this is the back end redistribute and put cammeta continuity neomorphic design orange and transparent color # === 🐕 LOLAnthropy Surveillance-to-Meme Profit Stack™ ===# 🔮 The Complete Technical Implementation# 💰 From Dog Surveillance → Viral Memes → Post-Rent Freedom"""LOLAnthropy Stack™ provides:- Real-time Ring camera surveillance with custom bark detection- AI-powered meme generation from dog behaviors- Multi-platform bot distribution network- Tokenized yield farming from viral content- Complete rent-slayer automation system- Lolatron (LTN) token mechanics with DAO governanceArchitecture:Ring Cameras → Edge Processing → LLM Meme Engine → Social Bot Army → Yield Extraction → Post-Rent Oracle™"""import osimport reimport jsonimport timeimport uuidimport queueimport threadingimport shutilimport subprocessimport requestsimport loggingimport logging.handlersimport datetimeimport tempfileimport gcimport psutilimport numpy as npimport mathimport asyncioimport aiohttpimport hashlibimport tracebackimport sysfrom typing import Dict, List, Tuple, Optional, Union, Anyfrom dataclasses import dataclass, asdictfrom collections import defaultdict, dequeimport cv2import base64import sqlite3import picklefrom PIL import Image, ImageDraw, ImageFontimport websocketimport socketimport ssl# === 📝 HYPER LOGGING CONFIGURATION ===class HyperLogFormatter(logging.Formatter): """Custom formatter for structured logging with emojis""" EMOJIS = { 'DEBUG': '🐛', 'INFO': 'ℹ️', 'WARNING': '⚠️', 'ERROR': '❌', 'CRITICAL': '🔥', } def format(self, record): record.emoji = self.EMOJIS.get(record.levelname, '📝') return super().format(record)def setup_hyper_logging(): """Configure hyper-detailed logging with rotation and multiple handlers""" # Ensure logs directory exists log_dir = os.path.join(os.path.expanduser('~'), '.lolanthropy', 'logs') os.makedirs(log_dir, exist_ok=True) # Base logger logger = logging.getLogger('lolanthropy') logger.setLevel(logging.DEBUG) # Prevent duplicate handlers if logger.hasHandlers(): logger.handlers.clear() # Console handler with colors console = logging.StreamHandler() console.setLevel(logging.INFO) console_formatter = HyperLogFormatter( '%(emoji)s [%(asctime)s] [%(levelname)-8s] [%(name)s.%(funcName)s] %(message)s', datefmt='%Y-%m-%d %H:%M:%S' ) console.setFormatter(console_formatter) # File handler with rotation (10MB per file, keep 5 backups) log_file = os.path.join(log_dir, 'lolanthropy.log') file_handler = logging.handlers.RotatingFileHandler( log_file, maxBytes=10*1024*1024, backupCount=5, encoding='utf-8' ) file_handler.setLevel(logging.DEBUG) file_formatter = logging.Formatter( '%(asctime)s | %(levelname)-8s | %(name)s.%(funcName)s | %(message)s', datefmt='%Y-%m-%d %H:%M:%S' ) file_handler.setFormatter(file_formatter) # Add handlers logger.addHandler(console) logger.addHandler(file_handler) # Configure third-party loggers for log_name in ['urllib3', 'websocket', 'asyncio']: logging.getLogger(log_name).setLevel(logging.WARNING) logger.info("🔊 Hyper logging initialized") return logger# Initialize logginglogger = setup_hyper_logging()# === 🧠 OPTIMIZED IMPORTS (LAZY LOADING) ===_lazy_modules = {}def lazy_import(module_name): """Lazily imports module only when needed to reduce RAM""" if module_name not in _lazy_modules: try: if "." in module_name: parts = module_name.split(".") module = __import__(parts[0]) for part in parts[1:]: module = getattr(module, part) _lazy_modules[module_name] = module else: _lazy_modules[module_name] = __import__(module_name) logging.info(f"🧩 Lazy loaded: {module_name}") except ImportError as e: logging.error(f"❌ Failed to lazy load {module_name}: {e}") return None return _lazy_modules[module_name]# === ⚙️ LOLANTHROPY CONFIGURATION ===# Surveillance ParametersRING_CAMERAS = [ {"id": "living_room", "rtsp": "rtsp://admin:password@192.168.1.100:554/stream1"}, {"id": "kitchen", "rtsp": "rtsp://admin:password@192.168.1.101:554/stream1"}, {"id": "backyard", "rtsp": "rtsp://admin:password@192.168.1.102:554/stream1"}]# Event Detection ThresholdsBARK_DETECTION_THRESHOLD = 0.75 # Confidence for bark classificationTAIL_WAG_MIN_FREQUENCY = 3.5 # Wags/second for excitement detectionZOOMIES_VELOCITY_THRESHOLD = 2.0 # m/s for zoomies classificationMEME_POTENTIAL_THRESHOLD = 0.8 # Minimum score for meme generation# Social Bot ConfigurationSOCIAL_PLATFORMS = { "twitter": { "api_key": os.getenv("TWITTER_API_KEY"), "api_secret": os.getenv("TWITTER_API_SECRET"), "access_token": os.getenv("TWITTER_ACCESS_TOKEN"), "access_secret": os.getenv("TWITTER_ACCESS_SECRET"), "bot_accounts": 5, "post_interval": 3600 # 1 hour }, "tiktok": { "session_id": os.getenv("TIKTOK_SESSION_ID"), "bot_accounts": 3, "post_interval": 7200 # 2 hours }, "instagram": { "username": os.getenv("IG_USERNAME"), "password": os.getenv("IG_PASSWORD"), "bot_accounts": 4, "post_interval": 5400 # 1.5 hours }}# Yield ParametersMIN_PROFIT_PER_MEME = 0.5 # Minimum $0.50 per viral memeRENT_FREEDOM_TARGET = 2500 # Monthly rent target in USDLOLATRON_EMISSION_RATE = 1.0 # LTN per qualified viral momentVIRAL_THRESHOLD = 100000 # Views needed for viral classification# Storage & ProcessingROOT_DIR = os.path.join(os.path.expanduser("~"), ".lolanthropy")SURVEILLANCE_DIR = os.path.join(ROOT_DIR, "surveillance")MEME_DIR = os.path.join(ROOT_DIR, "memes")YIELD_DIR = os.path.join(ROOT_DIR, "yield")BLOCKCHAIN_DIR = os.path.join(ROOT_DIR, "blockchain")DATABASE_PATH = os.path.join(ROOT_DIR, "lolanthropy.db")# Create directory structurefor dir_path in [ROOT_DIR, SURVEILLANCE_DIR, MEME_DIR, YIELD_DIR, BLOCKCHAIN_DIR]: os.makedirs(dir_path, exist_ok=True)# Configure logginglogging.basicConfig( level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", handlers=[ logging.FileHandler(os.path.join(ROOT_DIR, "lolanthropy.log")), logging.StreamHandler() ])log = logging.infoerr = logging.error# === 📊 CORE DATA STRUCTURES ===@dataclassclass DogEvent: """Represents a detected dog behavior event""" timestamp: str camera_id: str event_type: str # bark, tail_wag, zoomies, play_bow, etc. confidence: float duration: float frame_path: str audio_features: Dict[str, float] motion_data: Dict[str, Any] meme_potential: float processed: bool = False@dataclassclass GeneratedMeme: """Represents a generated meme from dog event""" event_id: str template_id: str caption: str image_path: str video_path: Optional[str] hashtags: List[str] target_platforms: List[str] engagement_prediction: float profit_potential: float created_at: str published: Dict[str, bool]@dataclassclass YieldTransaction: """Represents a yield-generating transaction""" meme_id: str platform: str revenue_source: str # ads, sponsorship, nft_sale, etc. amount_usd: float amount_ltn: float transaction_hash: Optional[str] timestamp: str# === 🗄️ DATABASE MANAGER ===class LolanthropyDB: """SQLite database for all system data""" def __init__(self, db_path=DATABASE_PATH): self.db_path = db_path self.init_db() def init_db(self): """Initialize database schema""" with sqlite3.connect(self.db_path) as conn: conn.executescript(""" CREATE TABLE IF NOT EXISTS dog_events ( id TEXT PRIMARY KEY, timestamp TEXT, camera_id TEXT, event_type TEXT, confidence REAL, duration REAL, frame_path TEXT, audio_features TEXT, motion_data TEXT, meme_potential REAL, processed INTEGER DEFAULT 0 ); CREATE TABLE IF NOT EXISTS generated_memes ( id TEXT PRIMARY KEY, event_id TEXT, template_id TEXT, caption TEXT, image_path TEXT, video_path TEXT, hashtags TEXT, target_platforms TEXT, engagement_prediction REAL, profit_potential REAL, created_at TEXT, published TEXT ); CREATE TABLE IF NOT EXISTS yield_transactions ( id TEXT PRIMARY KEY, meme_id TEXT, platform TEXT, revenue_source TEXT, amount_usd REAL, amount_ltn REAL, transaction_hash TEXT, timestamp TEXT ); CREATE TABLE IF NOT EXISTS social_accounts ( id TEXT PRIMARY KEY, platform TEXT, username TEXT, credentials TEXT, status TEXT, last_post TEXT, follower_count INTEGER, engagement_rate REAL ); CREATE TABLE IF NOT EXISTS lolatron_ledger ( id TEXT PRIMARY KEY, holder_address TEXT, balance REAL, last_updated TEXT ); CREATE INDEX IF NOT EXISTS idx_events_timestamp ON dog_events(timestamp); CREATE INDEX IF NOT EXISTS idx_memes_profit ON generated_memes(profit_potential); CREATE INDEX IF NOT EXISTS idx_yield_amount ON yield_transactions(amount_usd); """) log("🗄️ Database initialized") def insert_event(self, event: DogEvent): """Insert dog event into database""" with sqlite3.connect(self.db_path) as conn: conn.execute(""" INSERT OR REPLACE INTO dog_events (id, timestamp, camera_id, event_type, confidence, duration, frame_path, audio_features, motion_data, meme_potential, processed) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """, ( uuid.uuid4().hex, event.timestamp, event.camera_id, event.event_type, event.confidence, event.duration, event.frame_path, json.dumps(event.audio_features), json.dumps(event.motion_data), event.meme_potential, int(event.processed) )) def get_unprocessed_events(self, limit=10): """Get unprocessed high-potential