| from openai import OpenAI | |
| client = OpenAI() | |
| OpenAI.api_key = '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' | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| def compute(params): | |
| public_score = 0 | |
| private_score = 0 | |
| solution_file = hf_hub_download( | |
| repo_id=params.competition_id, | |
| filename="solution.csv", | |
| token=params.token, | |
| repo_type="dataset", | |
| ) | |
| solution_df = pd.read_csv(solution_file) | |
| submission_filename = f"submissions/{params.team_id}-{params.submission_id}.csv" | |
| submission_file = hf_hub_download( | |
| repo_id=params.competition_id, | |
| filename=submission_filename, | |
| token=params.token, | |
| repo_type="dataset", | |
| ) | |
| submission_df = pd.read_csv(submission_file) | |
| submitted_answer = str(submission_df.iloc[0]['pred']) | |
| gt = str(solution_df.iloc[0]['pred']) | |
| prompt=f"Give me a score from 1 to 10 (higher is better) judging how similar these two captions are. Caption one: {submitted_answer}. Caption two: {gt}\nScore:" | |
| try: | |
| response = client.completions.create( | |
| engine="gpt-3.5-turbo-instruct", | |
| prompt=prompt, | |
| temperature=0, | |
| max_tokens=1, | |
| ) | |
| public_score = int(response.choices[0].text.strip()) | |
| except: | |
| print("Error w/ api") | |
| private_score = public_score | |
| metric_dict = {"public_score": {"metric1": public_score}, | |
| "private_score": {"metric1": private_score} | |
| } | |
| return metric_dict |