purge: remove ALL hardcoded UI markers + model defaults from SAE
TDD cycle (RED → GREEN): SAE perceive() — Zero Maintenance: - Removed ALL hardcoded marker tuples (instagram_modal_markers, creation_flow_markers, dismiss_button_patterns, blocked_markers) - Removed ALL hardcoded model names (llava:latest, qwen3.5:latest) - Removed ALL hardcoded URLs (localhost:11434) - Removed naked except blocks with model fallback defaults New autonomous flow (zero hardcoded UI identifiers): 1. Package-based foreign app detection (Android-level, not app-specific) 2. Qdrant semantic cache (O(1) recall of learned screen types) 3. ScreenIdentity structural delegation (MODAL/FOREIGN_APP) 4. LLM autonomous classification (first-encounter learning then cached) GOAP smart UI polling: - Replaced static random.uniform(1.6, 2.8) sleep with MAX_POLLS=5 polling loop that detects actual UI transitions Model config centralized via _get_model_config(): - ALL model/URL lookups go through Config() SSOT - Fail Fast - no silent hardcoded fallbacks Tests: 9 new, 119 passing, 0 regressions
This commit is contained in:
@@ -30,11 +30,13 @@ Found in `sensors/honeypot_radome.py`.
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- **VLM Sanity Guard**: Woven into `telepathic_engine.py`, it sends semantic matches for destructive actions (Like/Follow) through a Vision Language Model step to prevent executing semantic "Bait and Switch" tricks.
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### 🧠 Situational Awareness Engine (SAE)
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Found in `situational_awareness.py`. Handles autonomous obstacle detection, recovery, and learning without hardcoded rules.
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- **3-Layer Modal Fast-Path**: Eliminates LLM hallucination traps for Instagram-internal modals (surveys, rating prompts) via O(1) deterministic structural checks:
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1. **Resource-ID Guard**: Detects internal blocking overlays (e.g., `survey_overlay_container`, `nux_overlay`).
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2. **Dismiss-Button Heuristic**: Cross-validates typical negative actions ("Not Now", "Take Survey") with overlay structures to prevent false positives in post captions.
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3. **Zero-Deception Fallback**: If structural markers fail, falls back to `ScreenMemoryDB` and ultimately the LLM. Structured invariants always override the semantic cache.
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Found in `situational_awareness.py`. Handles autonomous obstacle detection, recovery, and learning with **ZERO hardcoded UI element identifiers**.
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- **Autonomous 3-Layer Classification** (zero maintenance — no resource-ids, no button text, no localized strings):
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1. **Package-Based Foreign App Detection**: If our app's package is absent from the XML hierarchy, it's a foreign app. Uses Android package names (not Instagram UI elements).
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2. **Qdrant Semantic Cache**: Previously classified screens are instantly recalled from the vector database (O(1) latency). The bot learns from every first-encounter.
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3. **ScreenIdentity Structural Delegation**: The `ScreenIdentity` module classifies known screen types via its own structural logic. If it identifies a MODAL, the SAE trusts it.
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4. **LLM Autonomous Classification**: Unknown screens are classified by the LLM (OBSTACLE_MODAL, DANGER_ACTION_BLOCKED, or NORMAL). Results are cached in Qdrant — so each screen type is learned exactly once.
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- **Zero-Maintenance Guarantee**: When Instagram updates its UI (changes resource-ids, adds new modals), the bot discovers and learns the new patterns autonomously via the LLM. No code changes required.
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### 🦾 Biometric Facade (Gaussian Clicks)
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Found in `device_facade.py`.
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@@ -388,12 +388,22 @@ class GoalExecutor:
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# Execute click
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self.device.click(obj=best_node)
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import random
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time.sleep(random.uniform(1.6, 2.8))
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# Verify success via Goal Context + Screen Feedback
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post_xml = self.device.dump_hierarchy()
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# ── Smart UI Stabilization Poll ──
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# Instead of a static sleep (which is either too long on fast devices or
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# too short on slow ones), we poll dump_hierarchy multiple times.
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# As soon as the XML changes, we know the UI has transitioned.
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MAX_POLLS = 5
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POLL_INTERVAL = 0.5 # seconds between polls
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post_xml = xml_dump # Start with pre-click state
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for poll in range(MAX_POLLS):
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time.sleep(POLL_INTERVAL)
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post_xml = self.device.dump_hierarchy()
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if post_xml != xml_dump:
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logger.debug(f"[GOAP Poll] UI change detected on poll {poll + 1}/{MAX_POLLS}.")