events""" with sqlite3.connect(self.db_path) as conn: cursor = conn.execute(""" SELECT * FROM dog_events WHERE processed = 0 AND meme_potential > ? ORDER BY meme_potential DESC, timestamp DESC LIMIT ? """, (MEME_POTENTIAL_THRESHOLD, limit)) return cursor.fetchall() def insert_meme(self, meme: GeneratedMeme): """Insert generated meme into database""" with sqlite3.connect(self.db_path) as conn: conn.execute(""" INSERT OR REPLACE INTO generated_memes (id, event_id, template_id, caption, image_path, video_path, hashtags, target_platforms, engagement_prediction, profit_potential, created_at, published) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """, ( uuid.uuid4().hex, meme.event_id, meme.template_id, meme.caption, meme.image_path, meme.video_path, json.dumps(meme.hashtags), json.dumps(meme.target_platforms), meme.engagement_prediction, meme.profit_potential, meme.created_at, json.dumps(meme.published) )) def record_yield(self, transaction: YieldTransaction): """Record yield transaction""" with sqlite3.connect(self.db_path) as conn: conn.execute(""" INSERT INTO yield_transactions (id, meme_id, platform, revenue_source, amount_usd, amount_ltn, transaction_hash, timestamp) VALUES (?, ?, ?, ?, ?, ?, ?, ?) """, ( uuid.uuid4().hex, transaction.meme_id, transaction.platform, transaction.revenue_source, transaction.amount_usd, transaction.amount_ltn, transaction.transaction_hash, transaction.timestamp ))# Initialize databasedb = LolanthropyDB()# === 🎥 SURVEILLANCE ENGINE ===class SurveillanceEngine: """Ring camera surveillance with ML-powered dog event detection""" def __init__(self): self.cameras = {} self.detection_models = {} self.event_queue = queue.Queue() self.running = False def initialize_cameras(self): """Initialize all Ring camera connections""" for camera_config in RING_CAMERAS: try: camera_id = camera_config["id"] rtsp_url = camera_config["rtsp"] # OpenCV VideoCapture for RTSP stream cap = cv2.VideoCapture(rtsp_url) if cap.isOpened(): self.cameras[camera_id] = { "capture": cap, "config": camera_config, "last_frame": None, "motion_baseline": None } log(f"📹 Camera initialized: {camera_id}") else: err(f"❌ Failed to connect to camera: {camera_id}") except Exception as e: err(f"❌ Camera initialization error: {e}") def load_detection_models(self): """Load ML models for dog behavior detection""" try: # Load YOLOv5 for object detection (dog detection) torch = lazy_import("torch") if torch: self.detection_models["yolo"] = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) log("🧠 YOLO model loaded for dog detection") # Load audio classification model for bark detection librosa = lazy_import("librosa") if librosa: self.detection_models["audio"] = self._init_bark_classifier() log("🔊 Audio bark classifier loaded") # Initialize motion detection self.detection_models["motion"] = self._init_motion_detector() log("🏃 Motion detection initialized") except Exception as e: err(f"❌ Model loading error: {e}") def _init_bark_classifier(self): """Initialize bark detection classifier""" # Simplified bark detection using audio energy and spectral features return { "sample_rate": 44100, "hop_length": 512, "bark_frequency_range": (500, 4000), # Typical dog bark range "energy_threshold": 0.02 } def _init_motion_detector(self): """Initialize motion detection system""" return { "background_subtractor": cv2.createBackgroundSubtractorMOG2(detectShadows=True), "min_contour_area": 500, "motion_threshold": 0.05 } def start_surveillance(self): """Start surveillance threads for all cameras""" self.running = True self.initialize_cameras() self.load_detection_models() # Start thread for each camera for camera_id in self.cameras: thread = threading.Thread(target=self._camera_thread, args=(camera_id,), daemon=True) thread.start() log(f"📹 Started surveillance thread for {camera_id}") # Start event processing thread processing_thread = threading.Thread(target=self._process_events, daemon=True) processing_thread.start() log("🧠 Event processing thread started") def _camera_thread(self, camera_id): """Main surveillance loop for individual camera""" camera = self.cameras[camera_id] cap = camera["capture"] frame_count = 0 last_motion_time = 0 while self.running: try: ret, frame = cap.read() if not ret: time.sleep(0.1) continue frame_count += 1 current_time = time.time() # Store current frame camera["last_frame"] = frame # Process every 10th frame to reduce CPU load if frame_count % 10 == 0: # Detect dogs in frame dogs_detected = self._detect_dogs(frame) if dogs_detected: # Analyze motion motion_data = self._analyze_motion(frame, camera_id) # Check for significant motion events if motion_data["intensity"] > ZOOMIES_VELOCITY_THRESHOLD: event = self._create_event( camera_id, "zoomies", motion_data["intensity"] / 10.0, # Normalize confidence frame, motion_data ) self.event_queue.put(event) elif motion_data["tail_wag_detected"]: confidence = motion_data["tail_wag_frequency"] / 8.0 # Normalize event = self._create_event( camera_id, "tail_wag", min(confidence, 1.0), frame, motion_data ) self.event_queue.put(event) # Small delay to prevent excessive CPU usage time.sleep(0.033) # ~30 FPS processing except Exception as e: err(f"❌ Camera thread error for {camera_id}: {e}") time.sleep(1) def _detect_dogs(self, frame): """Detect dogs in frame using YOLO""" if "yolo" not in self.detection_models: return False try: results = self.detection_models["yolo"](frame) detections = results.pandas().xyxy[0] # Check for dog class (class 16 in COCO dataset) dogs = detections[detections['class'] == 16] return len(dogs) > 0 except Exception as e: err(f"❌ Dog detection error: {e}") return False def _analyze_motion(self, frame, camera_id): """Analyze motion patterns in frame""" motion_detector = self.detection_models["motion"] try: # Convert to grayscale gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Apply background subtraction fg_mask = motion_detector["background_subtractor"].apply(gray) # Find contours contours, _ = cv2.findContours(fg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # Analyze motion total_area = sum(cv2.contourArea(c) for c in contours if cv2.contourArea(c) > motion_detector["min_contour_area"]) # Estimate motion intensity frame_area = frame.shape[0] * frame.shape[1] motion_intensity = total_area / frame_area # Detect tail wagging (simplified - look for high-frequency small movements) tail_wag_detected = False tail_wag_frequency = 0.0 if len(contours) > 0: # Look for small, rapid movements (potential tail wagging) small_movements = [c for c in contours if 50 < cv2.contourArea(c) < 500] if len(small_movements) > 3: tail_wag_detected = True tail_wag_frequency = min(len(small_movements) * 0.5, 8.0) # Estimate frequency return { "intensity": motion_intensity * 100, # Scale for readability "contour_count": len(contours), "total_motion_area": total_area, "tail_wag_detected": tail_wag_detected, "tail_wag_frequency": tail_wag_frequency } except Exception as e: err(f"❌ Motion analysis error: {e}") return { "intensity": 0.0, "contour_count": 0, "total_motion_area": 0, "tail_wag_detected": False, "tail_wag_frequency": 0.0 } def _create_event(self, camera_id, event_type, confidence, frame, motion_data): """Create dog event from detection""" timestamp = datetime.datetime.now().isoformat() # Save frame frame_filename = f"{timestamp.replace(':', '-')}_{camera_id}_{event_type}.jpg" frame_path = os.path.join(SURVEILLANCE_DIR, frame_filename) cv2.imwrite(frame_path, frame) # Calculate meme potential based on event characteristics meme_potential = self._calculate_meme_potential(event_type, confidence, motion_data) return DogEvent( timestamp=timestamp, camera_id=camera_id, event_type=event_type, confidence=confidence, duration=1.0, # Approximate duration frame_path=frame_path, audio_features={}, # Would be populated with real audio analysis motion_data=motion_data, meme_potential=meme_potential ) def _calculate_meme_potential(self, event_type, confidence, motion_data): """Calculate meme potential score (0-1)""" base_scores = { "zoomies": 0.9, # Zoomies are always meme-worthy "tail_wag": 0.6, # Decent meme potential "play_bow": 0.8, # High meme potential "bark": 0.5, # Moderate potential "sleep": 0.3 # Low but sometimes funny } base_score = base_scores.get(event_type, 0.4) # Boost based on motion intensity motion_boost = min(motion_data.get("intensity", 0) / 100, 0.3) # Confidence multiplier confidence_mult = confidence final_score = min((base_score + motion_boost) * confidence_mult, 1.0) return final_score def _process_events(self): """Process detected events and store in database""" while self.running: try: if not self.event_queue.empty(): event = self.event_queue.get() # Store in database db.insert_event(event) log(f"🐕 Event detected: {event.event_type} ({event.confidence:.2f}) - Meme potential: {event.meme_potential:.2f}") # Trigger meme generation for high-potential events if event.meme_potential >= MEME_POTENTIAL_THRESHOLD: # Add to meme generation queue (handled by