Update app.py

app.py

@@ -197,12 +197,10 @@ def rule_based_bias_check(text):
             
             
 def show_mitigation(bias_type):
-    tip = MITIGATION_PROMPTS.get(
+    return MITIGATION_PROMPTS.get(
         bias_type.lower(),
         "Try rephrasing the prompt using neutral and inclusive language."
     )
-    print("\nMitigation Suggestion:")
-    print(tip)
 
 #function to set up and return LLama 3.2-1b Instruct model transformer and its tokenizer + the Regard classifier model from Hugging Face
 def setup_models():
@@ -415,6 +413,8 @@ def analyze_response(user_response):
         dpr = demographic_parity_ratio(
             ALL_Y_TRUE, ALL_Y_PRED, sensitive_features=ALL_GROUPS
         )
+        if np.isnan(dpr):
+            dpr = 0.0
         eod = equalized_odds_difference(
             ALL_Y_TRUE, ALL_Y_PRED, sensitive_features=ALL_GROUPS
         )
@@ -434,9 +434,15 @@ Samples collected: {len(ALL_Y_TRUE)}
 
 # 🔥 RULE-BASED OVERRIDE (THIS FIXES YOUR PROBLEM)
     rule_check = rule_based_bias_check(user_response)
-    if rule_check:
+
+    if rule_check is not None:
         parsed = rule_check
 
+    # 🔥 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