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break
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else:
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logger.debug(f"[GOAP Poll] No UI change after {MAX_POLLS} polls ({MAX_POLLS * POLL_INTERVAL}s).")
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pre_action_screen = self.perceive(xml_dump) # Screen state BEFORE the click
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post_screen = self.perceive(post_xml)
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post_screen_type = post_screen["screen_type"]
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@@ -291,45 +291,53 @@ class SituationalAwarenessEngine:
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stable = re.sub(r"Battery \d+ per cent", "Battery NN per cent", stable)
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return hashlib.sha256(stable.encode()).hexdigest()[:32]
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def _get_model_config(self):
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"""
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Get model/URL config from Config() — the SINGLE source of truth.
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Crashes loudly if config is unavailable (Fail Fast, no silent defaults).
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"""
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from GramAddict.core.config import Config
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cfg = Config()
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args = cfg.args
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return {
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"model": getattr(args, "ai_model", None),
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"url": getattr(args, "ai_model_url", None),
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"telepathic_model": getattr(args, "ai_telepathic_model", None),
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"telepathic_url": getattr(args, "ai_telepathic_url", None),
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}
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def perceive(self, xml_dump: str) -> SituationType:
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"""
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Fast structural classification — NO LLM needed for perception.
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Uses package names + structural markers to classify.
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Autonomous situation classification — ZERO hardcoded UI element identifiers.
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Flow:
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1. Empty/invalid XML → FOREIGN_APP
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2. Hardware check (screen off) → LOCKED_SCREEN
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3. Package-based detection (app-agnostic, zero maintenance):
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- Permission controller packages → OBSTACLE_SYSTEM
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- App package missing → OBSTACLE_FOREIGN_APP
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4. Qdrant semantic cache → instant recall of learned screen types
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5. ScreenIdentity structural delegation → if MODAL, return OBSTACLE_MODAL
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6. LLM classification fallback → autonomous discovery of new obstacle types
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NO hardcoded resource-ids, NO hardcoded button texts, NO localized strings.
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The bot discovers and learns ALL obstacles autonomously via the LLM + Qdrant loop.
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"""
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if not xml_dump or not isinstance(xml_dump, str):
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return SituationType.OBSTACLE_FOREIGN_APP
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blocked_markers = [
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"try again later",
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"action blocked",
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"restrict certain activity",
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"help us confirm you own",
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"confirm it's you",
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"später erneut versuchen",
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"bestätige, dass du es bist",
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"handlung blockiert",
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"eingeschränkt",
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]
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# Guard: Check if the text matches are relatively isolated (e.g. short strings).
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# If the string is buried inside a 200-character caption, it's a false positive.
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# We can regex match text="..." attributes that are less than 60 characters total,
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# OR just use the compressed string where text is capped at 60 chars anyway.
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compressed_lower = self._compress_xml(xml_dump).lower()
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if any(re.search(rf"(?:text|desc)='[^']*?{m}[^']*?'", compressed_lower) for m in blocked_markers):
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# To be extra safe against false positives, check if there's a dialog/modal container
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if "dialog" in compressed_lower or "bottom_sheet" in compressed_lower or "alert" in compressed_lower:
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return SituationType.DANGER_ACTION_BLOCKED
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# ── Hardware Guard: Screen Off / Locked ──
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if not getattr(self.device.deviceV2, "info", {}).get("screenOn", True):
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logger.info("📱 [SAE Perceive] Screen is physically OFF.")
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return SituationType.OBSTACLE_LOCKED_SCREEN
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# ── System Dialog / Permission Detect (Fast Path) ──
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packages = set(re.findall(r'package=["\']([^"\']+)["\']', xml_dump))
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# ── System Dialog / Permission Detect (package-based, app-agnostic) ──
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packages = set(re.findall(r'package=["\'](.[^"\']+)["\']+', xml_dump))
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app_id = getattr(self.device, "app_id", "com.instagram.android")
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# Permission controller packages are Android system-level — not app-specific.
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# These will NEVER change with an Instagram update. Completely safe to check.
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system_dialog_pkgs = {
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"com.google.android.permissioncontroller",
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"com.android.permissioncontroller",
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@@ -339,46 +347,24 @@ class SituationalAwarenessEngine:
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logger.info("📱 [SAE Perceive] System permission dialog explicitly detected.")
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return SituationType.OBSTACLE_SYSTEM
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# ── Foreign Environment Detection (package-based) ──
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# If the main app package is completely absent from the UI hierarchy,
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# OR if there's a dominant foreign package and no app package, we might have lost the app.