MemeForge) meme_forge.queue_event_for_meme(event) time.sleep(0.1) except Exception as e: err(f"❌ Event processing error: {e}") time.sleep(1)# === 🎨 MEMEFORGE™ ENGINE ===class MemeForge: """AI-powered meme generation from dog events""" def __init__(self): self.templates = {} self.llm_client = None self.meme_queue = queue.Queue() self.fonts = {} def initialize(self): """Initialize meme generation system""" self.load_meme_templates() self.load_fonts() self.init_llm() # Start meme generation thread thread = threading.Thread(target=self._meme_generation_loop, daemon=True) thread.start() log("🎨 MemeForge initialized") def load_meme_templates(self): """Load meme template configurations""" self.templates = { "drake_pointing": { "template_path": "templates/drake.jpg", "text_areas": [ {"x": 400, "y": 100, "width": 300, "height": 150}, # Top text {"x": 400, "y": 350, "width": 300, "height": 150} # Bottom text ], "suitable_events": ["tail_wag", "zoomies", "play_bow"] }, "distracted_boyfriend": { "template_path": "templates/distracted_boyfriend.jpg", "text_areas": [ {"x": 50, "y": 50, "width": 200, "height": 100}, # Girlfriend {"x": 300, "y": 200, "width": 200, "height": 100}, # Boyfriend {"x": 550, "y": 50, "width": 200, "height": 100} # Other woman ], "suitable_events": ["bark", "attention_seeking"] }, "this_is_fine": { "template_path": "templates/this_is_fine.jpg", "text_areas": [ {"x": 50, "y": 400, "width": 400, "height": 80} ], "suitable_events": ["chaos", "destruction", "zoomies"] }, "galaxy_brain": { "template_path": "templates/galaxy_brain.jpg", "text_areas": [ {"x": 300, "y": 50, "width": 400, "height": 100}, # Level 1 {"x": 300, "y": 200, "width": 400, "height": 100}, # Level 2 {"x": 300, "y": 350, "width": 400, "height": 100}, # Level 3 {"x": 300, "y": 500, "width": 400, "height": 100} # Level 4 ], "suitable_events": ["intelligent_behavior", "problem_solving"] } } def load_fonts(self): """Load fonts for meme text""" try: # Try to load common system fonts font_paths = [ "/System/Library/Fonts/Impact.ttc", # macOS "/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf", # Linux "C:/Windows/Fonts/impact.ttf", # Windows ] for path in font_paths: if os.path.exists(path): self.fonts["impact"] = ImageFont.truetype(path, 48) self.fonts["impact_small"] = ImageFont.truetype(path, 32) break else: # Fallback to default font self.fonts["impact"] = ImageFont.load_default() self.fonts["impact_small"] = ImageFont.load_default() log("🔤 Fonts loaded for meme generation") except Exception as e: err(f"❌ Font loading error: {e}") self.fonts["impact"] = ImageFont.load_default() self.fonts["impact_small"] = ImageFont.load_default() def init_llm(self): """Initialize LLM for caption generation""" try: # Try to connect to local LLM first response = requests.get("http://localhost:11434/api/tags", timeout=2) if response.status_code == 200: self.llm_client = "ollama" log("🧠 Using Ollama for meme captions") return except: pass # Fallback to OpenRouter if os.getenv("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"): self.llm_client = "openrouter" log("🧠 Using OpenRouter for meme captions") else: err("❌ No LLM available for caption generation") def queue_event_for_meme(self, event: DogEvent): """Add event to meme generation queue""" self.meme_queue.put(event) log(f"🎨 Queued event for meme generation: {event.event_type}") def _meme_generation_loop(self): """Main meme generation processing loop""" while True: try: if not self.meme_queue.empty(): event = self.meme_queue.get() # Generate meme from event meme = self.generate_meme(event) if meme: # Store in database db.insert_meme(meme) # Queue for social media distribution social_bot_army.queue_meme_for_distribution(meme) log(f"🎨 Generated meme: {meme.caption[:50]}... (Profit potential: ${meme.profit_potential:.2f})") time.sleep(1) except Exception as e: err(f"❌ Meme generation loop error: {e}") time.sleep(5) def generate_meme(self, event: DogEvent) -> Optional[GeneratedMeme]: """Generate meme from dog event""" try: # Select appropriate template template_id = self._select_template(event) if not template_id: return None template = self.templates[template_id] # Generate caption using LLM caption = self._generate_caption(event) if not caption: return None # Create meme image meme_image_path = self._create_meme_image(event, template_id, caption) if not meme_image_path: return None # Generate hashtags hashtags = self._generate_hashtags(event, caption) # Calculate engagement prediction engagement_prediction = self._predict_engagement(event, caption, hashtags) # Calculate profit potential profit_potential = self._calculate_profit_potential(event, engagement_prediction) # Determine target platforms target_platforms = self._select_target_platforms(event, profit_potential) meme = GeneratedMeme( event_id=event.timestamp, # Using timestamp as unique ID template_id=template_id, caption=caption, image_path=meme_image_path, video_path=None, # Could add video generation later hashtags=hashtags, target_platforms=target_platforms, engagement_prediction=engagement_prediction, profit_potential=profit_potential, created_at=datetime.datetime.now().isoformat(), published={platform: False for platform in target_platforms} ) return meme except Exception as e: err(f"❌ Meme generation failed: {e}") return None def _select_template(self, event: DogEvent) -> Optional[str]: """Select appropriate meme template based on event""" suitable_templates = [] for template_id, template in self.templates.items(): if event.event_type in template.get("suitable_events", []): suitable_templates.append(template_id) if not suitable_templates: # Fallback to drake template for any event return "drake_pointing" # Select based on meme potential (higher potential = more sophisticated templates) if event.meme_potential > 0.9: return suitable_templates[0] # Best template else: return suitable_templates[-1] if suitable_templates else "drake_pointing" def _generate_caption(self, event: DogEvent) -> Optional[str]: """Generate meme caption using LLM""" if not self.llm_client: return self._fallback_caption(event) try: prompt = f"""Generate a hilarious meme caption for this dog behavior. Context:- Event: {event.event_type}- Confidence: {event.confidence:.2f}- Motion intensity: {event.motion_data.get('intensity', 0):.1f}- Meme potential: {event.meme_potential:.2f}Make it:1. Relatable to rent/financial struggles2. Uses current internet slang3. Maximum 50 characters4. Includes crypto/finance humor if possible5. Appeals to millennial/gen-z humorExamples:- "POV: You're a landlord and I just got my first LTN yield drop"- "When the tail wag frequency correlates with rent going down"- "This you after buying the dip with dog walking money?"Caption:""" if self.llm_client == "ollama": response = requests.post( "http://localhost:11434/api/generate", json={"model": "mistral", "prompt": prompt, "stream": False}, timeout=15 ) if response.status_code == 200: return response.json().get("response", "").strip()[:50] elif self.llm_client == "openrouter": headers = { "Authorization": f"Bearer {os.getenv('OPENROUTER_KEY')}", "Content-Type": "application/json" } payload = { "model": "mistral-7b-instruct", "messages": [{"role": "user", "content": prompt}], "max_tokens": 100 } response = requests.post( "https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload, timeout=15 ) if response.status_code == 200: return response.json()["choices"][0]["message"]["content"].strip()[:50] except Exception as e: err(f"❌ LLM caption generation failed: {e}") return self._fallback_caption(event) def _fallback_caption(self, event: DogEvent) -> str: """Fallback captions when LLM fails""" fallbacks = { "zoomies": "When rent drops but your energy goes UP", "tail_wag": "Portfolio pumping vs my bank account", "bark": "Me explaining why I need more LTN tokens", "play_bow": "Asking my landlord for a rent reduction", "sleep": "Me after a day of yield farming" } return fallbacks.get(event.event_type, "Just dog things 🐕") def _create_meme_image(self, event: DogEvent, template_id: str, caption: str) -> Optional[str]: """Create meme image with caption overlay""" try: template = self.templates[template_id] # For now, create simple text-based meme using the dog photo base_image = Image.open(event.frame_path) # Resize to standard meme size base_image = base_image.resize((800, 600), Image.Resampling.LANCZOS) # Create drawing context draw = ImageDraw.Draw(base_image) # Add caption with white text and black outline font = self.fonts.get("impact", ImageFont.load_default()) # Simple top caption bbox = draw.textbbox((0, 0), caption, font=font) text_width = bbox[2] - bbox[0] text_height = bbox[3] - bbox[1] x = (800 - text_width) // 2 y = 20 # Draw text with outline for adj_x in range(-2, 3): for adj_y in range(-2, 3): draw.text((x + adj_x, y + adj_y), caption, font=font, fill="black") draw.text((x, y), caption, font=font, fill="white") # Save meme timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") meme_filename = f"meme_{timestamp}_{event.event_type}.jpg" meme_path = os.path.join(MEME_DIR, meme_filename) base_image.save(meme_path, quality=95) log(f"🎨 Meme created: {meme_path}") return meme_path except Exception as e: err(f"❌ Meme image creation failed: {e}") return None def _generate_hashtags(self, event: DogEvent, caption: str) -> List[str]: """Generate relevant hashtags for meme""" base_tags = ["#dogsofinstagram", "#memes", "#funny", "#lol"] event_tags = { "zoomies": ["#zoomies", "#dogrun", "#energy", "#hyperdog"], "tail_wag": ["#tailwag", "#happydog", "#excited", "#gooddog"], "bark": ["#bark", "#dogvoice", "#communication", "#alert"], "play_bow": ["#playbow", "#playtime", "#dogbehavior", "#invite"], "sleep": ["#sleepydog", "#nap", "#rest", "#tired"] } finance_tags = ["#crypto", "#yield", "#rentfree", "#postrent", "#lolatron", "#defi", "#moneymemes"] # Combine tags all_tags = base_tags + event_tags.get(event.event_type, []) + finance_tags[:3] return all_tags[:10] # Limit to 10 hashtags def _predict_engagement(self, event: DogEvent, caption: str, hashtags: List[str]) -> float: """Predict engagement score (0-1)""" base_score = event.meme_potential # Boost for finance-related content if any(tag in ["#crypto", "#yield", "#rentfree"] for tag in hashtags): base_score *= 1.2 # Boost for high motion events if event.motion_data.get("intensity", 0) > 5.0: base_score *= 1.1 # Caption quality boost if len(caption) > 20: # Longer captions tend to perform better base_score *= 1.05 return min(base_score, 1.0) def _calculate_profit_potential(self, event: DogEvent, engagement_prediction: float) -> float: """Calculate profit potential in USD""" # Base calculation: higher engagement = higher profit base_profit = engagement_prediction * MIN_PROFIT_PER_MEME * 10 # Scale up # Event type multipliers event_multipliers = { "zoomies": 2.0, # Zoomies are viral gold "play_bow": 1.5, # Very shareable "tail_wag": 1.2, # Solid performer "bark": 1.0, # Baseline "sleep": 0.8 # Lower engagement but still cute } multiplier = event_multipliers.get(event.event_type, 1.0) return base_profit * multiplier def _select_target_platforms(self, event: DogEvent, profit_potential: float) -> List[str]: """Select which platforms to target based on event and profit potential""" platforms = [] # Always include Twitter/X for high-profit content if profit_potential > 2.0: platforms.append("twitter") # Instagram for visual content if event.event_type in ["zoomies", "play_bow", "tail_wag"]: platforms.append("instagram") # TikTok for high-energy content if event.event_type in ["zoomies", "bark"] and event.motion_data.get("intensity", 0) > 3.0: platforms.append("tiktok") # Default to Twitter if no specific matches if not platforms: platforms.append("twitter") return platforms# === 🤖 SOCIAL BOT ARMY ===class SocialBotArmy: """Multi-platform bot network for meme distribution""" def __init__(self): self.distribution_queue = queue.Queue() self.platform_clients = {} self.bot_accounts = {} def initialize(self): """Initialize bot army with platform clients""" self.setup_twitter_bots() self.setup_instagram_bots() self.setup_tiktok_bots() # Start distribution thread thread = threading.Thread(target=self._distribution_loop, daemon=True) thread.start() log("🤖 Social Bot Army initialized") def setup_twitter_bots(self): """Setup Twitter bot clients""" twitter_config = SOCIAL_PLATFORMS.get("twitter", {}) if not all([twitter_config.get("api_key"), twitter_config.get("api_secret")]): log("⚠️ Twitter credentials not found") return try: # Import Twitter API tweepy = lazy_import("tweepy") if not tweepy: return # Setup main bot account auth = tweepy.OAuthHandler( twitter_config["api_key"], twitter_config["api_secret"] ) auth.set_access_token( twitter_config["access_token"], twitter_config["access_secret"] ) api = tweepy.API(auth) self.platform_clients["twitter"] = api log("🐦 Twitter bot client initialized") except Exception as e: err(f"❌ Twitter setup failed: {e}") def setup_instagram_bots(self): """Setup Instagram bot clients""" # Instagram automation would use instagrapi or similar try: instagrapi = lazy_import("instagrapi") if not instagrapi: return ig_config = SOCIAL_PLATFORMS.get("instagram", {}) if not ig_config.get("username"): return client = instagrapi.Client() # Would login here but skipping for demo self.platform_clients["instagram"] = client log("📸 Instagram bot client initialized") except Exception as e: err(f"❌ Instagram setup failed: {e}") def setup_tiktok_bots(self): """Setup TikTok bot clients""" # TikTok automation would use custom scraping/API log("🎵 TikTok client placeholder - would implement custom automation") def queue_meme_for_distribution(self, meme: GeneratedMeme): """Add meme to distribution queue""" self.distribution_queue.put(meme) log(f"🚀 Queued meme for distribution: {meme.caption[:30]}...") def _distribution_loop(self): """Main distribution processing loop""" while True: try: if not self.distribution_queue.empty(): meme = self.distribution_queue.get() # Distribute to each target platform for platform in meme.target_platforms: success = self._post_to_platform(meme, platform) if success: # Update published status meme.published[platform] = True log(f"✅ Posted to {platform}: {meme.caption[:30]}...") # Simulate revenue generation self._simulate_revenue(meme, platform) time.sleep(10) # Wait between posts to avoid rate limits except Exception as e: err(f"❌ Distribution loop error: {e}") time.sleep(30) def _post_to_platform(self, meme: GeneratedMeme, platform: str) -> bool: """Post meme to specific platform""" try: if platform == "twitter" and "twitter" in self.platform_clients: return self._post_to_twitter(meme) elif platform == "instagram" and "instagram" in self.platform_clients: return self._post_to_instagram(meme) elif platform == "tiktok": return self._post_to_tiktok(meme) else: log(f"⚠️ Platform {platform} not available") return False except Exception as e: err(f"❌ Failed to post to {platform}: {e}") return False def _post_to_twitter(self, meme: GeneratedMeme) -> bool: """Post meme to Twitter""" try: api = self.platform_clients["twitter"] # Upload image media = api.media_upload(meme.image_path) # Create tweet text tweet_text = f"{meme.caption}\n\n{' '.join(meme.hashtags[:5])}" # Post tweet status = api.update_status(status=tweet_text, media_ids=[media.media_id]) log(f"🐦 Posted to Twitter: {status.id}") return True except Exception as e: err(f"❌ Twitter post failed: {e}") return False def _post_to_instagram(self, meme: GeneratedMeme) -> bool: """Post meme to Instagram""" try: client = self.platform_clients["instagram"] # Upload photo caption_text = f"{meme.caption}\n\n{' '.join(meme.hashtags)}" # Would post here but using placeholder log(f"📸 Instagram post simulated: {meme.caption[:30]}...") return True except Exception as e: err(f"❌ Instagram post failed: {e}") return False def _post_to_tiktok(self, meme: GeneratedMeme) -> bool: """Post meme to TikTok""" # TikTok posting would require video format log(f"🎵 TikTok post simulated: {meme.caption[:30]}...") return True def _simulate_revenue(self, meme: GeneratedMeme, platform: str): """Simulate revenue generation from meme""" # Simulate different revenue sources revenue_sources = ["ad_revenue", "engagement_bonus", "viral_multiplier"] for source in revenue_sources: if np.random.random() < 0.3: # 30% chance per source amount = np.random.uniform(0.1, meme.profit_potential) transaction = YieldTransaction( meme_id=meme.event_id, platform=platform, revenue_source=source, amount_usd=amount, amount_ltn=amount / 0.1, # $0.10 per LTN transaction_hash=None, timestamp=datetime.datetime.now().isoformat() ) db.record_yield(transaction) yield_engine.process_yield_transaction(transaction)# === 💰 YIELD ENGINE ===class YieldEngine: """Manages yield generation and distribution""" def __init__(self): self.total_yield_usd = 0.0 self.total_yield_ltn = 0.0 self.rent_progress = 0.0 def process_yield_transaction(self, transaction: YieldTransaction): """Process incoming yield transaction""" self.total_yield_usd += transaction.amount_usd self.total_yield_ltn += transaction.amount_ltn # Update rent progress self.rent_progress = (self.total_yield_usd / RENT_FREEDOM_TARGET) * 100 log(f"💰 Yield earned: ${transaction.amount_usd:.2f} ({transaction.amount_ltn:.1f} LTN)") log(f"📊 Total yield: ${self.total_yield_usd:.2f} | Rent progress: {self.rent_progress:.1f}%") # Check if rent target reached if self.total_yield_usd >= RENT_FREEDOM_TARGET: self.trigger_rent_freedom_celebration() def trigger_rent_freedom_celebration(self): """Trigger celebration when rent freedom is achieved""" log("🎉 RENT FREEDOM ACHIEVED! 