feat(perception): integrate VLM visual ad detection into resonance vibe check to block obfuscated ads

GramAddict/core/behaviors/resonance_evaluator.py

@@ -66,6 +66,12 @@ class ResonanceEvaluatorPlugin(BehaviorPlugin):
                         "✨ [Resonance] VLM vibe check returned None (truncated JSON?). Keeping neutral score."
                     )
                 else:
+                    if vibe.get("is_ad"):
+                        logger.info("🛡️ [Resonance Oracle] Visually identified post as an Ad! Skipping...")
+                        from GramAddict.core.utils import humanized_scroll
+                        humanized_scroll(ctx.device)
+                        return BehaviorResult(executed=True, should_skip=True)
+
                     # BUG 6 Fix: VLM returns {"should_like": true/false}, not "quality_score"
                     should_like = vibe.get("should_like", False)
                     vibe_score = 1.0 if should_like else 0.2

GramAddict/core/perception/semantic_evaluator.py

@@ -117,11 +117,13 @@ class SemanticEvaluator:
             You are a user with the following interests: {', '.join(persona_interests)}.
             You are looking at an Instagram post.
             Evaluate if this post is highly relevant to your interests and if you should like/comment on it.
+            CRITICAL: Check if this post is an advertisement or sponsored content (look for "Sponsored", "Ad", or promotional product placement).
 
             Reply ONLY in valid JSON format:
             {{
                 "should_like": true/false,
                 "should_comment": true/false,
+                "is_ad": true/false,
                 "reasoning": "brief explanation"
             }}
             """