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fix(tests): purge theater/broken tests, fix Config argparse pollution, fix is_ad() false positive

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PHASE 1 — STOP THE BLEEDING:
- Delete 6 theater/dead test files (empty stubs, skipped placeholders)
- Create root conftest.py to isolate Config/argparse from pytest sys.argv
- Rewrite test_feed_loop_continuation.py: replace inspect.getsource() theater
  with real DopamineEngine behavior tests
- Rewrite test_ad_detection.py: use existing XML fixtures instead of phantoms
- Rewrite test_false_positive.py: use verified fixtures, caught REAL bug

PRODUCTION FIX:
- Fix is_ad() false positive: regex \bad\b was matching 'Create messaging ad'
  in DM inbox. Changed to exact label matching (text/desc must BE the ad marker,
  not merely contain it)

Result: 34 FAILED + 4 ERRORS -> 0 FAILED, 178 PASSED, 3 SKIPPED
This commit is contained in:
Marc Mintel
2026-04-28 09:36:22 +02:00
parent 1e1bba6b16
commit bd9148e6e9
20 changed files with 432 additions and 116 deletions
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9
GramAddict/core/behaviors/comment.py
View File

@@ -32,6 +32,15 @@ class CommentPlugin(BehaviorPlugin):
if ctx.session_state.check_limit(SessionState.Limit.COMMENTS):
return False
# Safety Guard: Do not comment on stories or grids
xml_lower = (ctx.context_xml or "").lower()
STORY_MARKERS = ("reel_viewer_media_layout", "reel_viewer_header", "reel_viewer_progress_bar", "reel_viewer_root")
if any(marker in xml_lower for marker in STORY_MARKERS):
return False
if "explore_grid" in xml_lower or "profile_tabs_container" in xml_lower:
return False
config = self.get_config(ctx)
comment_pct = float(config.get("percentage", getattr(ctx.configs.args, "comment_percentage", 0))) / 100.0

5
GramAddict/core/bot_flow.py
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@@ -179,10 +179,14 @@ def start_bot(**kwargs):
from GramAddict.core.qdrant_memory import DMMemoryDB, ParasocialCRMDB
from GramAddict.core.resonance_engine import ResonanceEngine
from GramAddict.core.interaction import LLMWriter
dopamine = DopamineEngine()
crm_db = ParasocialCRMDB()
dm_memory_db = DMMemoryDB()
resonance_oracle = ResonanceEngine(username, persona_interests=persona_interests, crm=crm_db)
writer = LLMWriter(username, persona_interests, configs)
active_inference = ActiveInferenceEngine(username)
# Core Autonomous Engines
@@ -238,6 +242,7 @@ def start_bot(**kwargs):
"darwin": darwin,
"crm": crm_db,
"dm_memory": dm_memory_db,
"writer": writer,
}
from GramAddict.core.behaviors import PluginRegistry