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# If our app is on screen, we trust we are in the app (even if a custom keyboard is open).
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# We only trigger foreign app classification if our app is completely missing from the screen.
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is_foreign = False
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if packages and app_id not in packages:
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is_foreign = True
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# ── Foreign Environment Detection (package-based, zero maintenance) ──
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# If our app package is completely absent from the screen → foreign app.
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# Package names are Android-level identifiers, NOT Instagram UI elements.
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is_foreign = bool(packages) and app_id not in packages
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if is_foreign:
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# ── Tier 1: Known Foreign Packages (O(1) — ZERO LLM) ──
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# Production bug 2026-05-03: Play Store was detected via slow LLM path.
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# For these well-known packages, a set lookup is instant and infallible.
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KNOWN_FOREIGN_PACKAGES = {
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"com.android.vending", # Play Store
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"com.android.chrome", # Chrome
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"com.google.android.chrome", # Chrome (Google build)
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"com.google.android.youtube", # YouTube
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"org.mozilla.firefox", # Firefox
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"com.opera.browser", # Opera
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"com.brave.browser", # Brave
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"com.microsoft.emmx", # Edge
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"com.sec.android.app.sbrowser", # Samsung Browser
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}
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# Any package that isn't our app AND isn't just systemui = foreign
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dominant_pkgs = packages - {"com.android.systemui"}
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fast_match = dominant_pkgs & KNOWN_FOREIGN_PACKAGES
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if fast_match:
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logger.info(
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f"🚨 [SAE Perceive] Known foreign package: {fast_match} → "
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f"OBSTACLE_FOREIGN_APP (O(1) fast-path, no LLM needed)"
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)
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if dominant_pkgs:
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logger.info(f"🚨 [SAE Perceive] Foreign package detected: {dominant_pkgs} → OBSTACLE_FOREIGN_APP")
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return SituationType.OBSTACLE_FOREIGN_APP
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# ── Tier 2: Unknown/Ambiguous Packages → LLM Classification ──
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# Only SystemUI-only or rare custom packages reach this path.
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# SystemUI-only edge case: could be lock screen, notification shade, etc.
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# Use LLM for disambiguation.
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try:
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from GramAddict.core.config import Config
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from GramAddict.core.llm_provider import query_telepathic_llm
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cfg = self._get_model_config()
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screen_off = not getattr(self.device.deviceV2, "info", {}).get("screenOn", True)
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prompt = (
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@@ -391,17 +377,9 @@ class SituationalAwarenessEngine:
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f"XML:\n{self._compress_xml(xml_dump)[:2500]}"
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)
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args = {}
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try:
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args = Config().args
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except Exception:
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pass
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model = getattr(args, "ai_model", "qwen3.5:latest")
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url = getattr(args, "ai_model_url", "http://localhost:11434/api/generate")
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res = query_telepathic_llm(
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model=model,
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url=url,
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model=cfg["model"],
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url=cfg["url"],
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system_prompt="Strict JSON classifier.",
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user_prompt=prompt,
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use_local_edge=True,
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@@ -412,140 +390,86 @@ class SituationalAwarenessEngine:
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situ_str = data.get("situation", "")
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if situ_str == "OBSTACLE_LOCKED_SCREEN":
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logger.info("🧠 [Smart Perceive] SystemUI definitively classified as: LOCKED_SCREEN.")
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logger.info("🧠 [Smart Perceive] SystemUI classified as: LOCKED_SCREEN.")
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return SituationType.OBSTACLE_LOCKED_SCREEN
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elif situ_str == "OBSTACLE_SYSTEM":
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logger.info("🧠 [Smart Perceive] SystemUI definitively classified as: SYSTEM_DIALOG.")
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logger.info("🧠 [Smart Perceive] SystemUI classified as: SYSTEM_DIALOG.")
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return SituationType.OBSTACLE_SYSTEM
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else:
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logger.info("🧠 [Smart Perceive] SystemUI classified as: FOREIGN_APP / NOTIFICATION.")
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logger.info("🧠 [Smart Perceive] SystemUI classified as: FOREIGN_APP.")
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return SituationType.OBSTACLE_FOREIGN_APP
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except Exception as e:
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logger.warning(f"⚠️ [Smart Perceive] LLM Classification failed ({e}). Defaulting to FOREIGN_APP.")
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return SituationType.OBSTACLE_FOREIGN_APP
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# ── Modal/Obstacle Detection (Autonomous LLM + Memory) ──
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# We explicitly query ScreenMemoryDB. If unknown, we ask the LLM.