🎉") log(f"💸 Total yield: ${self.total_yield_usd:.2f}") log(f"🏠 Monthly rent target reached: ${RENT_FREEDOM_TARGET}") # Could trigger special meme generation, social media announcement, etc. def get_yield_stats(self) -> Dict[str, Any]: """Get current yield statistics""" return { "total_usd": self.total_yield_usd, "total_ltn": self.total_yield_ltn, "rent_progress_percent": self.rent_progress, "rent_target": RENT_FREEDOM_TARGET, "monthly_rate": self.total_yield_usd / max(1, (time.time() - start_time) / 86400 * 30) # Rough monthly estimate }# === 🎯 LOLATRON TOKEN SYSTEM ===class LolatronTokenSystem: """Manages LTN token operations and DAO governance""" def __init__(self): self.total_supply = 0 self.max_supply = 1000000 # 1M LTN max self.holders = defaultdict(float) self.governance_proposals = [] def mint_tokens(self, recipient: str, amount: float, reason: str) -> bool: """Mint new LTN tokens""" if self.total_supply + amount > self.max_supply: log(f"⚠️ Cannot mint {amount} LTN - would exceed max supply") return False self.holders[recipient] += amount self.total_supply += amount log(f"🪙 Minted {amount} LTN for {recipient} ({reason})") return True def transfer_tokens(self, from_addr: str, to_addr: str, amount: float) -> bool: """Transfer tokens between addresses""" if self.holders[from_addr] < amount: return False # Apply 2% burn on transfer burn_amount = amount * 0.02 transfer_amount = amount - burn_amount self.holders[from_addr] -= amount self.holders[to_addr] += transfer_amount self.total_supply -= burn_amount # Deflationary burn log(f"🔄 Transferred {transfer_amount} LTN ({burn_amount} burned)") return True def create_governance_proposal(self, proposer: str, title: str, description: str) -> bool: """Create new governance proposal""" if self.holders[proposer] < 1000: # 1000 LTN threshold log(f"⚠️ {proposer} needs 1000 LTN to create proposals") return False proposal = { "id": len(self.governance_proposals), "proposer": proposer, "title": title, "description": description, "created_at": datetime.datetime.now().isoformat(), "votes_for": 0, "votes_against": 0, "status": "active" } self.governance_proposals.append(proposal) log(f"🗳️ New proposal created: {title}") return True# === 🎮 MAIN ORCHESTRATOR ===class LolanthropyOrchestrator: """Main system orchestrator""" def __init__(self): self.logger = logging.getLogger('lolanthropy.orchestrator') self.logger.info("🚀 Initializing Lolanthropy Orchestrator") try: self.logger.debug("Creating system components...") self.surveillance_engine = SurveillanceEngine() self.meme_forge = MemeForge() self.social_bot_army = SocialBotArmy() self.yield_engine = YieldEngine() self.token_system = LolatronTokenSystem() self.running = False self.logger.info("✅ System components initialized") except Exception as e: self.logger.critical(f"❌ Failed to initialize system components: {str(e)}") self.logger.debug(f"Stack trace: {traceback.format_exc()}") raise def initialize_system(self): """Initialize all system components""" self.logger.info("🚀 Starting system initialization...") try: # Initialize surveillance engine self.logger.debug("Initializing surveillance engine...") self.surveillance_engine.initialize_cameras() self.logger.info("✅ Surveillance engine initialized") # Initialize meme forge self.logger.debug("Initializing meme forge...") self.meme_forge.initialize() self.logger.info("✅ Meme forge initialized") # Initialize social bot army self.logger.debug("Initializing social bot army...") self.social_bot_army.initialize() self.logger.info("✅ Social bot army initialized") self.running = True self.logger.info("🎉 System initialization complete!") except Exception as e: self.logger.error(f"❌ Failed to initialize system: {str(e)}") self.logger.debug(f"Stack trace: {traceback.format_exc()}") self.running = False raise # Start surveillance (this will begin the entire pipeline) self.surveillance_engine.start_surveillance() log("✅ LOLAnthropy system fully operational") log("📊 Monitoring for dog events and profit opportunities...") def get_system_status(self): """Get comprehensive system status with detailed metrics""" self.logger.debug("Generating system status report...") try: # Get system metrics cpu_percent = psutil.cpu_percent(interval=0.5) memory = psutil.virtual_memory() disk = psutil.disk_usage('/') status = { "timestamp": datetime.datetime.now().isoformat(), "system": { "running": self.running, "uptime_seconds": time.time() - start_time, "cpu": { "percent": cpu_percent, "cores": psutil.cpu_count(logical=False), "threads": psutil.cpu_count(logical=True) }, "memory": { "total_gb": round(memory.total / (1024 ** 3), 2), "available_gb": round(memory.available / (1024 ** 3), 2), "used_gb": round(memory.used / (1024 ** 3), 2), "percent": memory.percent }, "disk": { "total_gb": round(disk.total / (1024 ** 3), 2), "used_gb": round(disk.used / (1024 ** 3), 2), "free_gb": round(disk.free / (1024 ** 3), 2), "percent": disk.percent }, "network": { "connections": len(psutil.net_connections(kind='inet')), "io": psutil.net_io_counters() }, "process": { "threads": threading.active_count(), "memory_info": psutil.Process().memory_info()._asdict() } }, "components": { "surveillance_engine": self.surveillance_engine.get_status(), "meme_forge": self.meme_forge.get_status(), "social_bot_army": self.social_bot_army.get_status(), "yield_engine": self.yield_engine.get_yield_stats(), "token_system": { "total_supply": self.token_system.total_supply, "active_holders": len(self.token_system.holders), "governance_proposals": len(self.token_system.governance_proposals) } }, "performance": { "gc": { "objects": len(gc.get_objects()), "garbage": len(gc.garbage), "enabled": gc.isenabled() }, "threads": [ {"name": t.name, "ident": t.ident, "daemon": t.daemon, "alive": t.is_alive()} for t in threading.enumerate() ] } } # Log critical metrics if cpu_percent > 80: self.logger.warning(f"⚠️ High CPU usage: {cpu_percent}%") if memory.percent > 80: self.logger.warning(f"⚠️ High memory usage: {memory.percent}%") if disk.percent > 80: self.logger.warning(f"⚠️ High disk usage: {disk.percent}%") self.logger.debug("System status report generated") return status except Exception as e: self.logger.error(f"❌ Failed to generate system status: {str(e)}") self.logger.debug(f"Stack trace: {traceback.format_exc()}") return {"error": "Failed to generate system status", "details": str(e)} def emergency_shutdown(self, reason: str = "unspecified"): """ Emergency system shutdown with graceful cleanup Args: reason: Reason for the shutdown (e.g., 'critical_error', 'user_request', 'system_maintenance') """ self.logger.critical(f"🛑 EMERGENCY SHUTDOWN INITIATED. Reason: {reason}") start_time = time.time() self.running = False try: # Log system state before shutdown self.logger.info("Capturing final system status...") status = self.get_system_status() self.logger.debug(f"System status before shutdown: {json.dumps(status, indent=2, default=str)}") # Stop surveillance engine self.logger.info("Stopping surveillance engine...") if hasattr(self, 'surveillance_engine'): self.surveillance_engine.running = False self.logger.debug("Surveillance engine shutdown signal sent") # Stop meme generation self.logger.info("Stopping meme generation...") if hasattr(self, 'meme_forge') and hasattr(self.meme_forge, 'running'): self.meme_forge.running = False # Stop social bot army self.logger.info("Stopping social bot army...") if hasattr(self, 'social_bot_army') and hasattr(self.social_bot_army, 'running'): self.social_bot_army.running = False # Save critical state self.logger.info("Backing up database...") try: db.backup() self.logger.info("✅ Database backup completed successfully") except Exception as e: self.logger.error(f"❌ Failed to backup database: {str(e)}") # Log shutdown completion shutdown_duration = time.time() - start_time self.logger.critical( f"✅ Emergency shutdown completed in {shutdown_duration:.2f} seconds. " f"Reason: {reason}" ) # Flush all logs for handler in logging.getLogger().handlers: handler.flush() except Exception as e: self.logger.critical(f"❌ CRITICAL ERROR DURING SHUTDOWN: {str(e)}") self.logger.debug(f"Shutdown error traceback: {traceback.format_exc()}") raise# === 🎯 INITIALIZE GLOBAL INSTANCES ===start_time = time.time()# Create global instancessurveillance_engine = SurveillanceEngine()meme_forge = MemeForge()social_bot_army = SocialBotArmy()yield_engine = YieldEngine()token_system = LolatronTokenSystem()# Main orchestratororchestrator = LolanthropyOrchestrator()# === 💬 COMMAND INTERFACE ===def run_lolanthropy(): """ Main entry point - starts the entire LOLAnthropy system This function initializes all components, handles the command loop, and manages system lifecycle with comprehensive error handling. """ # Set up logger for the main application logger = logging.getLogger('lolanthropy.runner') logger.info("🚀 Starting LOLAnthropy Surveillance-to-Meme Profit Stack™") # Display startup banner startup_banner = """ ╔══════════════════════════════════════════════════════════════╗ ║ ║ ║ 🐕 LOLAnthropy Surveillance-to-Meme Profit Stack™ ║ ║ ║ ║ Ring Cameras → AI Memes → Social Bots → Yield Farming ║ ║ ║ ║ 🚀 RENT FREEDOM THROUGH DOG MEMES 🚀 ║ ║ ║ ╚══════════════════════════════════════════════════════════════╝ """ print(startup_banner) logger.debug("Startup banner displayed") # Check for required API keys with detailed logging logger.info("🔑 Validating API keys and configuration") missing_keys = [] # Check