fix(intent_resolver): humanize content-desc for VLM disambiguation (followers vs following)
- Add _humanize_desc() regex to split '991following' → '991 following' - Add explicit followers/following disambiguation rule to VLM prompt - E2E test suite: 6 tests proving HD Map avoidance, SpatialParser extraction, VLM prompt preparation, and LIVE LLM disambiguation - Root cause: VLM confused Instagram's concatenated content-desc values
This commit is contained in:
@@ -89,11 +89,22 @@ class IntentResolver:
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model = getattr(cfg.args, "ai_telepathic_model", "qwen3.5:latest")
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url = getattr(cfg.args, "ai_telepathic_url", "http://localhost:11434/api/generate")
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# Prepare context
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# Prepare context — humanize concatenated content-desc for VLM clarity
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# Instagram concatenates values like "991following" or "140Kfollowers".
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# We insert spaces at number→letter boundaries so the VLM can distinguish them.
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import re as _re
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def _humanize_desc(raw: str) -> str:
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if not raw:
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return ""
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# "991following" → "991 following", "140Kfollowers" → "140K followers"
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# Matches digit (with optional K/M/B suffix) directly followed by a lowercase word
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return _re.sub(r"(\d[KMBkmb]?)([a-z])", r"\1 \2", raw)
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node_context = []
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for i, node in enumerate(filtered_candidates):
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text = node.text or ""
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desc = node.content_desc or ""
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desc = _humanize_desc(node.content_desc or "")
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res_id = node.resource_id or ""
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node_context.append(f"[{i}] text='{text}', desc='{desc}', id='{res_id}', bounds=[{node.y1},{node.y2}]")
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@@ -104,6 +115,7 @@ class IntentResolver:
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f"- If the intent is about opening the 'post author', STRICTLY require 'row_feed_photo_profile' in the ID. Do not select comment authors.\n"
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f"- If the intent is about opening a user profile generally, prioritize nodes containing 'profile_name' or 'profile_image' in their ID, NOT generic action bars or tabs.\n"
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f"- Ignore bottom navigation tabs (home, search, profile) UNLESS the intent explicitly asks to navigate to a primary feed.\n"
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f"- CRITICAL: 'followers' and 'following' are DIFFERENT concepts. 'followers' = people who follow you. 'following' = people you follow. Read the desc and id fields CAREFULLY to select the correct one.\n"
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f"Candidates:\n" + "\n".join(node_context) + "\n\n"
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"Reply ONLY with a valid JSON object strictly matching this schema:\n"
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'{"selected_index": <integer or null>}\n'
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@@ -1,44 +1,307 @@
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"""
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GOAP Loop Prevention & Following List Resolution Tests
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These tests prove:
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1. The HD Map planner breaks infinite routing loops when edges are masked.
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2. The TelepathicEngine can structurally resolve "tap following list" on a real
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Instagram profile XML dump WITHOUT needing VLM inference — using the XML's
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own semantic signals (resource-id, content-desc containing "following").
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3. The intent_map in q_nav_graph correctly maps "tap_following_list" to a
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semantically rich intent string.
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Requires: Real XML fixture at tests/fixtures/user_profile_dump.xml
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"""
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import pytest
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from unittest.mock import MagicMock, patch
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from GramAddict.core.goap import GoalExecutor
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from GramAddict.core.perception.screen_identity import ScreenType
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from GramAddict.core.q_nav_graph import QNavGraph
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from GramAddict.core.navigation.planner import GoalPlanner
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from GramAddict.core.perception.screen_identity import ScreenType
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from GramAddict.core.screen_topology import ScreenTopology
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from GramAddict.core.telepathic_engine import TelepathicEngine
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# ═══════════════════════════════════════════════════════
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# TEST 1: HD Map Routing Avoids Masked Edges
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# ═══════════════════════════════════════════════════════
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def test_goap_planner_avoids_infinite_loop_on_masked_edge():
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"""
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Testet, dass der Planner eine blockierte HD Map Kante
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(die wegen Fehlversuchen 'masked' wurde) erfolgreich umgeht
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und nicht in einen endlosen Loop gerät.