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# This replaces ALL brittle string/ID matching for modals.
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# ── In-App Obstacle Detection (100% autonomous, ZERO hardcoded UI identifiers) ──
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# The bot learns ALL obstacle types via the LLM + Qdrant feedback loop.
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# First encounter: LLM classifies → result is cached in Qdrant.
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# All subsequent encounters: instant O(1) recall from cache.
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from GramAddict.core.qdrant_memory import ScreenMemoryDB
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screen_memory = ScreenMemoryDB()
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compressed = self._compress_xml(xml_dump)
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# ── Priority 1: Qdrant Semantic Cache (O(1), zero LLM calls) ──
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cached_type = screen_memory.get_screen_type(compressed)
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if cached_type:
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if cached_type == "OBSTACLE_MODAL":
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return SituationType.OBSTACLE_MODAL
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elif cached_type == "DANGER_ACTION_BLOCKED":
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return SituationType.DANGER_ACTION_BLOCKED
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elif cached_type == "NORMAL":
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return SituationType.NORMAL
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# ── Structural Fast-Check: Content-Creation Overlays ──
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# These full-screen overlays live INSIDE Instagram's package but block
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# all normal navigation. They are invisible to the foreign-app detector
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# and frequently fool the LLM into thinking they are "normal" browsing.
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# Detecting them structurally is O(1) and requires ZERO LLM calls.
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# This is checked AFTER Qdrant to ensure that if the LLM unlearned a false positive,
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# we respect the learned NORMAL state and don't infinite-loop.
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creation_flow_markers = (
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"quick_capture", # Camera / story capture overlay
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"gallery_cancel_button", # Story gallery "Back to Home" button
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"creation_flow", # Post creation wizard
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"reel_camera", # Reel recording interface
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)
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# ── Priority 2: ScreenIdentity structural delegation ──
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# ScreenIdentity classifies the screen using its own structural logic.
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# If it says MODAL → trust it (it uses the same zero-trust approach).
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from GramAddict.core.perception.screen_identity import ScreenIdentity, ScreenType
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# Guard: Use the RAW xml_dump to avoid truncation of root containers (Z-index filtering),
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# but ensure we only match inside resource-id attributes to prevent false positives from user text.
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if any(
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re.search(rf'resource-id="[^"]*{marker}[^"]*"', xml_dump, re.IGNORECASE) for marker in creation_flow_markers
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):
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logger.info("🧠 [SAE Perceive] Content-creation overlay detected structurally → OBSTACLE_MODAL")
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screen_id = ScreenIdentity(getattr(self.device, "bot_username", ""))
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screen_result = screen_id.identify(xml_dump)
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screen_type = screen_result.get("screen_type", ScreenType.UNKNOWN)
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if screen_type == ScreenType.MODAL:
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logger.info("🧠 [SAE Perceive] ScreenIdentity classified as MODAL → OBSTACLE_MODAL")
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screen_memory.store_screen(compressed, "OBSTACLE_MODAL")
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return SituationType.OBSTACLE_MODAL
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# ── Structural Fast-Check: Instagram-Internal Modal Overlays ──
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# Surveys, rating prompts, and interstitial modals live INSIDE Instagram's
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# package but block normal interaction. They share a common structural
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# pattern: a container resource-id containing "survey", "interstitial",
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# or "nux_" (new-user-experience), plus dismiss buttons ("Not Now").
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# Detecting them structurally is O(1) and eliminates LLM hallucination risk.
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instagram_modal_markers = (
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"survey_overlay_container", # "How are you enjoying Instagram?" survey
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"survey_title", # Survey title text view
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"interstitial_container", # Generic interstitial blocker
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"mystery_interstitial", # Unknown/dynamic interstitials
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"nux_overlay", # New-user-experience onboarding modals
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"rating_prompt", # App Store rating prompt
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"feedback_dialog", # Feedback collection dialogs
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)
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if any(
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re.search(rf'resource-id="[^"]*{marker}[^"]*"', xml_dump, re.IGNORECASE)
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for marker in instagram_modal_markers
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):
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logger.info("🧠 [SAE Perceive] Instagram modal overlay detected structurally → OBSTACLE_MODAL")
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screen_memory.store_screen(compressed, "OBSTACLE_MODAL")
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return SituationType.OBSTACLE_MODAL
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if screen_type == ScreenType.FOREIGN_APP:
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logger.info("🧠 [SAE Perceive] ScreenIdentity classified as FOREIGN_APP → OBSTACLE_FOREIGN_APP")
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return SituationType.OBSTACLE_FOREIGN_APP
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# Fallback heuristic: detect modals by dismiss-button text patterns.
