import time
from common.utils.business_event_context import current_event


def tracked_transcribe(client, *args, **kwargs):
    start_time = time.time()

    # Extract the file and model from kwargs if present, otherwise use defaults
    file = kwargs.get('file')
    model = kwargs.get('model', 'whisper-1')
    duration = kwargs.pop('duration', 600)

    result = client.audio.transcriptions.create(*args, **kwargs)
    end_time = time.time()

    # Token usage for transcriptions is actually the duration in seconds we pass, as the whisper model is priced per second transcribed

    metrics = {
        'total_tokens': duration,
        'prompt_tokens': 0,  # For transcriptions, all tokens are considered "completion"
        'completion_tokens': duration,
        'time_elapsed': end_time - start_time,
        'interaction_type': 'ASR',
    }
    current_event.log_llm_metrics(metrics)

    return result