metric.py

import os
from openai import OpenAI
if "OPENAI" in os.environ:
    pass
else:
    print('Doesn\'t find OPENAI')
client = OpenAI(api_key = os.environ['OPENAI'])

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)

    public_ids = solution_df[solution_df.split == "public"][params.submission_id_col].values
    private_ids = solution_df[solution_df.split == "private"][params.submission_id_col].values

    public_solution_df = solution_df[solution_df[params.submission_id_col].isin(public_ids)]
    public_submission_df = submission_df[submission_df[params.submission_id_col].isin(public_ids)]

    private_solution_df = solution_df[solution_df[params.submission_id_col].isin(private_ids)]
    private_submission_df = submission_df[submission_df[params.submission_id_col].isin(private_ids)]

    public_solution_df = public_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
    public_submission_df = public_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)

    private_solution_df = private_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
    private_submission_df = private_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)

    # METRICS Calculation Evaluation
    # _metric = SOME METRIC FUNCTION
    def _metric(outputs, targets):
        # input example: public_solution_df[target_cols], public_submission_df[target_cols]
        
        score = 0.5
        return score

    target_cols = [col for col in solution_df.columns if col not in [params.submission_id_col, "split"]]
    public_score = _metric(public_solution_df[target_cols], public_submission_df[target_cols])
    private_score = _metric(private_solution_df[target_cols], private_submission_df[target_cols])



    ## LLM Scoring Evaluation
    

    def _metric(outputs, targets):
        # input example: public_solution_df[target_cols], public_submission_df[target_cols]
        
        score = 0.5
        return score
        
    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