculate scores based on weights
    completeness_score = random.uniform(0.6, 0.95)
    validity_score = random.uniform(0.5, 0.9)

    avg_score = completeness_score * request.weights.get(
        "completeness", 0.7
    ) + validity_score * request.weights.get("validity", 0.3)

    # Calculate how many records are below threshold
    below_threshold = int(records_scored * (1 - avg_score) * random.uniform(0.8, 1.2))

    # Generate sample problematic records
    samples = []
    if below_threshold > 0:
        for i in range(min(5, below_threshold)):
            sample = {
                "record_id": f"rec_{i+1}",
                "score": round(random.uniform(0.3, request.threshold - 0.1), 2),
                "issues": [],
            }

            # Add random issues
            if random.random() > 0.5:
                sample["issues"].append("Missing required field: Email")
            if random.random() > 0.5:
                sample["issues"].append("Personal email domain detected")
            if random.random() > 0.5:
                sample["issues"].append("Invalid phone format")

            samples.append(sample)

    # Determine if alerts should be emitted
    alerts_emitted = below_threshold > (
        records_scored * 0.1
    )  # Alert if >10% below threshold

    return QualityScoreResponse(
        records_scored=records_scored,
        below_threshold=below_threshold,
        avg_score=round(avg_score, 2),
        alerts_emitted=alerts_emitted,
        samples=samples,
    )


@router.get("/thresholds")
async def get_quality_thresholds():
    """Get recommended quality thresholds by object type."""
    return {
        "Lead": {
            "recommended": 0.8,
            "minimum": 0.6,
            "weights": {"completeness": 0.7, "validity": 0.3},
        },
        "Contact": {
            "recommended": 0.85,
            "minimum": 0.7,
            "weights": {"completeness": 0.6, "validity": 0.4},
        },
        "Account": {
            "recommended": 0.9,
            "minimum": 0.75,
            "weights": {"completeness": 0.5, "validity": 0.5},
        },
    }


@router.post("/enrich/estimate")
async def estimate_enrichment(job_id: str, record_ids: Optional[List[str]] = None):
    """Estimate cost and impact of data enrichment."""

    # Mock estimation
    if record_ids:
        records_to_enrich = len(record_ids)
    else:
        records_to_enrich = random.randint(50, 500)

    cost_per_record = 0.03
    estimated_cost = records_to_enrich * cost_per_record

    return {
        "records_to_enrich": records_to_enrich,
        "cost_per_record": cost_per_record,
        "estimated_cost": round(estimated_cost, 2),
        "estimated_improvement": {
            "before_score": 0.65,
            "after_score": 0.92,
            "improvement_pct": 41.5,
        },
        "fields_to_enrich": ["Company", "Phone", "Industry", "Company Size"],
    }