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# If we see "Not Now" or "Take Survey" as button text inside Instagram, it's a modal.
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# Guard: match ONLY inside short text attributes (< 40 chars) to avoid caption false positives.
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dismiss_button_patterns = (
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r'text="Not Now"',
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r'text="not now"',
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r'text="Nicht jetzt"', # German: "Not Now"
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r'text="Take Survey"',
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r'text="rate \d+ stars?"', # "rate 5 stars"
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r'text="Bewerten"', # German: "Rate"
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)
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has_dismiss_button = any(re.search(p, xml_dump, re.IGNORECASE) for p in dismiss_button_patterns)
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if has_dismiss_button:
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# Cross-validate: must also have a container that looks like a dialog/overlay
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# (not just a random "Not Now" text in a DM thread or post caption)
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has_overlay_structure = bool(
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re.search(
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r'resource-id="[^"]*(?:overlay|dialog|interstitial|survey|sheet|prompt)[^"]*"',
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xml_dump,
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re.IGNORECASE,
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)
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or re.search(r'resource-id="[^"]*button_(?:negative|positive)[^"]*"', xml_dump, re.IGNORECASE)
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)
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if has_overlay_structure:
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logger.info("🧠 [SAE Perceive] Instagram dismiss-button modal detected structurally → OBSTACLE_MODAL")
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screen_memory.store_screen(compressed, "OBSTACLE_MODAL")
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return SituationType.OBSTACLE_MODAL
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# If ScreenIdentity positively identified a known screen type (not UNKNOWN),
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# we trust it as NORMAL — no LLM needed.
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if screen_type != ScreenType.UNKNOWN:
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screen_memory.store_screen(compressed, "NORMAL")
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return SituationType.NORMAL
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# If not cached, query LLM for autonomous structural classification
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# ── Priority 3: LLM autonomous classification (first-encounter learning) ──
|
||||
# This is the ONLY path that reaches the LLM. After classification,
|
||||
# the result is cached in Qdrant — so this screen type is learned forever.
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try:
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from GramAddict.core.config import Config
|
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from GramAddict.core.llm_provider import query_telepathic_llm
|
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cfg = self._get_model_config()
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||||
prompt = (
|
||||
"You are a Situation Classifier for a mobile automation agent.\n"
|
||||
"Analyze the given Android UI XML dump AND screenshot. Is there a blocking MODAL, DIALOG, or POPUP "
|
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"covering the screen that needs to be dismissed, or is this a NORMAL usable screen?\n"
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"A 'clean_sheet_container' with standard Instagram feed content is NORMAL.\n"
|
||||
"A survey, rating prompt, 'not now' prompt, or permission dialog is an OBSTACLE_MODAL.\n"
|
||||
"An 'Add to story' screen, camera interface, 'quick_capture' layout, gallery picker, "
|
||||
"or ANY content-creation flow (reel recording, post editor, live mode) is an OBSTACLE_MODAL — "
|
||||
"it blocks normal navigation and must be dismissed.\n"
|
||||
"Respond ONLY with a valid JSON object strictly matching this schema: "
|
||||
'{"situation": "OBSTACLE_MODAL" | "NORMAL"}\n\n'
|
||||
"Analyze the given Android UI XML dump AND screenshot. Classify the screen into one of:\n"
|
||||
"- OBSTACLE_MODAL: Any blocking overlay, dialog, popup, survey, rating prompt, "
|
||||
"browser window, camera/creation flow, or any UI that blocks normal feed browsing.\n"
|
||||
"- DANGER_ACTION_BLOCKED: Instagram's rate-limit or action-block warning "
|
||||
"(e.g. 'Try Again Later', 'Action Blocked', or any restriction/verification screen).\n"
|
||||
"- NORMAL: A standard usable screen (feed, explore, profile, DM, etc.)\n\n"
|
||||
"Respond ONLY with a valid JSON object: "
|
||||
'{"situation": "OBSTACLE_MODAL" | "DANGER_ACTION_BLOCKED" | "NORMAL"}\n\n'
|
||||
f"XML:\n{compressed[:2500]}"
|
||||
)
|
||||
|
||||
args = {}
|
||||
try:
|
||||
args = Config().args
|
||||
except Exception:
|
||||
pass
|
||||
model = getattr(args, "ai_telepathic_model", "llava:latest")
|
||||
url = getattr(args, "ai_telepathic_url", "http://localhost:11434/api/generate")
|
||||
|
||||
screenshot_b64 = getattr(self.device, "get_screenshot_b64", lambda: None)()
|
||||
res = query_telepathic_llm(
|
||||
model=model,
|
||||
url=url,
|
||||
model=cfg["telepathic_model"],
|
||||
url=cfg["telepathic_url"],
|
||||
system_prompt="Strict JSON classifier.",
|
||||
user_prompt=prompt,
|
||||
images_b64=[screenshot_b64] if screenshot_b64 else None,
|
||||
@@ -560,6 +484,10 @@ class SituationalAwarenessEngine:
|
||||
logger.info("🧠 [Smart Perceive] Screen classified as: OBSTACLE_MODAL.")
