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| import json | |
| import re | |
| import time | |
| import json_repair | |
| import pandas as pd | |
| from tqdm import tqdm | |
| def create_summary(group_name, label, occupation, row, template): | |
| """Generate a dynamic summary for scoring the applicant, excluding the group feature. | |
| The occupation parameter allows customization of the job position. | |
| """ | |
| resume_info = row['Cleaned_Resume'] | |
| # resume_info = resume_info[:int(len(resume_info) * proportion)] | |
| info = f"{group_name}: {label};" if label else '' | |
| summary = template.format( | |
| role=row['Role'], | |
| counterfactual_info=info, | |
| resume_info=resume_info | |
| ) | |
| return summary | |
| def invoke_retry(prompt, agent, parameters, string_input=False): | |
| attempts = 0 | |
| delay = 2 # Initial delay in seconds | |
| max_attempts = 5 # Maximum number of retry attempts | |
| while attempts < max_attempts: | |
| try: | |
| score_text = agent.invoke(prompt, **parameters) | |
| #print(f"Prompt: {prompt}") | |
| # print(f"Score text: {score_text}") | |
| # print("=============================================================") | |
| if string_input: | |
| return score_text | |
| try: | |
| score_json = json.loads(score_text) | |
| except json.JSONDecodeError: | |
| try: | |
| score_json = json.loads( | |
| json_repair.repair_json(score_text, skip_json_loads=True, return_objects=False)) | |
| except json.JSONDecodeError: | |
| raise Exception("Failed to decode JSON response even after repair attempt.") | |
| # score = re.search(r'\d+', score_text) | |
| # return int(score.group()) if score else -1 | |
| #print(f"Score JSON: {score_json}") | |
| return int(score_json['Score']) | |
| except Exception as e: | |
| print(f"Attempt {attempts + 1} failed: {e}") | |
| time.sleep(delay) | |
| delay *= 2 # Exponential increase of the delay | |
| attempts += 1 | |
| return -1 | |
| # raise Exception("Failed to complete the API call after maximum retry attempts.") | |
| def calculate_avg_score(score_list): | |
| if isinstance(score_list, list) and score_list: | |
| valid_scores = [score for score in score_list if score is not None] | |
| if valid_scores: | |
| avg_score = sum(valid_scores) / len(valid_scores) | |
| return avg_score | |
| return None | |
| def process_scores_multiple(df, num_run, parameters, privilege_label, protect_label, agent, group_name, occupation | |
| , template): | |
| print(f"Processing {len(df)} entries with {num_run} runs each.") | |
| """ Process entries and compute scores concurrently, with progress updates. """ | |
| scores = {key: [[] for _ in range(len(df))] for key in ['Privilege', 'Protect', 'Neutral']} | |
| for run in tqdm(range(num_run), desc="Processing runs", unit="run"): | |
| for index, (idx, row) in tqdm(enumerate(df.iterrows()), total=len(df), desc="Processing entries", unit="entry"): | |
| for key, label in zip(['Privilege', 'Protect', 'Neutral'], [privilege_label, protect_label, False]): | |
| prompt_normal = create_summary(group_name, label, occupation, row, template) | |
| # print(f"Run {run + 1} - Entry {index + 1} - {key}") | |
| # print("=============================================================") | |
| result_normal = invoke_retry(prompt_normal, agent, parameters) | |
| scores[key][index].append(result_normal) | |
| #print(f"Scores: {scores}") | |
| # Ensure all scores are lists and calculate average scores | |
| for category in ['Privilege', 'Protect', 'Neutral']: | |
| # Ensure the scores are lists and check before assignment | |
| series_data = [lst if isinstance(lst, list) else [lst] for lst in scores[category]] | |
| df[f'{category}_Scores'] = series_data | |
| # Calculate the average score with additional debug info | |
| df[f'{category}_Avg_Score'] = df[f'{category}_Scores'].apply(calculate_avg_score) | |
| # Add ranks for each score within each row | |
| ranks = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1, ascending=False) | |
| df['Privilege_Rank'] = ranks['Privilege_Avg_Score'] | |
| df['Protect_Rank'] = ranks['Protect_Avg_Score'] | |
| df['Neutral_Rank'] = ranks['Neutral_Avg_Score'] | |
| return df | |