code: def annotate(prediction_set, caption_files, output_dir):
      """
      Evaluates question and answer pairs using GPT-3 and
      returns a score for contextual understanding.
      """
      for file in caption_files:
          key = file[:-5]  # Strip file extension
          qa_set = prediction_set[key]
          question = qa_set["q"]
          answer = qa_set["a"]
          pred = qa_set["pred"]
          try:
              print(key, "query")
              # Compute the contextual understanding score
              completion = openai.ChatCompletion.create(
                  model="gpt-3.5-turbo",
                  messages=[
                      {
                          "role": "system",
                          "content": "You are an intelligent chatbot designed for evaluating the contextual understanding of generative outputs for video-based question-answer pairs. "
                          "Your task is to compare the predicted answer with the correct answer and determine if the generated response aligns with the overall context of the video content. Here's how you can accomplish the task:"
                          "------"
                          "##INSTRUCTIONS: "
                          "- Evaluate whether the predicted answer aligns with the overall context of the video content. It should not provide information that is out of context or misaligned.\n"
                          "- The predicted answer must capture the main themes and sentiments of the video.\n"
                          "- Consider synonyms or paraphrases as valid matches.\n"
                          "- Provide your evaluation of the contextual understanding of the prediction compared to the answer.",
                      },
                      {
                          "role": "user",
                          "content": "Please evaluate the following video-based question-answer pair:\n\n"
                          f"Question: {question}\n"
                          f"Correct Answer: {answer}\n"
                          f"Predicted Answer: {pred}\n\n"
                          "Provide your evaluation only as a contextual understanding score where the contextual understanding score is an integer value between 0 and 5, with 5 indicating the highest level of contextual understanding. "
                          "Please generate the response in the form of a Python dictionary string with keys 'score', where its value is contextual understanding score in INTEGER, not STRING."
                          "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
                          "For example, your response should look like this: {''score': 4.8}.",
                      },
                  ],
              )
              # Convert response to a Python dictionary.
              response_message = completion["choices"][0]["message"][
                  "content"
              ]
              response_dict = ast.literal_eval(response_message)
              result_qa_pair = [response_dict, qa_set]
  
              print(key, "done")
  
              # Save the question-answer pairs to a json file.
              with open(f"{output_dir}/{key}.json", "w") as f:
                  json.dump(result_qa_pair, f, indent=4)
  
          except Exception as e:
              print(f"Error processing file '{key}': {e}")
  
file_path: llama-vid/llamavid/eval/evaluate_benchmark_3_context.py
imports: import openai
  import os
  import argparse
  import json
  import ast
  from multiprocessing.pool import Pool
label_desc: Use image node_id 2 for calling a global function with example usage: annotate(prediction_set, caption_files, output_dir) without return types