import os
import json
import datetime
from google.cloud import bigquery
from google.cloud import pubsub_v1

class AnomalyDetector:
    def __init__(self):
        self.project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
        if not self.project_id:
            raise ValueError("GOOGLE_CLOUD_PROJECT environment variable not set.")
        
        self.billing_table = os.environ.get("BILLING_TABLE")
        if not self.billing_table:
            raise ValueError("BILLING_TABLE environment variable not set.")
            
        self.pubsub_topic_name = os.environ.get("PUBSUB_TOPIC", "billing_alerts")
        self.bq_client = bigquery.Client(project=self.project_id)
        self.publisher = pubsub_v1.PublisherClient()
        self.topic_path = self.publisher.topic_path(self.project_id, self.pubsub_topic_name)

    def detect_anomalies(self):
        """
        Detects anomalies in billing data by comparing today's cost to the 7-day average.
        """
        query = f"""
            WITH daily_costs AS (
                SELECT
                    service.description AS service,
                    DATE(_PARTITIONTIME) AS usage_date,
                    SUM(cost) AS daily_cost
                FROM `{self.billing_table}`
                WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 14 DAY)
                GROUP BY 1, 2
            ),
            costs_with_avg AS (
                SELECT
                    service,
                    usage_date,
                    daily_cost,
                    AVG(daily_cost) OVER (PARTITION BY service ORDER BY usage_date ROWS BETWEEN 7 PRECEDING AND 1 PRECEDING) AS avg_7day
                FROM daily_costs
            )
            SELECT
                service,
                daily_cost AS today_cost,
                avg_7day,
                daily_cost / avg_7day AS ratio
            FROM costs_with_avg
            WHERE usage_date = CURRENT_DATE()
              AND daily_cost > 2.0 * avg_7day
        """
        query_job = self.bq_client.query(query)
        results = query_job.result()

        anomalies = []
        for row in results:
            anomalies.append({
                "service": row.service,
                "today_cost": row.today_cost,
                "avg_7d": row.avg_7day,
                "ratio": row.ratio
            })

        if anomalies:
            self.publish_alerts(anomalies)

        return {"anomalies": anomalies}

    def publish_alerts(self, anomalies):
        """
        Publishes anomaly alerts to a Pub/Sub topic.
        """
        for anomaly in anomalies:
            message_data = {
                "service": anomaly["service"],
                "today_cost": anomaly["today_cost"],
                "avg_7d": anomaly["avg_7d"],
                "ratio": anomaly["ratio"],
                "timestamp": datetime.datetime.now().isoformat()
            }
            message_bytes = json.dumps(message_data).encode("utf-8")
            self.publisher.publish(self.topic_path, data=message_bytes)

if __name__ == '__main__':
    detector = AnomalyDetector()
    print("Anomalies:", detector.detect_anomalies())