import os from openai import OpenAI if "OPENAI" in os.environ: print('Loaded OPENAI Key.') else: print('Can not load the OPENAI key.') 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 print('public_solution_df', public_solution_df) print('private_solution_df', private_solution_df) ## LLM Scoring Evaluation def _metric(outputs, targets): # inputs: public_solution_df[target_cols], public_submission_df[target_cols] # output: score for row, output in outputs.iterrows(): print('outputs type', type(outputs), 'targets type', type(outputs)) answer = str(output['pred']) label = str(targets.iloc[row]['pred']) print('answer:', answer) print('label:', label) prompt=f"Give me a score from 1 to 10 (higher is better) judging how similar these two captions are. Caption one: {answer}. Caption two: {label}\nScore:" response = client.chat.completions.create( engine="gpt-3.5-turbo-instruct", prompt=prompt, temperature=0, max_tokens=1, ) eval_result = response.choices[0].text.strip() print('eval_result', eval_result) score = int(eval_result) # try: # except: # print("Error: API Calling") # return 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]) metric_name = "metric1" metric_dict = {"public_score": {metric_name: public_score}, "private_score": {metric_name: private_score} } return metric_dict