def get_eval()

in lmms_eval/tasks/ferret/utils.py [0:0]

• 32 lines of code
• 7 McCabe index (conditional complexity)

def get_eval(content: str, max_tokens: int, retries: int = 3):
    global headers

    messages = [
        {
            "role": "system",
            "content": "You are a helpful and precise assistant for checking the quality of the answer.",
        },
        {"role": "user", "content": content},
    ]

    payload = {
        "model": GPT_EVAL_MODEL_NAME,
        "messages": messages,
        "temperature": 0.2,
        "max_tokens": max_tokens,
    }

    for attempt in range(retries):
        try:
            response = requests.post(API_URL, headers=headers, json=payload)
            response.raise_for_status()
            response_data = response.json()

            content = response_data["choices"][0]["message"]["content"].strip()
            if content != "":
                return content, response_data["model"]
            break  # If successful, break out of the loop

        except Exception as e:
            eval_logger.info(f"Attempt {attempt + 1} failed with error: {e}")
            if attempt < retries - 1:  # If we have retries left, sleep and then continue to next attempt
                time.sleep(NUM_SECONDS_TO_SLEEP)
            else:  # If this was the last attempt, log and return empty
                eval_logger.error(f"All {retries} attempts failed. Last error message: {e}")
                return "", ""
    return "", ""