# agents/core-logic/heavy_predictor.py

from google.cloud import bigquery

# Configuration
BIGQUERY_PROJECT = "propane-will-491900-m5"
BIGQUERY_DATASET = "osvauco_logs"
BIGQUERY_TABLE = "cloud_run_logs"
TELEMETRY_CALLS_THRESHOLD = 500

def check_and_trigger_heavy_mode():
    """
    Checks the number of telemetry calls and triggers heavy mode if the threshold is exceeded.
    """
    try:
        client = bigquery.Client(project=BIGQUERY_PROJECT)
        table_id = f"{BIGQUERY_PROJECT}.{BIGQUERY_DATASET}.{BIGQUERY_TABLE}"

        # 1. Query the number of telemetry calls from BigQuery
        query = f"""
            SELECT COUNT(*) as total_calls
            FROM `{table_id}`
            WHERE timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
        """
        query_job = client.query(query)
        results = query_job.result()

        for row in results:
            total_calls = row.total_calls
            print(f"Total telemetry calls in the last 24 hours: {total_calls}")

            # 2. Trigger heavy mode if the threshold is exceeded
            if total_calls > TELEMETRY_CALLS_THRESHOLD:
                print("Telemetry calls threshold exceeded. Triggering heavy mode.")
                # Placeholder for logic to trigger heavy mode
                # This could involve scaling up resources, switching to a more powerful model, etc.
                pass
            else:
                print("Telemetry calls are within acceptable limits.")

    except Exception as e:
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    check_and_trigger_heavy_mode()