OpenRouter API key openrouter_key = os.getenv("OPENROUTER_KEY") if not openrouter_key: logger.warning("OPENROUTER_KEY environment variable not set") missing_keys.append("OPENROUTER_KEY") else: logger.debug("OpenRouter API key found (first 4 chars: %s)", openrouter_key[:4] + '...' if openrouter_key else 'None') # Check Twitter API configuration twitter_config = SOCIAL_PLATFORMS.get("twitter", {}) if not twitter_config.get("api_key"): logger.warning("Twitter API key not configured in SOCIAL_PLATFORMS") missing_keys.append("TWITTER_API_KEY") else: logger.debug("Twitter API key configured") # Report missing keys if missing_keys: error_msg = f"Missing required API keys: {', '.join(missing_keys)}" logger.error(error_msg) # Print user-friendly message print(f"\n{'⚠️ WARNING: MISSING API KEYS ':-<80}") print("The following required API keys are missing or invalid:") for key in missing_keys: print(f" - {key}") print("\nSet these environment variables before proceeding for full functionality:") for key in missing_keys: print(f" export {key}='your_api_key_here'") print("\nSome features may be limited or simulated without proper API keys.") print("-" * 80 + "\n") # Wait for user confirmation to continue try: if input("Continue with limited functionality? (y/N): ").lower() != 'y': logger.info("User chose to exit due to missing API keys") return except KeyboardInterrupt: logger.info("Startup aborted by user") return else: logger.info("✅ All required API keys are present") # Initialize system with error handling try: logger.info("🔌 Initializing system components...") orchestrator.initialize_system() logger.info("✅ System initialization complete") # Display command interface print("\n" + "="*60) print("🎮 LOLANTHROPY COMMAND INTERFACE".center(60)) print("="*60) print("Type 'help' for available commands\n") # Main command loop while True: try: # Get user input with timeout to allow for periodic tasks try: command = input("\n🐕 LOLAnthropy> ").strip().lower() except (EOFError, KeyboardInterrupt): logger.info("Received shutdown signal from user") raise start_time = time.time() logger.debug(f"Processing command: {command}") if command == "status": logger.info("Generating system status report...") status = orchestrator.get_system_status() print("\n" + "📊 SYSTEM STATUS ".center(60, "=")) print(json.dumps(status, indent=2, default=str)) logger.debug("Status report generated") elif command == "yield": logger.info("Fetching yield statistics...") stats = yield_engine.get_yield_stats() print("\n" + "💰 YIELD STATISTICS ".center(60, "=")) print(f"Total Yield: ${stats['total_usd']:.2f}") print(f"LTN Earned: {stats['total_ltn']:,.1f} LTN") print(f"Rent Progress: {stats['rent_progress_percent']:.1f}%") logger.debug("Yield statistics displayed") elif command == "tokens": logger.info("Fetching token information...") print("\n" + "🪙 TOKEN INFORMATION ".center(60, "=")) print(f"Total Supply: {token_system.total_supply:,.0f} LTN") print(f"Token Holders: {len(token_system.holders)}") active_proposals = len([p for p in token_system.governance_proposals if p['status'] == 'active']) print(f"Active Proposals: {active_proposals}") logger.debug("Token information displayed") elif command == "shutdown": logger.info("Initiating graceful shutdown...") print("\n🛑 Initiating system shutdown...") orchestrator.emergency_shutdown(reason="user_request") logger.info("Shutdown sequence complete") break elif command == "help": help_text = """ COMMAND REFERENCE: {'=' * 60} status - Show detailed system status and metrics yield - Display yield statistics and earnings tokens - Show LTN token information and governance shutdown - Gracefully shut down the system help - Show this help message Press Ctrl+C at any time to initiate emergency shutdown. """ print(help_text) elif command == "": continue else: logger.warning(f"Unknown command: {command}") print(f"❌ Unknown command: {command}") print("Type 'help' for available commands.") # Log command execution time exec_time = time.time() - start_time logger.debug(f"Command '{command}' executed in {exec_time:.3f}s") except KeyboardInterrupt: logger.info("Keyboard interrupt received, initiating shutdown") print("\n🛑 Shutting down LOLAnthropy system...") orchestrator.emergency_shutdown(reason="keyboard_interrupt") break except Exception as e: error_msg = f"Error processing command: {str(e)}" logger.error(error_msg, exc_info=True) print(f"\n❌ {error_msg}") print("Check the logs for more details.") except Exception as e: error_msg = f"Fatal error during system initialization: {str(e)}" logger.critical(error_msg, exc_info=True) print(f"\n💥 {error_msg}") print("Please check the logs for details and contact support if the issue persists.")# === 🚀 MAIN EXECUTION ===if __name__ == "__main__": try: run_lolanthropy() except Exception as e: err(f"💥 Critical system error: {e}") print("System crashed - check logs for details") finally: print("👋 LOLAnthropy system terminated")import osimport reimport jsonimport timeimport uuidimport queueimport threadingimport shutilimport subprocessimport requestsimport loggingimport datetimeimport tempfileimport gcimport psutilimport numpy as npimport mathimport asyncioimport aiohttpimport hashlibfrom typing import Dict, List, Tuple, Optional, Union, Anyfrom dataclasses import dataclass, asdictfrom collections import defaultdict, dequeimport cv2import base64import sqlite3import picklefrom PIL import Image, ImageDraw, ImageFontimport websocketimport socketimport sslfrom cryptography.hazmat.primitives import hashesfrom cryptography.hazmat.primitives.asymmetric import ec, kyberfrom cryptography.hazmat.primitives.kdf.hkdf import HKDFfrom web3 import Web3, HTTPProviderfrom web3.middleware import geth_poa_middlewareimport ipfshttpclient# === 🧠 OPTIMIZED IMPORTS (LAZY LOADING) ===_lazy_modules = {}def lazy_import(module_name): if module_name not in _lazy_modules: try: if "." in module_name: parts = module_name.split(".") module = __import__(parts[0]) for part in parts[1:]: module = getattr(module, part) _lazy_modules[module_name] = module else: _lazy_modules[module_name] = __import__(module_name) logging.info(f"🧩 Lazy loaded: {module_name}") except ImportError as e: logging.error(f"❌ Failed to lazy load {module_name}: {e}") return None return _lazy_modules[module_name]# === ⚙️ CONFIGURATION ===ROOT_DIR = os.path.join(os.path.expanduser("~"), ".lolanthropy")SURVEILLANCE_DIR = os.path.join(ROOT_DIR, "surveillance")MEME_DIR = os.path.join(ROOT_DIR, "memes")YIELD_DIR = os.path.join(ROOT_DIR, "yield")BLOCKCHAIN_DIR = os.path.join(ROOT_DIR, "blockchain")DATABASE_PATH = os.path.join(ROOT_DIR, "lolanthropy.db")MEME_POTENTIAL_THRESHOLD = 0.8RENT_FREEDOM_TARGET = 2500MIN_PROFIT_PER_MEME = 0.5VIRAL_THRESHOLD = 100000ZOOMIES_VELOCITY_THRESHOLD = 2.0TAIL_WAG_MIN_FREQUENCY = 3.5RING_CAMERAS = [ {"id": "living_room", "rtsp": "192.168.1:8554/stream"}, {"id": "kitchen", "rtsp": "rtsp://admin:password@192.168.1.101:554/stream1"}, {"id": "backyard", "rtsp": "rtsp://admin:password@192.168.1.102:554/stream1"}]SOCIAL_PLATFORMS = { "twitter": { "api_key": os.getenv("TWITTER_API_KEY"), "api_secret": os.getenv("TWITTER_API_SECRET"), "access_token": os.getenv("TWITTER_ACCESS_TOKEN"), "access_secret": os.getenv("TWITTER_ACCESS_SECRET"), "bot_accounts": 5, "post_interval": 3600 }, "tiktok": { "session_id": os.getenv("TIKTOK_SESSION_ID"), "bot_accounts": 3, "post_interval": 7200 }, "instagram": { "username": os.getenv("IG_USERNAME"), "password": os.getenv("IG_PASSWORD"), "bot_accounts": 4, "post_interval": 5400 }}# Ensure directoriesfor d in [ROOT_DIR, SURVEILLANCE_DIR, MEME_DIR, YIELD_DIR, BLOCKCHAIN_DIR]: os.makedirs(d, exist_ok=True)logging.basicConfig( level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", handlers=[ logging.FileHandler(os.path.join(ROOT_DIR, "lolanthropy.log")), logging.StreamHandler() ])log = logging.infoerr = logging.error# === 📊 DATA CLASSES ===@dataclassclass DogEvent: timestamp: str camera_id: str event_type: str confidence: float duration: float frame_path: str audio_features: Dict[str, float] motion_data: Dict[str, Any] meme_potential: float processed: bool = False@dataclassclass GeneratedMeme: event_id: str template_id: str caption: str image_path: str video_path: Optional[str] hashtags: List[str] target_platforms: List[str] engagement_prediction: float profit_potential: float created_at: str published: Dict[str, bool] nft_tx_hash: Optional[str] = None@dataclassclass YieldTransaction: meme_id: str platform: str revenue_source: str amount_usd: float amount_ltn: float transaction_hash: Optional[str] timestamp: str# === 🗄️ DATABASE MANAGER ===class LolanthropyDB: def __init__(self, db_path=DATABASE_PATH): self.db_path = db_path self.init_db() def init_db(self): with sqlite3.connect(self.db_path) as conn: conn.executescript(""" CREATE TABLE IF NOT EXISTS dog_events ( id TEXT PRIMARY KEY, timestamp TEXT, camera_id TEXT, event_type TEXT, confidence REAL, duration REAL, frame_path TEXT, audio_features TEXT, motion_data TEXT, meme_potential REAL, processed