Fix stray div in UI and wire MongoDB auto-logging for all moderation results

ml-service/app/services/moderation_service.py

@@ -4,12 +4,15 @@ SafeChat — Moderation Service
 Orchestrates the full moderation pipeline:
   1. Classify text for toxicity (fine-tuned HingBERT)
   2. Generate polite alternative via LLM if toxic
-  3. Return combined result
+  3. Auto-log every result to MongoDB for continuous learning
+  4. Return combined result
 
 This is the main entry point called by the API routes.
 """
 
 import time
+import uuid
+from datetime import datetime, timezone
 from typing import Dict, List, Optional
 
 from loguru import logger
@@ -18,6 +21,33 @@ from app.models.model_manager import model_manager
 from app.schemas.moderation import ModerationResponse
 
 
+async def _log_to_mongodb(text: str, result: ModerationResponse) -> None:
+    """Fire-and-forget: save every moderation result to MongoDB for training data."""
+    try:
+        from app.services.feedback_service import feedback_service
+        if feedback_service.collection is None:
+            return
+        doc = {
+            "message_id": str(uuid.uuid4()),
+            "text": text,
+            "is_toxic": result.is_toxic,
+            "overall_score": result.overall_score,
+            "severity": result.severity,
+            "categories": result.categories,
+            "detected_language": result.detected_language,
+            "suggestion": result.suggestion,
+            "model_version": result.model_version,
+            "inference_time_ms": result.inference_time_ms,
+            "logged_at": datetime.now(timezone.utc),
+            # Moderator correction fields (filled later via /feedback endpoint)
+            "model_prediction_was_correct": None,
+        }
+        await feedback_service.collection.insert_one(doc)
+    except Exception as e:
+        logger.warning(f"MongoDB auto-log failed (non-critical): {e}")
+
+
+
 class ModerationService:
     """
     Orchestrates toxicity classification + LLM detoxification.
@@ -68,7 +98,7 @@ class ModerationService:
 
         total_time_ms = int((time.perf_counter() - start_time) * 1000)
 
-        return ModerationResponse(
+        response = ModerationResponse(
             is_toxic=classification["is_toxic"],
             overall_score=classification["overall_score"],
             severity=classification["severity"],
@@ -79,6 +109,11 @@ class ModerationService:
             inference_time_ms=total_time_ms,
         )
 
+        # Auto-log to MongoDB for continuous learning (non-blocking)
+        await _log_to_mongodb(text, response)
+
+        return response
+
     @staticmethod
     async def moderate_batch(
         texts: List[str],

python-frontend/app.py

@@ -385,9 +385,9 @@ for idx, msg in enumerate(st.session_state.messages):
             <div style="font-size:0.78rem; color:#9ca3af; margin-bottom:6px; font-weight:500;">
                 CATEGORY PROBABILITIES
             </div>
-            {render_categories(mod.get('categories', dict()))}
         </div>
         """, unsafe_allow_html=True)
+        st.markdown(render_categories(mod.get('categories', dict())), unsafe_allow_html=True)
 
         # Show suggestion
         suggestion = mod.get("suggestion") or msg.get("detoxified")