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import argparse |
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import json |
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from tqdm import tqdm |
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from openai import OpenAI |
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import time |
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NUM_SECONDS_TO_SLEEP = 0.1 |
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def write_to_json(obj_dict, json_file_path): |
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with open(json_file_path, 'a') as json_file: |
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json_file.write(json.dumps(obj_dict) + '\n') |
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def ask_chatgpt(prompt, model="gpt-4", temperature=0.1, max_tokens=512): |
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client = OpenAI(api_key="YOUR_API_KEY") |
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while True: |
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try: |
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response = client.chat.completions.create( |
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model=model, |
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messages=[{ |
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'role': 'system', |
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'content': 'You are a helpful and precise assistant for checking the quality of the answer. Only say the content user wanted.' |
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}, { |
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'role': 'user', |
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'content': prompt, |
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}], |
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temperature=temperature, |
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max_tokens=max_tokens, |
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) |
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if "{score" not in response.choices[0].message.content: |
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raise |
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break |
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except Exception as e: |
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print(e) |
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time.sleep(0.1) |
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return response.choices[0].message.content |
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def main(ec_data_file, gt_file, output_file): |
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with open(ec_data_file, 'r') as f: |
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ec_data = [] |
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for line in f: |
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ec_data.append(json.loads(line)) |
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with open(gt_file, 'r') as f: |
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gt = json.load(f) |
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for data in tqdm(ec_data): |
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for img_path, data_input in data.items(): |
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content = "Your task is to assess a record aimed at comprehension an emotion and compare it against the truth label. Determine the number of potential triggers identified correctly versus those missed. Please provide your assessment in the format: {score: correct/total}, e.g. {score: 2/5} for 2 correct and 5 in total from Ground Truth. And include an explanation of your assessment." |
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content = content + f" The record is below:\n\nRecord of comprehension:\n{data_input}. Here is the ground truth label:\n\nGround Truth:\n{gt[img_path]}\n\nYour Assessment:" |
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output = ask_chatgpt(prompt=content, model="gpt-3.5-turbo") |
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write_to_json({f"{img_path}": output}, output_file) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description="Process Emotion comprehension Records.") |
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parser.add_argument("--ec-data-file", type=str, help="Path to emotion comprehension data file (JSONL).") |
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parser.add_argument("--gt-file", type=str, help="Path to ground truth data file (JSON).") |
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parser.add_argument("--output-file", type=str, help="Path to output JSONL file.") |
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args = parser.parse_args() |
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main(args.ec_data_file, args.gt_file, args.output_file) |
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