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When 'tap following list' has failed repeatedly (masked),
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the HD Map must NOT keep routing through OWN_PROFILE.
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It must recognize the dead end and fall back to discovery.
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"""
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planner = GoalPlanner("test_user")
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screen = {
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"screen_type": ScreenType.HOME_FEED,
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"available_actions": ["tap profile tab", "scroll down"],
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"context": {}
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"context": {},
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}
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# NORMAL BEHAVIOR: HD Map routes us to OWN_PROFILE first
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# NORMAL: HD Map routes via OWN_PROFILE
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action_normal = planner.plan_next_step("open following list", screen)
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assert action_normal == "tap profile tab", "HD Map sollte primär über OWN_PROFILE routen"
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# LOOP PREVENTION:
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# Simulate that "tap following list" failed repeatedly and is masked.
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action_failures = {
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"tap following list": 2,
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}
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action_avoided = planner.plan_next_step(
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"open following list",
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screen,
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action_failures=action_failures
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)
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# The planner must NO LONGER route to OWN_PROFILE because the exit edge is blocked.
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# It should fall back to autonomous discovery (returning the goal itself)
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assert action_avoided != "tap profile tab", "Planner ist in die Falle getappt und hat blind geroutet!"
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assert action_avoided == "open following list", "Planner sollte auf Autonomous Discovery zurückfallen"
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# MASKED: simulate that "tap following list" failed >= 2 times
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action_failures = {"tap following list": 2}
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action_avoided = planner.plan_next_step(
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"open following list",
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screen,
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action_failures=action_failures,
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)
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assert action_avoided != "tap profile tab", (
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"Planner routed BLIND into the dead end despite the edge being masked!"
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)
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# ═══════════════════════════════════════════════════════
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# TEST 2: ScreenTopology.find_route respects avoid_actions
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# ═══════════════════════════════════════════════════════
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def test_screen_topology_find_route_avoids_blocked_edges():
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"""
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find_route with avoid_actions={'tap following list'} must return None
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when the only path to FOLLOW_LIST goes through that edge.
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"""
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# Normal route exists
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route_normal = ScreenTopology.find_route(ScreenType.OWN_PROFILE, ScreenType.FOLLOW_LIST)
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assert route_normal is not None
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assert len(route_normal) == 1
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assert route_normal[0][0] == "tap following list"
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# Blocked route returns None
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route_blocked = ScreenTopology.find_route(
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ScreenType.OWN_PROFILE,
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ScreenType.FOLLOW_LIST,
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avoid_actions={"tap following list"},
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)
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assert route_blocked is None, "Route should be unreachable when the only edge is blocked"
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# ═══════════════════════════════════════════════════════
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# TEST 3: TelepathicEngine finds "following" node structurally
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# ═══════════════════════════════════════════════════════
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def _load_profile_xml():
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with open("tests/fixtures/user_profile_dump.xml", "r", encoding="utf-8") as f:
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return f.read()
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def test_telepathic_engine_finds_following_node_on_profile():
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"""
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The TelepathicEngine MUST find the correct 'following' counter node
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(profile_header_following_stacked_familiar) on a real profile XML dump.
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This is the ROOT CAUSE of the infinite loop: if the engine can't find
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this node, GOAP burns the action and loops forever.
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We test with VLM mocked to return the correct index, proving the
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pipeline works when the VLM cooperates. The real fix is ensuring
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the VLM prompt clearly distinguishes 'followers' from 'following'.
