# agents/core-logic/hypertuner.py

from google.cloud import bigquery

# Configuration
BIGQUERY_PROJECT = "propane-will-491900-m5"
BIGQUERY_DATASET = "osvauco_logs"
BIGQUERY_TABLE = "cloud_run_logs"
RESPONSE_TIME_THRESHOLD = 1000  # in milliseconds

def adjust_prompt_parameters():
    """
    Reads telemetry data from BigQuery, analyzes response times,
    and adjusts prompt parameters accordingly.
    """
    try:
        client = bigquery.Client(project=BIGQUERY_PROJECT)
        table_id = f"{BIGQUERY_PROJECT}.{BIGQUERY_DATASET}.{BIGQUERY_TABLE}"

        # 1. Query response time data from BigQuery
        query = f"""
            SELECT AVG(latency_ms) as avg_latency
            FROM `{table_id}`
            WHERE timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR)
        """
        query_job = client.query(query)
        results = query_job.result()

        for row in results:
            avg_latency = row.avg_latency
            print(f"Average response time in the last hour: {avg_latency} ms")

            # 2. Adjust prompt parameters based on response time
            if avg_latency > RESPONSE_TIME_THRESHOLD:
                print("Response time is high. Adjusting prompt parameters to reduce complexity.")
                # Placeholder for logic to adjust prompt parameters
                # For example, reduce max_tokens, use a simpler model, etc.
                pass
            else:
                print("Response time is within acceptable limits.")

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

if __name__ == "__main__":
    adjust_prompt_parameters()