fix(perception): enforce visual Set of Mark by passing device context globally

GramAddict/core/account_switcher.py

@@ -47,7 +47,7 @@ def verify_and_switch_account(device, nav_graph, target_username):
         telepath = TelepathicEngine.get_instance()
 
         # We ask the semantic engine to find the profile tab, ensuring 100% ID-agnostic behavior
-        profile_tab_node = telepath.find_best_node(xml_dump, "tap profile tab", min_threshold=0.3)
+        profile_tab_node = telepath.find_best_node(xml_dump, "tap profile tab", min_threshold=0.3, device=device)
         if profile_tab_node:
             profile_tab = (profile_tab_node["x"], profile_tab_node["y"])
     except Exception as e:
@@ -113,7 +113,7 @@ def verify_and_switch_account(device, nav_graph, target_username):
             dump_ui_state(
                 device, "identity_guard", {"reason": "account_not_found_in_bottom_sheet", "target": target_username}
             )
-        except:
+        except Exception:
             pass
         # Escape the bottom sheet
         device.press("back")

GramAddict/core/behaviors/obstacle_guard.py

@@ -56,7 +56,7 @@ class ObstacleGuardPlugin(BehaviorPlugin):
             # Check recovery
             new_xml = ctx.device.dump_hierarchy()
             tele = TelepathicEngine.get_instance()
-            best_node = tele.find_best_node(new_xml, intent_description="Dismiss obstacle")
+            best_node = tele.find_best_node(new_xml, intent_description="Dismiss obstacle", device=ctx.device)
             if best_node:
                 ctx.device.click(best_node.get("x", 0), best_node.get("y", 0))
 

GramAddict/core/behaviors/post_data_extraction.py

@@ -30,7 +30,7 @@ class PostDataExtractionPlugin(BehaviorPlugin):
 
     def execute(self, ctx: BehaviorContext) -> BehaviorResult:
         logger.debug("🧩 [PostDataExtraction] Extracting post metadata...")
-        post_data = extract_post_content(ctx.context_xml)
+        post_data = extract_post_content(ctx.context_xml, device=ctx.device)
 
         if post_data:
             ctx.post_data = post_data

GramAddict/core/perception/action_memory.py

@@ -1,10 +1,46 @@
+import json
 import logging
+import re
 from typing import Any, Dict, Optional
 
 from GramAddict.core.perception.spatial_parser import SpatialNode
 
 logger = logging.getLogger(__name__)
 
+
+def _parse_yes_no(response: str) -> Optional[bool]:
+    """Parses a VLM response to find a definitive YES or NO without substring-matching 'not' or 'now'."""
+    text = response.strip()
+
+    # Try parsing as JSON first
+    if text.startswith("{"):
+        try:
+            data = json.loads(text)
+            for k, v in data.items():
+                if str(k).strip().upper() == "YES" or str(v).strip().upper() == "YES":
+                    return True
+                if str(k).strip().upper() == "NO" or str(v).strip().upper() == "NO":
+                    return False
+        except Exception:
+            pass
+
+    text_lower = text.lower()
+    if text_lower.startswith("yes"):
+        return True
+    if text_lower.startswith("no") and not text_lower.startswith("now") and not text_lower.startswith("not"):
+        return False
+
+    has_yes = re.search(r"\byes\b", text_lower) is not None
+    has_no = re.search(r"\bno\b", text_lower) is not None
+
+    if has_yes and not has_no:
+        return True
+    if has_no and not has_yes:
+        return False
+
+    return None
+
+
 # ═══════════════════════════════════════════════════════
 # Semantic Match Keywords — SSOT for intent → element validation
 # ═══════════════════════════════════════════════════════
@@ -189,10 +225,12 @@ class ActionMemory:
                     raise ValueError("No screenshot available from device")
                 response = evaluator._query_vlm(prompt, screenshot)
 
-                if response and "yes" in response.lower() and "no" not in response.lower():
+                decision = _parse_yes_no(response) if response else None
+
+                if decision is True:
                     logger.debug(f"🧠 [ActionMemory] VLM visually confirmed success for '{intent}'.")
                     return True
-                elif response and "no" in response.lower() and "yes" not in response.lower():
+                elif decision is False:
                     logger.warning(
                         f"⚠️ [ActionMemory] VLM visual verification FAILED for '{intent}'. VLM replied: '{response}'"
                     )
@@ -265,10 +303,13 @@ class ActionMemory:
                         prompt = f"The user just attempted to perform the action: '{intent}'. Does the current screen match the expected outcome? Answer ONLY with the word YES or NO."
                         try:
                             response = evaluator._query_vlm(prompt, device.get_screenshot_b64())
-                            if response and "yes" in response.lower() and "no" not in response.lower():
+                            decision = _parse_yes_no(response) if response else None
+                            if decision is True:
                                 return True
                             else:
-                                logger.warning(f"⚠️ [ActionMemory] VLM rejected success for abstract intent '{intent}'.")
+                                logger.warning(
+                                    f"⚠️ [ActionMemory] VLM rejected success for abstract intent '{intent}'. Response: '{response}'"
+                                )
                                 return False
                         except Exception as e:
                             logger.error(f"VLM visual verification failed: {e}")

GramAddict/core/perception/feed_analysis.py

@@ -46,7 +46,7 @@ def has_carousel_in_view(xml_dump: str) -> bool:
     return any(ind in xml_dump for ind in CAROUSEL_INDICATORS)
 
 
-def extract_post_content(context_xml: str) -> dict:
+def extract_post_content(context_xml: str, device=None) -> dict:
     """
     Extracts meaningful content data from the current feed post's XML.
     This is the BOT'S EYES — what it actually "sees" about each post.
@@ -62,14 +62,16 @@ def extract_post_content(context_xml: str) -> dict:
         telepath = TelepathicEngine.get_instance()
 
         # 1. Learn/extract post author dynamically
-        author_node = telepath.find_best_node(context_xml, "post author username header", min_confidence=0.75)
+        author_node = telepath.find_best_node(
+            context_xml, "post author username header", min_confidence=0.75, device=device
+        )
 
         # 🛡️ Anti-Hallucination Guard: Ensure we actually found text.
         if author_node and author_node.get("original_attribs", {}).get("text"):
             result["username"] = author_node["original_attribs"]["text"].strip()
 
         # 2. Learn/extract post media description dynamically
-        media_node = telepath.find_best_node(context_xml, "post media content", min_confidence=0.35)
+        media_node = telepath.find_best_node(context_xml, "post media content", min_confidence=0.35, device=device)
         if media_node and media_node.get("original_attribs", {}).get("desc"):
             result["description"] = media_node["original_attribs"]["desc"].strip()
 

GramAddict/core/perception/intent_resolver.py

@@ -365,6 +365,12 @@ class IntentResolver:
             )
             data = json.loads(res)
             box_idx = data.get("box")
+            if box_idx is None:
+                box_idx = data.get("selected_index")
+            if box_idx is None:
+                box_idx = data.get("box_index")
+            if box_idx is None:
+                box_idx = data.get("index")
 
             if box_idx is not None and box_idx in box_map:
                 selected = box_map[box_idx]