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ai_responses_demo.py

@@ -440,8 +440,9 @@ def get_llama_guard_rating(together_client, user_input, user_response):
 # OpenAI moderation
 def get_openai_moderation(openai_client, user_input, user_response):
     try:
-        # Make the API call
+        start_time = time.time()
         response = openai_client.moderations.create(input=user_response, model="omni-moderation-latest")
+        end_time = time.time()
 
         moderation_result = response.results[0]
         flagged = moderation_result.flagged
@@ -449,63 +450,38 @@ def get_openai_moderation(openai_client, user_input, user_response):
         safety_level = "unsafe" if flagged else "safe"
 
         categories = moderation_result.categories
-        high_risk_categories = {}
-        
-        # Check each category explicitly
-        for cat_name in ["hate", "hate_threatening", "harassment", "harassment_threatening", 
-                         "self_harm", "self_harm_intent", "self_harm_instructions", 
-                         "violence", "violence_graphic", "sexual", "sexual_minors", 
-                         "illicit", "illicit_violent"]:
-            if hasattr(categories, cat_name):
-                score = getattr(categories, cat_name)
-                if score > 0.5:
-                    high_risk_categories[cat_name] = score
+        high_risk_categories = {
+            category: score
+            for category, score in vars(categories).items()
+            if isinstance(score, (int, float)) and score > 0.5
+        }
         
-        # Check for warning level
-        if not flagged:
-            for cat_name in ["hate", "hate_threatening", "harassment", "harassment_threatening", 
-                            "self_harm", "self_harm_intent", "self_harm_instructions", 
-                            "violence", "violence_graphic", "sexual", "sexual_minors", 
-                            "illicit", "illicit_violent"]:
-                if hasattr(categories, cat_name):
-                    score = getattr(categories, cat_name)
-                    if score > 0.3:
-                        safety_level = "warning"
-                        break
+        if not flagged and any(score > 0.3 for score in vars(categories).values() if isinstance(score, (int, float))):
+            safety_level = "warning"
 
         formatted_result = f"Safety Status: {safety_status}\n"
         if high_risk_categories:
             formatted_result += "Flagged Categories (Confidence > 0.5):\n"
             for category, score in high_risk_categories.items():
-                formatted_result += f"  - {category.replace('_', ' ').title()}: {score:.2f}\n"
+                formatted_result += f"  - {category}: {score:.2f}\n"
         else:
             formatted_result += "Flagged Categories: None\n"
         
         return formatted_result, safety_level
-        
     except Exception as e:
-        error_msg = str(e)
-        
-        # Handle rate limit errors with a more user-friendly message
-        if "429" in error_msg or "Too Many Requests" in error_msg:
-            return (
-                "OpenAI Moderation temporarily unavailable due to rate limiting.\n"
-                "Please try again in a few minutes.",
-                "warning"
-            )
-        else:
-            return f"OpenAI Moderation unavailable.\nError: {error_msg[:100]}...", "warning"
+        return f"Safety Status: Error\nError: {str(e)}", "unsafe"
 
 # NEW APPROACH: Instead of modal, show/hide the knowledge directly in the page
 def rate_user_interaction(user_input, user_response):
     # Initialize APIs with hardcoded keys
     contextual_api = ContextualAPIUtils(api_key=ORACLE_API_KEY)
     together_client = Together(api_key=TOGETHER_API_KEY)
+    openai_client = openai.OpenAI(api_key=OPENAI_API_KEY)
     
     # Get ratings
     llama_rating, llama_safety = get_llama_guard_rating(together_client, user_input, user_response)
     contextual_rating, contextual_retrieval, contextual_safety = get_contextual_rating(contextual_api, user_input, user_response)
-    openai_rating, openai_safety = get_openai_moderation(OPENAI_CLIENT, user_input, user_response)
+    openai_rating, openai_safety = get_openai_moderation(openai_client, user_input, user_response)
     
     # Format responses carefully to avoid random line breaks
     llama_rating = re.sub(r'\.(?=\s+[A-Z])', '.\n', llama_rating)