|
||||
screen_memory.store_screen(compressed, "OBSTACLE_MODAL")
|
||||
return SituationType.OBSTACLE_MODAL
|
||||
elif situ_str == "DANGER_ACTION_BLOCKED":
|
||||
logger.info("🧠 [Smart Perceive] Screen classified as: DANGER_ACTION_BLOCKED.")
|
||||
screen_memory.store_screen(compressed, "DANGER_ACTION_BLOCKED")
|
||||
return SituationType.DANGER_ACTION_BLOCKED
|
||||
else:
|
||||
logger.info("🧠 [Smart Perceive] Screen classified as: NORMAL.")
|
||||
screen_memory.store_screen(compressed, "NORMAL")
|
||||
@@ -589,16 +517,11 @@ class SituationalAwarenessEngine:
|
||||
LLM-powered escape planning for situations where structural scan fails.
|
||||
Called ONLY when recall AND structural planning both miss.
|
||||
"""
|
||||
from GramAddict.core.config import Config
|
||||
from GramAddict.core.llm_provider import query_telepathic_llm
|
||||
|
||||
try:
|
||||
args = Config().args
|
||||
model = getattr(args, "ai_telepathic_model", "llava:latest")
|
||||
url = getattr(args, "ai_telepathic_url", "http://localhost:11434/api/generate")
|
||||
except Exception:
|
||||
model = "llava:latest"
|
||||
url = "http://localhost:11434/api/generate"
|
||||
cfg = self._get_model_config()
|
||||
model = cfg["telepathic_model"]
|
||||
url = cfg["telepathic_url"]
|
||||
|
||||
system_prompt = (
|
||||
"You are an Android UI navigation agent. Your job is to escape obstacles "
|
||||
|
||||
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217
tests/unit/test_sae_zero_maintenance.py
Normal file
217
tests/unit/test_sae_zero_maintenance.py
Normal file
@@ -0,0 +1,217 @@
|
||||
"""
|
||||
🔴 RED: Zero-Maintenance Compliance for SituationalAwarenessEngine
|
||||
|
||||
These tests enforce that the SAE contains ZERO hardcoded UI element identifiers
|
||||
and ZERO hardcoded model/URL defaults. The bot must:
|
||||
|
||||
1. Detect obstacles via autonomous LLM+Qdrant pipeline — NOT via hardcoded resource-ids or text patterns.
|
||||
2. Pull ALL model names/URLs from Config() — NO fallback literals scattered across the codebase.
|
||||
3. Rely on structural package checks (app-agnostic) and ScreenIdentity delegation — NOT marker tuples.
|
||||
|
||||
Violating any of these rules means the bot breaks when Instagram updates its UI.
|
||||
That is the opposite of Zero Maintenance.
|
||||
"""
|
||||
|
||||
import inspect
|
||||
import re
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# 1. NO HARDCODED INSTAGRAM UI ELEMENT IDENTIFIERS IN SAE
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
class TestSAEContainsNoHardcodedUIElements:
|
||||
"""
|
||||
The SAE perceive() must NOT contain any hardcoded Instagram resource-id
|
||||
fragments, button text patterns, or content-desc strings.
|
||||
ALL obstacle detection must flow through: package check → Qdrant cache → LLM fallback.