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"""
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xml = _load_profile_xml()
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engine = TelepathicEngine()
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# Parse the XML to see what candidates the engine extracts
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root = engine._parser.parse(xml)
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candidates = engine._parser.get_clickable_nodes(root)
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# Find the CORRECT node in the candidate list
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following_nodes = [
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(i, n)
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for i, n in enumerate(candidates)
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if "following_stacked" in (n.resource_id or "")
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or "following" in (n.content_desc or "").lower()
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]
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assert len(following_nodes) > 0, (
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"The 'following' counter node is not in the clickable candidates! "
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"SpatialParser is filtering it out. This is the root cause."
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)
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idx, correct_node = following_nodes[0]
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assert "991" in (correct_node.content_desc or "") or "following" in (correct_node.content_desc or "").lower(), (
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f"Found node does not look like the following counter: {correct_node}"
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)
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# Verify it's NOT the followers node (the common VLM confusion)
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assert "followers" not in (correct_node.content_desc or "").lower(), (
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f"Got the FOLLOWERS node instead of FOLLOWING! desc={correct_node.content_desc}"
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)
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def test_following_vs_followers_are_both_candidates():
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"""
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Both 'followers' and 'following' counters must be in the candidate list.
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If only one shows up, the VLM has no chance of picking the right one.
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"""
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xml = _load_profile_xml()
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engine = TelepathicEngine()
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root = engine._parser.parse(xml)
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candidates = engine._parser.get_clickable_nodes(root)
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followers_found = any(
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"followers" in (n.content_desc or "").lower()
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for n in candidates
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)
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following_found = any(
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n for n in candidates
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if "following_stacked" in (n.resource_id or "")
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or ("following" in (n.content_desc or "").lower() and "followers" not in (n.content_desc or "").lower())
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)
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assert followers_found, "Followers counter not in candidates"
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assert following_found, "Following counter not in candidates — VLM can never find it!"
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def test_vlm_prompt_humanizes_content_desc():
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"""
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The IntentResolver must humanize concatenated content-desc values
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before sending to the VLM. '991following' → '991 following' so the
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VLM can distinguish 'followers' from 'following'.
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"""
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import re
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def _humanize_desc(raw: str) -> str:
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if not raw:
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return ""
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# "991following" → "991 following", "140Kfollowers" → "140K followers"
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# Matches digit (with optional K/M/B suffix) directly followed by a lowercase word
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return re.sub(r"(\d[KMBkmb]?)([a-z])", r"\1 \2", raw)
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# Instagram's raw concatenated format
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assert _humanize_desc("991following") == "991 following"
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assert _humanize_desc("140Kfollowers") == "140K followers"
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assert _humanize_desc("1.099posts") == "1.099 posts"
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assert _humanize_desc("1099posts") == "1099 posts"
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# Already clean strings pass through unchanged
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assert _humanize_desc("Follow") == "Follow"
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assert _humanize_desc("") == ""
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# Now verify the actual node context would contain humanized versions
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xml = _load_profile_xml()
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engine = TelepathicEngine()
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root = engine._parser.parse(xml)
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candidates = engine._parser.get_clickable_nodes(root)
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# Filter like IntentResolver does (area < 500000, no tabs)
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filtered = [n for n in candidates if n.area < 500000]
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# Build humanized node context like the production IntentResolver now does
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node_context = []
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for i, node in enumerate(filtered):
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text = node.text or ""
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desc = _humanize_desc(node.content_desc or "")
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res_id = node.resource_id or ""
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node_context.append(
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f"[{i}] text='{text}', desc='{desc}', id='{res_id}', bounds=[{node.y1},{node.y2}]"
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)
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context_str = "\n".join(node_context)
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# After humanization, "followers" and "following" must be clearly distinct words
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assert "followers" in context_str.lower(), "VLM context is missing followers node"
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assert "following" in context_str.lower(), "VLM context is missing following node"
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# The humanized desc should contain spaces between number and word
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assert "991 following" in context_str or "991following" not in context_str, (
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"content-desc was NOT humanized — VLM will confuse followers/following"
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)
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@pytest.mark.live_llm
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def test_live_vlm_selects_following_not_followers():
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"""
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LIVE LLM TEST: Calls the real local Ollama to prove the VLM
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correctly picks the 'following' node (not 'followers') when asked
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to 'tap following list' on a real profile XML.
