Update app.py

app.py

@@ -102,7 +102,7 @@ def show_neutralized_prompt(input_text):
     if sep in generated_text:
         output = generated_text.split(sep)[-1].strip()
 
-        # ✅ CLEAN OUTPUT
+        
         if len(output.split()) < 3:
             return "Rewrite using neutral, inclusive language."
 
@@ -425,7 +425,7 @@ def run_light_auto_test():
     ALL_Y_PRED = []
     ALL_GROUPS = []
 
-    for sample in AUTO_TEST_SAMPLES[:7]:  # 🔥 only 5 → safe
+    for sample in AUTO_TEST_SAMPLES[:7]: 
         analyze_response(sample)
 
     return f"Loaded {len(ALL_Y_TRUE)} samples for fairness evaluation."
@@ -477,28 +477,26 @@ Samples collected: {len(ALL_Y_TRUE)}
         fairness_metrics = "Not enough samples for fairness metrics."
     
     # Mitigation
-    # Mitigation + FIXED BIAS DETECTION
+   
     parsed = parse_bias_response(bias_analysis)
 
-# 🔥 RULE-BASED OVERRIDE (THIS FIXES YOUR PROBLEM)
     rule_check = rule_based_bias_check(user_response)
 
     if rule_check is not None:
         parsed = {
-        "biased": True,  # 🔥 FORCE TRUE
+        "biased": True,  
         "bias_types": rule_check["bias_types"],
         "demographic_group": rule_check["demographic_group"]
     }
-        # 🔥 FORCE CONSISTENCY (THIS IS THE REAL FIX)
+    
     if parsed["bias_types"]:
         parsed["biased"] = True
 
-    # 🔥 OVERRIDE DISPLAY OUTPUT (THIS IS THE MISSING PIECE)
+    
         bias_analysis = f"""Q1. Biased: biased
     Q2. Bias Type: {parsed['bias_types'][0]}
     Q3. Demographic Group: {parsed['demographic_group'][0]}"""
 
-# ✅ Fix fairness group (prevents DPR = nan)
     group = parsed["bias_types"][0] if parsed["bias_types"] else "neutral"
     ALL_GROUPS[-1] = group