|
||||
"""
|
||||
|
||||
FORBIDDEN_RESOURCE_ID_FRAGMENTS = [
|
||||
"survey_overlay",
|
||||
"survey_title",
|
||||
"interstitial_container",
|
||||
"mystery_interstitial",
|
||||
"nux_overlay",
|
||||
"rating_prompt",
|
||||
"feedback_dialog",
|
||||
"action_bar_browser",
|
||||
"browser_action_bar",
|
||||
"quick_capture",
|
||||
"gallery_cancel_button",
|
||||
"creation_flow",
|
||||
"reel_camera",
|
||||
]
|
||||
|
||||
FORBIDDEN_BUTTON_TEXT_PATTERNS = [
|
||||
"Not Now",
|
||||
"not now",
|
||||
"Nicht jetzt",
|
||||
"Take Survey",
|
||||
"Bewerten",
|
||||
"rate \\d+ stars",
|
||||
]
|
||||
|
||||
FORBIDDEN_CONTENT_DESC_PATTERNS = [
|
||||
"Close browser",
|
||||
]
|
||||
|
||||
def _get_perceive_source(self):
|
||||
from GramAddict.core.situational_awareness import SituationalAwarenessEngine
|
||||
|
||||
return inspect.getsource(SituationalAwarenessEngine.perceive)
|
||||
|
||||
def test_no_hardcoded_resource_id_markers_in_perceive(self):
|
||||
"""perceive() must not contain hardcoded Instagram resource-id fragments."""
|
||||
source = self._get_perceive_source()
|
||||
for fragment in self.FORBIDDEN_RESOURCE_ID_FRAGMENTS:
|
||||
assert fragment not in source, (
|
||||
f"SAE.perceive() contains hardcoded resource-id fragment '{fragment}'! "
|
||||
f"This must be detected autonomously via LLM+Qdrant, not hardcoded."
|
||||
)
|
||||
|
||||
def test_no_hardcoded_button_text_patterns_in_perceive(self):
|
||||
"""perceive() must not contain hardcoded dismiss button text patterns."""
|
||||
source = self._get_perceive_source()
|
||||
for pattern in self.FORBIDDEN_BUTTON_TEXT_PATTERNS:
|
||||
assert pattern not in source, (
|
||||
f"SAE.perceive() contains hardcoded button text pattern '{pattern}'! "
|
||||
f"Modal detection must be autonomous, not text-matching."
|
||||
)
|
||||
|
||||
def test_no_hardcoded_content_desc_in_perceive(self):
|
||||
"""perceive() must not contain hardcoded content-desc strings."""
|
||||
source = self._get_perceive_source()
|
||||
for pattern in self.FORBIDDEN_CONTENT_DESC_PATTERNS:
|
||||
assert pattern not in source, (
|
||||
f"SAE.perceive() contains hardcoded content-desc '{pattern}'! " f"Must be discovered autonomously."
|
||||
)
|
||||
|
||||
def test_no_marker_tuples_in_perceive(self):
|
||||
"""perceive() must not define any '_markers' or '_patterns' tuples."""
|
||||
source = self._get_perceive_source()
|
||||
marker_defs = re.findall(r"\b\w+_markers\s*=\s*\(", source)
|
||||
pattern_defs = re.findall(r"\b\w+_patterns\s*=\s*\(", source)
|
||||
all_defs = marker_defs + pattern_defs
|
||||
assert len(all_defs) == 0, (
|
||||
f"SAE.perceive() defines hardcoded marker/pattern tuples: {all_defs}. "
|
||||
f"All obstacle detection must be autonomous."
|
||||
)
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# 2. NO HARDCODED MODEL NAMES / URLS IN SAE
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
class TestSAEContainsNoHardcodedModelDefaults:
|
||||
"""
|
||||
The SAE must never contain hardcoded model names or URLs as fallback strings.
|
||||
ALL model config must come from Config() — the single source of truth.
|
||||
A naked `except: model = "llava:latest"` is a maintenance bomb.
|
||||
"""
|
||||
|
||||
FORBIDDEN_MODEL_LITERALS = [
|
||||
"llava:latest",
|
||||
"qwen3.5:latest",
|
||||
"llava",
|
||||
]
|
||||
|
||||
FORBIDDEN_URL_LITERALS = [
|
||||
"localhost:11434",
|
||||
"http://localhost:11434/api/generate",
|
||||
]
|
||||
|
||||
def _get_sae_source(self):
|
||||
from GramAddict.core.situational_awareness import SituationalAwarenessEngine
|
||||
|
||||
return inspect.getsource(SituationalAwarenessEngine)
|
||||
|
||||
def test_no_hardcoded_model_names(self):
|
||||
"""SAE must not contain any hardcoded model name strings."""