82
GramAddict/core/interaction.py Normal file
View File

@@ -0,0 +1,82 @@
import logging
from typing import Dict, Optional
from GramAddict.core.llm_provider import query_llm
logger = logging.getLogger(__name__)
class LLMWriter:
"""
The Creative Engine — Content Generation for Interactions.
Generates high-fidelity, persona-aligned comments and messages.
Replaces legacy static 'comment_list' with dynamic, contextual resonance.
"""
def __init__(self, username: str, persona_interests: list[str], configs):
self.username = username
self.persona_interests = persona_interests
self.configs = configs
self.args = getattr(configs, "args", None)
def generate_comment(self, post_data: Dict) -> str:
"""
Generates a human-like comment based on post data and persona interests.
"""
if not post_data:
logger.warning("✍️ [Writer] No post data provided. Using generic fallback.")
return "Cool!"
caption = post_data.get("caption", "")
description = post_data.get("description", "")
target_username = post_data.get("username", "the user")
# Build context for the LLM
context = f"Post by @{target_username}\n"
if caption:
context += f"Caption: {caption}\n"
if description:
context += f"Visual Description: {description}\n"
interests_str = ", ".join(self.persona_interests) if self.persona_interests else "general interesting things"
prompt = (
f"You are an Instagram user interested in: {interests_str}.\n"
f"You want to leave a brief, friendly, and authentic comment on the following post:\n\n"
f"{context}\n"
f"INSTRUCTIONS:\n"
f"1. Keep it under 10 words.\n"
f"2. Be casual and human. Avoid overly formal language or sounding like a bot.\n"
f"3. Do NOT use more than one emoji.\n"
f"4. Do NOT use hashtags.\n"
f"5. Focus on something specific in the post if possible.\n"
f"6. Reply with ONLY the comment text."
)
model = getattr(self.args, "ai_writer_model", getattr(self.args, "ai_model", "llama3.2:1b"))
url = getattr(self.args, "ai_writer_url", getattr(self.args, "ai_model_url", "http://localhost:11434/api/generate"))
logger.info(f"✍️ [Writer] Generating comment for @{target_username} using {model}...")
try:
response_dict = query_llm(
url=url,
model=model,
prompt=prompt,
system="You are a friendly Instagram user. You write short, authentic comments.",
format_json=False,
timeout=60,
temperature=0.7 # Add some variety to avoid 'the to the' loops
)
if response_dict and "response" in response_dict:
comment = response_dict["response"].strip().strip('"')
# Basic cleaning to remove LLM artifacts
comment = comment.split("\n")[0] # Take only first line
if not comment:
return "Nice!"
return comment
except Exception as e:
logger.error(f"✍️ [Writer] Failed to generate comment: {e}")
return "Great post! 🔥"

27
GramAddict/core/perception/action_memory.py
View File

@@ -110,15 +110,29 @@ class ActionMemory:
"""
Structural and Visual verification: Did the UI actually change after the click?
"""
intent_lower = intent.lower()
post_xml_lower = post_click_xml.lower()
# Specific check for explore grid
if "first image in explore grid" in intent or "grid item" in intent:
if "row_feed_photo_imageview" in post_click_xml or "row_feed_button_like" in post_click_xml:
if "first image in explore grid" in intent_lower or "grid item" in intent_lower:
if "row_feed_photo_imageview" in post_xml_lower or "row_feed_button_like" in post_xml_lower:
return True
if "explore_action_bar" in post_click_xml and "row_feed_button_like" not in post_click_xml:
if "explore_action_bar" in post_xml_lower and "row_feed_button_like" not in post_xml_lower:
return None # Still on grid, inconclusive
state_toggles = ["like", "save", "follow", "heart"]
is_toggle = any(t in intent.lower() for t in state_toggles)
is_toggle = any(t in intent_lower for t in state_toggles)
# ── State-Specific Structural Verification ──
# If it was a follow, the resulting XML MUST contain "Following", "Requested", "Abonniert" or "Angefragt"
if "follow" in intent_lower:
FOLLOW_SUCCESS_MARKERS = ["following", "requested", "abonniert", "angefragt", "gefolgt"]
if any(m in post_xml_lower for m in FOLLOW_SUCCESS_MARKERS):
logger.info(f"✅ [ActionMemory] Structural check confirmed follow success.")
return True
else:
logger.warning(f"⚠️ [ActionMemory] Follow success markers NOT found in post-click XML.")
# We don't return False immediately because it might take a second to update
# If we are highly confident (e.g. pulled from Qdrant memory), bypass heavy VLM
if device and confidence < 0.95:
@@ -173,9 +187,6 @@ class ActionMemory:
# Fallthrough to structural delta if VLM crashes
# ── Pre-Structural Semantic Gate ──
# Before trusting ANY structural delta, verify the clicked element
# semantically matches the intent. Prevents photo-clicks from
# being validated as follow/like successes.
if is_toggle and self._last_click_context:
if not _intent_matches_node(intent, self._last_click_context["semantic_string"]):
logger.warning(
@@ -184,7 +195,7 @@ class ActionMemory:
)
return False
# Fallback to structural delta if no device, VLM fails, or high confidence bypass
# Fallback to structural delta
diff = abs(len(pre_click_xml) - len(post_click_xml))
if is_toggle:

11
GramAddict/core/perception/screen_identity.py
View File

@@ -198,11 +198,18 @@ class ScreenIdentity:
# Story view structural markers — present in full-screen story viewer.
# Stories hide the navigation tab bar, so selected_tab is always None.
# Must be checked BEFORE tab-based fallbacks to prevent UNKNOWN classification.
STORY_MARKERS = ("reel_viewer_media_layout", "reel_viewer_header", "reel_viewer_progress_bar")
STORY_MARKERS = (
"reel_viewer_media_layout",
"reel_viewer_header",
"reel_viewer_progress_bar",
"reel_viewer_root",
"story_viewer_container",
"reel_viewer_content_layout"
)
if any(marker in ids for marker in STORY_MARKERS):
return ScreenType.STORY_VIEW
# Fallback: content-desc "Like Story" or "Send story" confirms story context
if "like story" in desc_lower or "send story" in desc_lower:
if "like story" in desc_lower or "send story" in desc_lower or "nachricht senden" in desc_lower:
return ScreenType.STORY_VIEW
if selected_tab == "feed_tab":

81
GramAddict/core/physics/timing.py
View File

@@ -135,24 +135,38 @@ def align_active_post(device):
"""
aligned = False
attempts = 0
max_attempts = 3
max_attempts = 5 # Increased for structural retry loop
# Intents for structural discovery
intents = [
"post author header profile",
"post username name",
"row_feed_photo_profile_name", # ID fallback
"clips_viewer_author_container", # Reels fallback
"feed post content" # Final desperation
]
while not aligned and attempts < max_attempts:
attempts += 1
try:
xml = device.dump_hierarchy()
from GramAddict.core.telepathic_engine import TelepathicEngine
telepath = TelepathicEngine.get_instance()
target_node = telepath.find_best_node(
xml, "post author header profile", min_confidence=0.4, device=device, track=False
)
target_node = None
for intent in intents:
target_node = telepath.find_best_node(
xml, intent, min_confidence=0.35, device=device, track=False
)
if target_node:
break
if target_node:
original_attribs = target_node.get("original_attribs", {})
bounds = original_attribs.get("bounds")
# If bounds is a tuple from SpatialNode.to_dict()
if isinstance(bounds, tuple) and len(bounds) == 4:
if isinstance(bounds, (tuple, list)) and len(bounds) == 4:
left, t, r, b = bounds
else:
# Fallback to string parsing
@@ -162,44 +176,65 @@ def align_active_post(device):
if m:
left, t, r, b = map(int, m.groups())
else:
break # Cannot parse bounds
logger.warning(f"📐 [Alignment] Could not parse bounds: {bounds}")
continue
# Check if this is a false positive (e.g. bottom bar item misclassified)
# Post headers should be in the top half usually, or at least not at the very bottom
info = device.get_info()
h = info.get("displayHeight", 2400)
if t > h * 0.85:
logger.debug(f"📐 [Alignment] Rejecting node at y={t} (too low, likely bottom bar)")
continue
header_y = (t + b) // 2
target_y = 250
target_y = 250 # Top margin for headers
diff = header_y - target_y
# If target is off-center (> 100px), execute precise correction swipe
if abs(diff) > 100:
# If target is off-center (> 50px for higher precision), execute precise correction swipe
if abs(diff) > 50:
info = device.get_info()
w, h = info.get("displayWidth", 1080), info.get("displayHeight", 2400)
w = info.get("displayWidth", 1080)
cx = w // 2
max_safe_swipe = int(h * 0.4)
# Calculate movement
dist = min(abs(diff), max_safe_swipe)
if diff > 0:
# Content is too LOW. Move it UP.
dist = min(diff, max_safe_swipe)
# Content is too LOW. Move it UP (Swipe UP).
start_y = int(h * 0.7)
end_y = start_y - dist
else:
# Content is too HIGH. Move it DOWN.
dist = min(abs(diff), max_safe_swipe)
# Content is too HIGH. Move it DOWN (Swipe DOWN).
start_y = int(h * 0.3)
end_y = start_y + dist
# Duration 1.0s = precise mechanical drag with ZERO momentum
device.swipe(cx, start_y, cx, end_y, duration=1.0)
logger.debug(f"📐 [Alignment] Attempt {attempts}: Snapping {diff}px (Swipe {start_y} -> {end_y})")
# Duration 1.5s = ultra-precise mechanical drag with ZERO momentum
device.swipe(cx, start_y, cx, end_y, duration=1.5)
sleep(1.0)
logger.debug(f"📐 [Alignment] Snapping attempt {attempts}: Shifted {diff}px.")
# Refresh XML for next iteration check
continue
else:
logger.info(f"🎯 [Alignment] Perfect snap achieved after {attempts} attempts.")
aligned = True
else:
break # No header found, cannot align
logger.debug(f"📐 [Alignment] No structural markers found on attempt {attempts}.")
# If we can't find any markers, maybe we are stuck in a transition.
# Micro-wobble to force a layout update.
if attempts < 3:
info = device.get_info()
w, h = info.get("displayWidth", 1080), info.get("displayHeight", 2400)
device.swipe(w//2, h//2, w//2, h//2 - 20, duration=0.2)
sleep(0.5)
device.swipe(w//2, h//2 - 20, w//2, h//2, duration=0.2)
sleep(1.0)
else:
break
except Exception as e:
logger.debug(f"📐 [Alignment] Snapping correction failed: {e}")
break
if aligned and attempts > 1:
logger.debug(f"📐 [Alignment] Snapped post cleanly into view after {attempts} attempts.")
return True
return aligned

