videollama2/eval/eval_benchmark_2_detailed_orientation.py

import os
import argparse
import json
import ast
from tqdm import tqdm
from multiprocessing.pool import Pool

from openai import AzureOpenAI


def init():
    client = AzureOpenAI(
        azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
        api_key=os.getenv("AZURE_OPENAI_KEY"),  
        api_version="2024-02-15-preview"
    )

    return client


def interaction(client, message_text):
    completion = client.chat.completions.create(
        model=os.getenv("AZURE_OPENAI_DEPLOYNAME"),
        messages = message_text,
        temperature=0.7,
        max_tokens=800,
        top_p=0.95,
        frequency_penalty=0,
        presence_penalty=0,
        stop=None
    )

    return completion


def annotate(prediction_set, caption_files, output_dir, args):
    """
    Evaluates question and answer pairs using GPT-3 and
    returns a score for detailed orientation.
    """
    for file in tqdm(caption_files):
        key = file[:-5] # Strip file extension
        qa_set = prediction_set[key]
        question = qa_set['q']
        answer = qa_set['a']
        pred = qa_set['p']
        try:
            # Compute the detailed-orientation score
            message = [
                    {
                        "role": "system",
                        "content":
                            "You are an intelligent chatbot designed for evaluating the detail orientation of generative outputs for video-based question-answer pairs. "
                            "Your task is to compare the predicted answer with the correct answer and determine its level of detail, considering both completeness and specificity. Here's how you can accomplish the task:"
                            "------"
                            "##INSTRUCTIONS: "
                            "- Check if the predicted answer covers all major points from the video. The response should not leave out any key aspects.\n"
                            "- Evaluate whether the predicted answer includes specific details rather than just generic points. It should provide comprehensive information that is tied to specific elements of the video.\n"
                            "- Consider synonyms or paraphrases as valid matches.\n"
                            "- Provide a single evaluation score that reflects the level of detail orientation of the prediction, considering both completeness and specificity."
                    },
                    {
                        "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 detail orientation score where the detail orientation score is an integer value between 0 and 5, with 5 indicating the highest level of detail orientation. "
                            "Please generate the response in the form of a Python dictionary string with keys 'score', where its value is the detail orientation 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}."
                    }
                ]

            completion = interaction(client, message)
            # 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]

            # 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)

        except Exception as e:
            print(f"Error processing file '{key}': {e}")


def main(args):
    pred_contents = [eval(line) for line in open(args.pred_path, 'r').readlines()]

    # Dictionary to store the count of occurrences for each video_id
    video_id_counts = {}
    new_pred_contents = []

    # Iterate through each sample in pred_contents
    for sample in pred_contents:
        video_id = sample['video_name']
        if video_id in video_id_counts:
            video_id_counts[video_id] += 1
        else:
            video_id_counts[video_id] = 0

        # Create a new sample with the modified key
        new_sample = sample
        new_sample['video_name'] = f"{video_id}_{video_id_counts[video_id]}"
        new_pred_contents.append(new_sample)

    # Generating list of id's and corresponding files
    id_list = [x['video_name'] for x in new_pred_contents]
    caption_files = [f"{id}.json" for id in id_list]

    output_dir = args.output_dir
    # Generate output directory if not exists.
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    # Preparing dictionary of question-answer sets
    prediction_set = {}
    for sample in new_pred_contents:
        id = sample['video_name']
        question = sample['Q']
        answer = sample['A']
        pred = sample['P']
        qa_set = {"q": question, "a": answer, "p": pred}
        prediction_set[id] = qa_set

    # Set the OpenAI API key.
    # openai.api_key = args.api_key
    num_tasks = args.num_tasks

    # While loop to ensure that all captions are processed.
    while True:
        try:
            # Files that have not been processed yet.
            completed_files = os.listdir(output_dir)
            print(f"completed_files: {len(completed_files)}")

            # Files that have not been processed yet.
            incomplete_files = [f for f in caption_files if f not in completed_files]
            print(f"incomplete_files: {len(incomplete_files)}")

            # Break the loop when there are no incomplete files
            if len(incomplete_files) == 0:
                break
            if len(incomplete_files) <= num_tasks:
                num_tasks = 1

            # Split tasks into parts.
            part_len = len(incomplete_files) // num_tasks
            all_parts = [incomplete_files[i:i + part_len] for i in range(0, len(incomplete_files), part_len)]
            task_args = [(prediction_set, part, args.output_dir, args) for part in all_parts]

            # Use a pool of workers to process the files in parallel.
            with Pool() as pool:
                pool.starmap(annotate, task_args)

        except Exception as e:
            print(f"Error: {e}")

    # Combine all the processed files into one
    combined_contents = {}
    json_path = args.output_json

    # Iterate through json files
    for file_name in tqdm(os.listdir(output_dir)):
        if file_name.endswith(".json"):
            file_path = os.path.join(output_dir, file_name)
            with open(file_path, "r") as json_file:
                content = json.load(json_file)
                combined_contents[file_name[:-5]] = content

    # Write combined content to a json file
    with open(json_path, "w") as json_file:
        json.dump(combined_contents, json_file)
    print("All evaluation completed!")

    # Calculate average score
    score_sum = 0
    count = 0
    for key, result in combined_contents.items():
        count += 1
        score_match = result[0]['score']
        score = int(score_match)
        score_sum += score
    average_score = score_sum / count

    print("Average score for detailed orientation:", average_score)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
    parser.add_argument("--pred-path", required=True, help="The path to file containing prediction.")
    parser.add_argument("--output-dir", required=True, help="The path to save annotation json files.")
    parser.add_argument("--output-json", required=True, help="The path to save annotation final combined json file.")
    parser.add_argument("--num-tasks", required=True, type=int, help="Number of splits.")
    parser.add_argument("--api-key", required=True, type=str, help="Azure Openai API key.")
    parser.add_argument("--api-endpoint", required=True, type=str, help="Azure Openai API endpoint.")
    parser.add_argument("--api-deployname", required=True, type=str, help="Azure Openai API deployname.")
    args = parser.parse_args()

    # Set the OpenAI API key.
    os.environ["AZURE_OPENAI_KEY"] = args.api_key
    os.environ["AZURE_OPENAI_ENDPOINT"] = args.api_endpoint
    os.environ["AZURE_OPENAI_DEPLOYNAME"] = args.api_deployname

    client = init()

    main(args)