evaluation.py

import openai
import os
import argparse

# Set up OpenAI API credentials
openai.api_key = os.environ["OPENAI_API_KEY"]

# Define the prompt to test the model
def generate_text(prompt, model_engine, temperature, max_tokens):
    # Call the OpenAI API to generate text based on the prompt
    response = openai.Completion.create(
        engine=model_engine,
        prompt=prompt,
        temperature=temperature,
        max_tokens=max_tokens
    )

    # Return the generated text
    return response["choices"][0]["text"]

if __name__ == "__main__":
    # Set up the argparse
    parser = argparse.ArgumentParser(description='Test a large language model on programming problems')
    parser.add_argument('--model_engine', type=str, default="text-davinci-002", help='The name of the OpenAI model engine to use (default: text-davinci-002)')
    parser.add_argument('--temperature', type=float, default=0.5, help='The temperature to use for text generation (default: 0.5)')
    parser.add_argument('--max_tokens', type=int, default=50, help='The maximum number of tokens to generate (default: 50)')

    # Parse the arguments
    args = parser.parse_args()

    # Generate the text based on the arguments
    generated_text = generate_text(args.prompt, args.model_engine, args.temperature, args.max_tokens)

    # Print the generated text
    print(generated_text)