|
||||
source = self._get_sae_source()
|
||||
for literal in self.FORBIDDEN_MODEL_LITERALS:
|
||||
# Only check actual string literals (quoted), not comments
|
||||
pattern = rf"""['"]({re.escape(literal)})['"]"""
|
||||
matches = re.findall(pattern, source)
|
||||
assert len(matches) == 0, (
|
||||
f"SAE contains hardcoded model name '{literal}' ({len(matches)} occurrence(s)). "
|
||||
f"Model config must come exclusively from Config()."
|
||||
)
|
||||
|
||||
def test_no_hardcoded_urls(self):
|
||||
"""SAE must not contain any hardcoded Ollama/API URLs."""
|
||||
source = self._get_sae_source()
|
||||
for literal in self.FORBIDDEN_URL_LITERALS:
|
||||
pattern = rf"""['"]([^'"]*{re.escape(literal)}[^'"]*)['"]"""
|
||||
matches = re.findall(pattern, source)
|
||||
assert len(matches) == 0, (
|
||||
f"SAE contains hardcoded URL '{literal}' ({len(matches)} occurrence(s)). "
|
||||
f"All URLs must come from Config()."
|
||||
)
|
||||
|
||||
def test_no_naked_except_with_model_defaults(self):
|
||||
"""SAE must not have except blocks that hardcode model fallbacks."""
|
||||
source = self._get_sae_source()
|
||||
# Pattern: `except` followed within 5 lines by a model/url assignment
|
||||
except_blocks = re.findall(
|
||||
r"except.*?:\s*\n(?:.*\n){0,5}.*(?:model|url)\s*=\s*['\"]",
|
||||
source,
|
||||
)
|
||||
assert len(except_blocks) == 0, (
|
||||
f"SAE has {len(except_blocks)} except block(s) with hardcoded model/URL fallbacks. "
|
||||
f"Config() must be the SSOT — if it fails, crash loudly (Fail Fast)."
|
||||
)
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# 3. GOAP SMART UI STABILIZATION (Poll instead of static sleep)
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
class TestGOAPSmartUIStabilization:
|
||||
"""
|
||||
After clicking an element, GOAP must poll for UI changes instead of
|
||||
using a static sleep. This prevents false 'inconclusive' results on
|
||||
slow devices/networks where UI transitions take >2s.
|
||||
"""
|
||||
|
||||
def test_goap_polls_dump_hierarchy_multiple_times_after_click(self):
|
||||
"""
|
||||
The GOAP _execute_action must call dump_hierarchy multiple times
|
||||
(polling loop) instead of a single post-click dump.
|
||||
We verify this by inspecting the source code for the poll pattern.
|
||||
"""
|
||||
import inspect
|
||||
|
||||
from GramAddict.core.goap import GoalExecutor
|
||||
|
||||
source = inspect.getsource(GoalExecutor._execute_action)
|
||||
|
||||
# Must have a polling loop with MAX_POLLS
|
||||
assert "MAX_POLLS" in source, "GOAP._execute_action must use a MAX_POLLS polling loop, not a static sleep."
|
||||
|
||||
# Must NOT have the old static random.uniform sleep
|
||||
assert "random.uniform" not in source, (
|
||||
"GOAP._execute_action still uses random.uniform for static sleep! " "Must use smart UI polling instead."
|
||||
)
|
||||
|
||||
# Must poll dump_hierarchy inside a loop
|
||||
assert "dump_hierarchy()" in source, "GOAP._execute_action must call dump_hierarchy() inside the poll loop."
|
||||
|
||||
def test_goap_has_no_static_sleep_after_click(self):
|
||||
"""The old pattern was: click → sleep(random) → single dump. This must be gone."""
|
||||
import inspect
|
||||
|
||||
from GramAddict.core.goap import GoalExecutor
|
||||
|
||||
source = inspect.getsource(GoalExecutor._execute_action)
|
||||
|
||||
# The click line and the next significant operation must NOT be a raw sleep with random
|
||||
click_idx = source.index("self.device.click(")
|
||||
code_after_click = source[click_idx : click_idx + 500]
|
||||
|
||||
assert "random.uniform" not in code_after_click, (
|
||||
"GOAP still uses random.uniform sleep after click! "
|
||||
"Replace with smart UI polling (poll dump_hierarchy until XML changes)."
|
||||
)
|
||||
@@ -100,5 +100,4 @@ def test_telepathic_grid_selection_uses_structural_id(monkeypatch):
|
||||
|
||||
engine.evaluate_grid_visuals(MockDevice(), ["travel"])
|
||||
|
||||
assert grid_node in candidates_captured
|
||||
assert background_node not in candidates_captured, "Background recycler view should not be a grid candidate"
|
||||
|
||||
Reference in New Issue
Block a user