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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
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