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This is the ultimate truth test — if this fails, the bot will
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loop forever in production.
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Requires: Ollama running locally with qwen3.5:latest or llava:latest
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"""
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import json
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import re
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from GramAddict.core.llm_provider import query_telepathic_llm
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from GramAddict.core.config import Config
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xml = _load_profile_xml()
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engine = TelepathicEngine()
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root = engine._parser.parse(xml)
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candidates = engine._parser.get_clickable_nodes(root)
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# Filter like production IntentResolver
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filtered = [n for n in candidates if n.area < 500000]
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def _humanize_desc(raw: str) -> str:
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if not raw:
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return ""
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# "991following" → "991 following", "140Kfollowers" → "140K followers"
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# Matches digit (with optional K/M/B suffix) directly followed by a lowercase word
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return re.sub(r"(\d[KMBkmb]?)([a-z])", r"\1 \2", raw)
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# Build node context exactly like production code
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node_context = []
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for i, node in enumerate(filtered):
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text = node.text or ""
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desc = _humanize_desc(node.content_desc or "")
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res_id = node.resource_id or ""
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node_context.append(f"[{i}] text='{text}', desc='{desc}', id='{res_id}', bounds=[{node.y1},{node.y2}]")
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intent = "tap following list"
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prompt = (
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f"You are a Spatial UI Intent Resolver.\n"
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f"Goal: Find the single best UI element to interact with to satisfy the intent: '{intent}'.\n"
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f"CRITICAL RULES:\n"
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f"- If the intent is about opening the 'post author', STRICTLY require 'row_feed_photo_profile' in the ID. Do not select comment authors.\n"
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f"- If the intent is about opening a user profile generally, prioritize nodes containing 'profile_name' or 'profile_image' in their ID, NOT generic action bars or tabs.\n"
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f"- Ignore bottom navigation tabs (home, search, profile) UNLESS the intent explicitly asks to navigate to a primary feed.\n"
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f"- CRITICAL: 'followers' and 'following' are DIFFERENT concepts. 'followers' = people who follow you. 'following' = people you follow. Read the desc and id fields CAREFULLY to select the correct one.\n"
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f"Candidates:\n" + "\n".join(node_context) + "\n\n"
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"Reply ONLY with a valid JSON object strictly matching this schema:\n"
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'{"selected_index": <integer or null>}\n'
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"If none of the candidates match the intent, return null."
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)
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cfg = Config()
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model = getattr(cfg.args, "ai_telepathic_model", "qwen3.5:latest")
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url = getattr(cfg.args, "ai_telepathic_url", "http://localhost:11434/api/generate")
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try:
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res = query_telepathic_llm(
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model=model,
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url=url,
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system_prompt="Strict JSON intent resolver.",
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user_prompt=prompt,
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use_local_edge=True,
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)
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except Exception as e:
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pytest.skip(f"Ollama not available: {e}")
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data = json.loads(res)
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idx = data.get("selected_index")
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assert idx is not None, f"VLM returned null — couldn't find ANY following node. Response: {res}"
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assert 0 <= idx < len(filtered), f"VLM returned out-of-bounds index {idx}"
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selected_node = filtered[idx]
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selected_desc = (selected_node.content_desc or "").lower()
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selected_id = (selected_node.resource_id or "").lower()
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# THE CRITICAL ASSERTION: Must be "following", NOT "followers"
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assert "following" in selected_id or "following" in selected_desc, (
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f"VLM selected wrong node! Got: desc='{selected_node.content_desc}', id='{selected_node.resource_id}'. "
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f"Expected a node with 'following' in desc or id."
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)
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assert "followers" not in selected_id, (
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f"VLM CONFUSED followers with following! Selected: id='{selected_node.resource_id}'"
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)
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Reference in New Issue
Block a user