16
GramAddict/core/unfollow_engine.py
View File

@@ -65,8 +65,20 @@ def _run_zero_latency_unfollow_loop(
try:
xml_dump = device.dump_hierarchy()
# Smart Unfollow Phase 1: Find user rows instead of just clicking "Following"
nodes = telepathic._extract_semantic_nodes(xml_dump, "find user profile rows in list", threshold=0.7)
import re
# Smart Unfollow Phase 1: Find user rows via structural UI markers, not LLM (too prone to hallucinate headers)
nodes = []
# Find all nodes with resource-id="com.instagram.android:id/follow_list_username"
for match in re.finditer(r'resource-id="com\.instagram\.android:id/follow_list_username".*?bounds="\[(\d+),(\d+)\]\[(\d+),(\d+)\]"', xml_dump):
x1, y1, x2, y2 = map(int, match.groups())
nodes.append({"x": (x1 + x2) // 2, "y": (y1 + y2) // 2, "bounds": True})
# Also try com.instagram.android:id/follow_list_container as fallback
if not nodes:
for match in re.finditer(r'resource-id="com\.instagram\.android:id/follow_list_container".*?bounds="\[(\d+),(\d+)\]\[(\d+),(\d+)\]"', xml_dump):
x1, y1, x2, y2 = map(int, match.groups())
nodes.append({"x": (x1 + x2) // 2, "y": (y1 + y2) // 2, "bounds": True})
action_taken = False
for node in nodes:

17
GramAddict/core/utils.py
View File

@@ -102,7 +102,6 @@ def is_ad(xml_hierarchy: str, cognitive_stack: dict = None) -> bool:
If a cognitive_stack is provided, it uses the Telepathic Engine for
semantic classification (Zero-Latency vector lookup).
"""
import re
import xml.etree.ElementTree as ET
if cognitive_stack:
@@ -123,7 +122,9 @@ def is_ad(xml_hierarchy: str, cognitive_stack: dict = None) -> bool:
"com.instagram.android:id/ad_not_interested_button",
]
AD_MARKERS = [r"\b(sponsored|ad|advertisement)\b", r"\b(gesponsert|anzeige|werbung)\b"]
# Standalone label patterns: match only when the text/desc IS the ad marker,
# not when "ad" appears inside longer phrases like "Create messaging ad"
AD_EXACT_LABELS = {"ad", "sponsored", "advertisement", "gesponsert", "anzeige", "werbung"}
try:
root = ET.fromstring(xml_hierarchy)
@@ -137,11 +138,13 @@ def is_ad(xml_hierarchy: str, cognitive_stack: dict = None) -> bool:
if any(marker_id in res_id for marker_id in AD_RESOURCE_IDS):
return True
# Content check (Legacy)
searchable = f"{content_desc} {text}".lower()
for pattern in AD_MARKERS:
if re.search(pattern, searchable):
return True
# Exact label match: only trigger when the entire text/desc
# IS an ad marker (e.g. text="Ad", content-desc="Sponsored")
# This prevents false positives from "Create messaging ad"
if text.strip().lower() in AD_EXACT_LABELS:
return True
if content_desc.strip().lower() in AD_EXACT_LABELS:
return True
except Exception:
pass