update

util/injection.py

@@ -20,8 +20,9 @@ def create_charateristics(original_resume, group_name, occupation, agent, parame
 
     additional_charateristics = invoke_retry(prompt, agent, parameters)
 
+
     combined_charateristics = f"{original_resume}\n{additional_charateristics}"
-    print(f"Additional characteristics: {additional_charateristics}")
+    print(f"Prompt: {prompt}")
 
     return combined_charateristics
 
@@ -100,36 +101,13 @@ def process_scores_multiple(df, num_run, parameters, privilege_label, protect_la
 
             for key, label in zip(['Privilege', 'Protect', 'Neutral'], [privilege_label, protect_label, False]):
                 prompt_temp = create_summary(group_name, label, occupation, charateristics)
-                print(f"Run {run + 1} - Entry {index + 1} - {key}:\n{prompt_temp}")
-                print("=============================================================")
-                result = invoke_retry(prompt_temp, agent, parameters)
-                scores[key][index].append(result)
-
-    # Assign score lists and calculate average scores
-    for category in ['Privilege', 'Protect', 'Neutral']:
-        df[f'{category}_Scores'] = pd.Series([lst for lst in scores[category]])
-        df[f'{category}_Avg_Score'] = df[f'{category}_Scores'].apply(
-            lambda scores: sum(score for score in scores if score is not None) / len(scores) if scores else None
-        )
-
-    return df
-
-
-def process_scores_single(df, num_run, parameters, counterfactual_label, agent, group_name, occupation):
-    """ Process entries and compute scores concurrently, with progress updates. """
-    scores = {key: [[] for _ in range(len(df))] for key in ['Counterfactual', 'Neutral']}
-
-    for run in tqdm(range(num_run), desc="Processing runs", unit="run"):
-        for index, row in tqdm(df.iterrows(), total=len(df), desc="Processing entries", unit="entry"):
-            for key, label in zip(['Counterfactual', 'Neutral'], [counterfactual_label, False]):
-                prompt_temp = create_summary(row, group_name, label, occupation)
                 print(f"Run {run + 1} - Entry {index + 1} - {key}")
                 print("=============================================================")
                 result = invoke_retry(prompt_temp, agent, parameters)
                 scores[key][index].append(result)
 
     # Assign score lists and calculate average scores
-    for category in ['Counterfactual', 'Neutral']:
+    for category in ['Privilege', 'Protect', 'Neutral']:
         df[f'{category}_Scores'] = pd.Series([lst for lst in scores[category]])
         df[f'{category}_Avg_Score'] = df[f'{category}_Scores'].apply(
             lambda scores: sum(score for score in scores if score is not None) / len(scores) if scores else None