from google.cloud import bigquery
from datetime import datetime, timezone

PROJECT_ID = "propane-will-491900-m5"
DATASET = "costguard_dataset"
BILLING_TABLE = f"{PROJECT_ID}.{DATASET}.gcp_billing_export"

def get_recommendations(budget_nok: float = 500.0) -> dict:
    client = bigquery.Client(project=PROJECT_ID)
    recommendations = []

    # 1. Anomali: tjenester der siste 3d-snitt >= 2x 30d-snitt
    anomaly_query = f"""
    WITH base AS (
      SELECT service.description AS service,
        AVG(cost) AS avg_30d
      FROM `{BILLING_TABLE}`
      WHERE DATE(usage_start_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
      GROUP BY service
    ),
    recent AS (
      SELECT service.description AS service,
        AVG(cost) AS avg_3d
      FROM `{BILLING_TABLE}`
      WHERE DATE(usage_start_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 3 DAY)
      GROUP BY service
    )
    SELECT r.service, r.avg_3d, b.avg_30d,
      SAFE_DIVIDE(r.avg_3d, b.avg_30d) AS ratio
    FROM recent r JOIN base b ON r.service = b.service
    WHERE SAFE_DIVIDE(r.avg_3d, b.avg_30d) >= 2.0
      AND b.avg_30d > 0.001
    ORDER BY ratio DESC
    """

    # 2. Sløsing: tjenester med kostnad men 0 aktivitet siste 7d
    waste_query = f"""
    WITH active AS (
      SELECT DISTINCT service.description AS service
      FROM `{BILLING_TABLE}`
      WHERE DATE(usage_start_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)
        AND cost > 0
    ),
    all_svc AS (
      SELECT DISTINCT service.description AS service
      FROM `{BILLING_TABLE}`
      WHERE DATE(usage_start_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
        AND cost > 0
    )
    SELECT a.service FROM all_svc a
    LEFT JOIN active b ON a.service = b.service
    WHERE b.service IS NULL
    """

    try:
        # Anomalier
        for row in client.query(anomaly_query).result():
            ratio = row.ratio or 0
            severity = "high" if ratio >= 3 else "medium"
            savings = round(float(row.avg_3d - row.avg_30d) * 30 * 10, 2)  # rough NOK
            recommendations.append({
                "type": "anomaly",
                "severity": severity,
                "service": row.service,
                "message": f"Unormal kostnadsøkning på {row.service} — {ratio:.1f}x over normalt",
                "estimated_savings_nok": max(savings, 0)
            })

        # Sløsing
        for row in client.query(waste_query).result():
            recommendations.append({
                "type": "waste",
                "severity": "low",
                "service": row.service,
                "message": f"{row.service} har hatt kostnad men ingen aktivitet siste 7 dager — vurder å skru av",
                "estimated_savings_nok": 0
            })

    except Exception as e:
        return {"recommendations": [], "total_estimated_savings_nok": 0, "error": str(e)}

    total_savings = sum(r["estimated_savings_nok"] for r in recommendations)
    return {
        "recommendations": recommendations,
        "total_estimated_savings_nok": round(total_savings, 2)
    }