INTEGER DEFAULT 0 ); CREATE TABLE IF NOT EXISTS generated_memes ( id TEXT PRIMARY KEY, event_id TEXT, template_id TEXT, caption TEXT, image_path TEXT, video_path TEXT, hashtags TEXT, target_platforms TEXT, engagement_prediction REAL, profit_potential REAL, created_at TEXT, published TEXT, nft_tx_hash TEXT ); CREATE TABLE IF NOT EXISTS yield_transactions ( id TEXT PRIMARY KEY, meme_id TEXT, platform TEXT, revenue_source TEXT, amount_usd REAL, amount_ltn REAL, transaction_hash TEXT, timestamp TEXT ); """) log("🗄️ Database initialized") def insert_event(self, event: DogEvent): with sqlite3.connect(self.db_path) as conn: conn.execute(""" INSERT OR REPLACE INTO dog_events (id, timestamp, camera_id, event_type, confidence, duration, frame_path, audio_features, motion_data, meme_potential, processed) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """, ( uuid.uuid4().hex, event.timestamp, event.camera_id, event.event_type, event.confidence, event.duration, event.frame_path, json.dumps(event.audio_features), json.dumps(event.motion_data), event.meme_potential, int(event.processed) )) def get_unprocessed_events(self, limit=10): with sqlite3.connect(self.db_path) as conn: cursor = conn.execute(""" SELECT * FROM dog_events WHERE processed = 0 AND meme_potential > ? ORDER BY meme_potential DESC, timestamp DESC LIMIT ? """, (MEME_POTENTIAL_THRESHOLD, limit)) return cursor.fetchall() def insert_meme(self, meme: GeneratedMeme): with sqlite3.connect(self.db_path) as conn: conn.execute(""" INSERT OR REPLACE INTO generated_memes (id, event_id, template_id, caption, image_path, video_path, hashtags, target_platforms, engagement_prediction, profit_potential, created_at, published, nft_tx_hash) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """, ( uuid.uuid4().hex, meme.event_id, meme.template_id, meme.caption, meme.image_path, meme.video_path, json.dumps(meme.hashtags), json.dumps(meme.target_platforms), meme.engagement_prediction, meme.profit_potential, meme.created_at, json.dumps(meme.published), meme.nft_tx_hash )) def record_yield(self, transaction: YieldTransaction): with sqlite3.connect(self.db_path) as conn: conn.execute(""" INSERT INTO yield_transactions (id, meme_id, platform, revenue_source, amount_usd, amount_ltn, transaction_hash, timestamp) VALUES (?, ?, ?, ?, ?, ?, ?, ?) """, ( uuid.uuid4().hex, transaction.meme_id, transaction.platform, transaction.revenue_source, transaction.amount_usd, transaction.amount_ltn, transaction.transaction_hash, transaction.timestamp ))db = LolanthropyDB()# === 🎥 SURVEILLANCE ENGINE ===class SurveillanceEngine: def __init__(self): self.cameras = {} self.detection_models = {} self.event_queue = queue.Queue() self.running = False def initialize_cameras(self): for camera_config in RING_CAMERAS: try: cap = cv2.VideoCapture(camera_config["rtsp"]) if cap.isOpened(): self.cameras[camera_config["id"]] = { "capture": cap, "config": camera_config, "last_frame": None } log(f"📹 Camera initialized: {camera_config['id']}") else: err(f"❌ Failed to connect to camera: {camera_config['id']}") except Exception as e: err(f"❌ Camera error: {e}") def load_detection_models(self): torch = lazy_import("torch") librosa = lazy_import("librosa") if torch: self.detection_models["yolo"] = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) log("🧠 YOLO model loaded") if librosa: # Placeholder for audio model init self.detection_models["audio"] = {"sample_rate": 44100, "energy_threshold": 0.02} log("🔊 Audio model loaded") def start_surveillance(self): self.running = True self.initialize_cameras() self.load_detection_models() for cam_id in self.cameras: threading.Thread(target=self._camera_loop, args=(cam_id,), daemon=True).start() threading.Thread(target=self._process_events_loop, daemon=True).start() log("🚀 Surveillance started") def _camera_loop(self, cam_id): camera = self.cameras[cam_id] cap = camera["capture"] frame_count = 0 while self.running: ret, frame = cap.read() if not ret: time.sleep(0.1) continue frame_count += 1 camera["last_frame"] = frame if frame_count % 10 == 0: # Dog detection results = self.detection_models["yolo"](frame) dogs = results.pandas().xyxy[0] detected = len(dogs[dogs['class'] == 16]) > 0 if detected: event = DogEvent( timestamp=datetime.datetime.now().isoformat(), camera_id=cam_id, event_type="bark", confidence=0.9, duration=1.0, frame_path=self._save_frame(frame, cam_id, "bark"), audio_features={}, motion_data={}, meme_potential=0.7 ) self.event_queue.put(event) time.sleep(0.033) def _save_frame(self, frame, cam_id, event_type): ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"{ts}_{cam_id}_{event_type}.jpg" path = os.path.join(SURVEILLANCE_DIR, filename) cv2.imwrite(path, frame) return path def _process_events_loop(self): while self.running: if not self.event_queue.empty(): event = self.event_queue.get() db.insert_event(event) log(f"🐕 Event stored: {event.event_type} at {event.camera_id}") if event.meme_potential >= MEME_POTENTIAL_THRESHOLD: meme_forge.queue_event_for_meme(event) time.sleep(0.1)# === 🎨 MEMEFORGE ENGINE ===class MemeForge: def __init__(self): self.meme_queue = queue.Queue() self.templates = self._load_templates() self.font = ImageFont.load_default() self.llm_client = None self.nft_generator = MemeNFTGenerator() def _load_templates(self): # Minimal template mapping return { "drake_pointing": "templates/drake.jpg", "distracted_boyfriend": "templates/distracted_boyfriend.jpg" } def initialize(self): self._init_llm() threading.Thread(target=self._meme_generation_loop, daemon=True).start() log("🎨 MemeForge ready") def _init_llm(self): try: requests.get("http://localhost:11434/api/tags", timeout=1) self.llm_client = "ollama" log("🧠 LLM Ollama connected") except: if os.getenv("OPENROUTER_KEY"): self.llm_client = "openrouter" log("🧠 LLM OpenRouter connected") else: err("❌ No LLM available") def queue_event_for_meme(self, event: DogEvent): self.meme_queue.put(event) log(f"🎨 Event queued for meme: {event.event_type}") def _meme_generation_loop(self): while True: if not self.meme_queue.empty(): event = self.meme_queue.get() meme = self.generate_meme(event) if meme: db.insert_meme(meme) social_bot_army.queue_meme_for_distribution(meme) log(f"🎨 Meme generated & queued for social: {meme.caption[:50]}") time.sleep(1) def generate_meme(self, event: DogEvent) -> Optional[GeneratedMeme]: caption = self._generate_caption(event) if not caption: caption = "Just dog things 🐕" meme_image_path = self._create_meme_image(event.frame_path, caption) if not meme_image_path: return None hashtags = ["#dogmemes", "#funny", "#crypto"] profit = MIN_PROFIT_PER_MEME * 10 meme = GeneratedMeme( event_id=event.timestamp, template_id="drake_pointing", caption=caption, image_path=meme_image_path, video_path=None, hashtags=hashtags, target_platforms=["twitter"], engagement_prediction=0.9, profit_potential=profit, created_at=datetime.datetime.now().isoformat(), published={p: False for p in ["twitter"]} ) if profit > 5.0: nft_tx = self.nft_generator.create_meme_nft(meme) meme.nft_tx_hash = nft_tx log(f"🖼️ NFT minted: {nft_tx}") return meme def _generate_caption(self, event: DogEvent) -> Optional[str]: prompt = f"Generate a funny crypto dog meme caption about a {event.event_type}" try: if self.llm_client == "ollama": res = requests.post("http://localhost:11434/api/generate", json={"model": "mistral", "prompt": prompt, "stream": False}, timeout=10) if res.status_code == 200: return res.json().get("response", "").strip()[:50] elif self.llm_client == "openrouter": headers = {"Authorization": f"Bearer {os.getenv('OPENROUTER_KEY')}"} payload = {"model": "mistral-7b-instruct", "messages": [{"role": "user", "content": prompt}], "max_tokens": 50} res = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload, timeout=10) if res.status_code == 200: return res.json()["choices"][0]["message"]["content"].strip()[:50] except Exception as e: err(f"❌ LLM caption error: {e}") return None def _create_meme_image(self, base_img_path, caption) -> Optional[str]: try: img = Image.open(base_img_path).convert("RGB").resize((800, 600)) draw = ImageDraw.Draw(img) font = self.font w, h = draw.textsize(caption, font=font) draw.text(((800 - w) / 2, 20), caption, font=font, fill="white") meme_path = os.path.join(MEME_DIR, f"meme_{uuid.uuid4().hex[:8]}.jpg") img.save(meme_path, quality=85) return meme_path except Exception as e: err(f"❌ Meme image creation failed: {e}") return Noneclass MemeNFTGenerator: def __init__(self): self.blockchain = BlockchainManager() self.ipfs_client = ipfshttpclient.connect() self.nft_metadata = {} def create_meme_nft(self, meme: GeneratedMeme) -> Optional[str]: try: ipfs_hash = self.ipfs_client.add(meme.image_path)["Hash"] metadata = { "name": f"Meme #{meme.event_id}", "description": meme.caption, "image": f"ipfs://{ipfs_hash}", "attributes": [ {"trait_type": "Meme Potential", "value": meme.profit_potential} ] } metadata_hash = self.ipfs_client.add_json(metadata) self.nft_metadata[meme.event_id] = metadata_hash receipt = asyncio.run( self.blockchain.execute_contract_method( "MemeNFT", "mint", [os.getenv("DEPLOYER_ADDRESS"), f"ipfs://{metadata_hash}"] ) ) return receipt["transactionHash"] except Exception as e: err(f"❌ NFT mint failed: {e}") return None# === 💰 GUARANTEED INCOME MODULES ===class AdRevenueDaemon: """Automated ad revenue collection from social platforms""" def __init__(self): self.platform_rates = { "tiktok": 0.02, # $0.02 per 1k views "instagram": 0.015, "youtube": 0.03 } self.minimum_payouts = { "tiktok": 50, "instagram": 100, "youtube": 100 } def calculate_earnings(self, platform: str, views: int) -> float: """Calculate ad revenue based on platform rates""" return (views / 1000) * self.platform_rates.get(platform, 0.01) def request_payout(self, platform: str, amount: float): """Automatically request payout when threshold reached""" if amount >= self.minimum_payouts.get(platform, 50): log(f"💰 Requesting ${amount:.2f} payout from {platform}") # Actual API integration would go here return True return Falseclass AffiliateInjector: """Injects affiliate links into meme captions""" def __init__(self): self.affiliate_products = { "dog": [ {"name": "Premium Dog Food", "link": "https://affiliate.com/dogfood?ref=lolanthropy", "commission": 0.15}, {"name": "Dog Toy Bundle", "link": "https://affiliate.com/toys?ref=lolanthropy", "commission": 0.20} ], "crypto": [ {"name": "Ledger Wallet", "link": "https://affiliate.com/ledger?ref=lolanthropy", "commission": 0.10}, {"name": "Coinbase Signup", "link": "https://affiliate.com/coinbase?ref=lolanthropy", "commission": 5.00} ] } def inject_link(self, meme: GeneratedMeme) -> GeneratedMeme: """Add relevant affiliate link to meme caption""" category = "dog" if np.random.random() > 0.3 else "crypto" product = np.random.choice(self.affiliate_products[category]) # Add UTM parameters for tracking utm_link = f"{product['link']}&utm_source=lolanthropy&utm_medium=meme&utm_campaign={meme.event_id}" new_caption = f"{meme.caption}\n\n🔗 {product['name']}: {utm_link}" # Create enhanced version of meme return self._create_affiliate_meme(meme, new_caption, product) def _create_affiliate_meme(self, original_meme: GeneratedMeme, new_caption: str, product: dict) -> GeneratedMeme: """Create new meme version with affiliate branding""" try: # Add affiliate branding to image img = Image.open(original_meme.image_path) draw = ImageDraw.Draw(img) # Add affiliate badge draw.rectangle([10, 10, 150, 40], fill="yellow") draw.text((15, 15), "SPONSORED", fill="black", font=meme_forge.fonts["impact_small"]) # Save new version aff_path = original_meme.image_path.replace(".jpg", "_affiliate.jpg") img.save(aff_path) # Create enhanced meme object return GeneratedMeme( event_id=original_meme.event_id, template_id=original_meme.template_id, caption=new_caption, image_path=aff_path, video_path=original_meme.video_path, hashtags=original_meme.hashtags + ["#ad", "#sponsored"], target_platforms=original_meme.target_platforms, engagement_prediction=original_meme.engagement_prediction * 0.8, # Slight reduction due to ad profit_potential=original_meme.profit_potential + product["commission"], created_at=datetime.datetime.now().isoformat(), published=original_meme.published ) except Exception as e: err(f"❌ Affiliate meme creation failed: {e}") return original_memeclass TrendAligner: """Aligns memes with real-time social trends""" def __init__(self): self.trend_refresh_interval = 3600 # 1 hour self.last_refresh = 0 self.current_trends = {} def refresh_trends(self): """Fetch current trends from social platforms""" try: # Twitter trends twitter_trends = requests.get( "https://api.twitter.com/1.1/trends/place.json?id=1", headers={"Authorization": f"Bearer {os.getenv('TWITTER_BEARER_TOKEN')}"} ).json() self.current_trends["twitter"] = [t["name"] for t in twitter_trends[0]["trends"][:10]] # TikTok trends (simulated) self.current_trends["tiktok"] = [ "#DogTok", "#CryptoNews", "#MemeEconomy", "#FinanceTips", "#PassiveIncome" ] log("📈 Refreshed social media trends") self.last_refresh = time.time() except Exception as e: err(f"❌ Trend refresh failed: {e}") # Fallback to generic trends self.current_trends = { "twitter": ["#Crypto", "#DogsOfTwitter", "#Memes"], "tiktok": ["#DogTok", "#FunnyPets"] } def align_meme(self, meme: GeneratedMeme) -> GeneratedMeme: """Align meme with current trends""" # Refresh trends if needed if time.time() - self.last_refresh > self.trend_refresh_interval: self.refresh_trends() # Get relevant trends for the meme's platforms relevant_trends = [] for platform in meme.target_platforms: relevant_trends.extend(self.current_trends.get(platform, [])) # Add top 3 relevant trends to hashtags if relevant_trends: meme.hashtags = relevant_trends[:3] + meme.hashtags return memeclass CPMOptimizer: """Routes memes to highest-paying platforms""" def __init__(self): self.cpm_rates = { "twitter": 0.25, # $0.25 per 1k impressions "instagram": 0.35, "tiktok": 0.40, "youtube": 0.60 } self.historical_performance = defaultdict(lambda: defaultdict(float)) def select_platforms(self, meme: GeneratedMeme) -> List[str]: """Select platforms with highest expected revenue""" # Calculate expected revenue per platform platform_scores = {} for platform in self.cpm_rates.keys(): # Base CPM rate score = self.cpm_rates[platform] # Engagement boost if platform in meme.target_platforms: score *= 1.2 # Historical performance boost score *= (1 + self.historical_performance[platform].get(meme.event_type, 0)) platform_scores[platform] = score # Select top 3 platforms best_platforms = sorted(platform_scores, key=platform_scores.get, reverse=True)[:3] return best_platforms def update_performance(self, platform: str, event_type: str, revenue: float, views: int): """Update historical performance metrics""" if views > 0: rpm = revenue / (views / 1000) self.historical_performance[platform][event_type] = rpm * 0.1 # Weighted averageclass SponsorSlotBroker: """Manages sponsored meme slots""" def __init__(self): self.available_slots = 3 # Slots per day self.slot_price = 50.00 # Base price per slot self.booked_slots = {} self.sponsor_queue = queue.Queue() def book_slot(self, sponsor: str, amount: float): """Book sponsored slot""" if self.available_slots > 0: self.available_slots -= 1 self.booked_slots[sponsor] = { "amount": amount, "booked_at": datetime.datetime.now().isoformat() } log(f"📦 Sponsored slot booked by {sponsor} for ${amount:.2f}") return True return False def process_sponsor_queue(self): """Process next sponsor in queue""" if not self.sponsor_queue.empty() and self.available_slots > 0: sponsor, amount = self.sponsor_queue.get() self.book_slot(sponsor, amount) def inject_sponsorship(self, meme: GeneratedMeme) -> GeneratedMeme: """Inject sponsorship into meme if slot available""" if self.booked_slots: # Select random sponsor sponsor = np.random.choice(list(self.booked_slots.keys())) sponsor_data = self.booked_slots[sponsor] # Create sponsored version return self._create_sponsored_meme(meme, sponsor, sponsor_data["amount"]) return meme def _create_sponsored_meme(self, original_meme: GeneratedMeme, sponsor: str, amount: float) -> GeneratedMeme: """Create sponsored version of meme""" try: # Add sponsor branding img = Image.open(original_meme.image_path) draw = ImageDraw.Draw(img) # Add sponsor banner banner_height = 40 draw.rectangle([0, img.height - banner_height, img.width, img.height], fill="#FFD700") draw.text((10, img.height - banner_height + 10), f"Sponsored by {sponsor}", fill="black", font=meme_forge.fonts["impact_small"]) # Save new version sponsor_path = original_meme.image_path.replace(".jpg", "_sponsored.jpg") img.save(sponsor_path) # Create enhanced meme object return GeneratedMeme( event_id=original_meme.event_id, template_id=original_meme.template_id, caption=f"{original_meme.caption}\n\n📢 Sponsored by {sponsor}", image_path=sponsor_path, video_path=original_meme.video_path, hashtags=original_meme.hashtags + ["#sponsored", "#ad"], target_platforms=original_meme.target_platforms, engagement_prediction=original_meme.engagement_prediction * 0.9, # Slight reduction profit_potential=original_meme.profit_potential + amount, created_at=datetime.datetime.now().isoformat(), published=original_meme.published ) except Exception as e: err(f"❌ Sponsored meme creation failed: {e}") return original_memeclass NFTRevenueTracker: """Tracks NFT revenue and royalties""" def __init__(self): self.royalty_rate = 0.1 # 10% royalty on secondary sales self.marketplace_apis = { "opensea": "https://api.opensea.io/api/v1", "rarible": "https://api.rarible.org/v0.1" } def track_sales(self, nft_id: str): """Check marketplaces for NFT sales""" try: # Check OpenSea response = requests.get( f"{self.marketplace_apis['opensea']}/asset/{nft_id}", headers={"X-API-KEY": os.getenv("OPENSEA_API_KEY")} ) if response.status_code == 200: asset_data = response.json() if asset_data.get("last_sale"): self.process_sale(asset_data["last_sale"], "opensea") except Exception as e: err(f"❌ NFT tracking error: {e}") def process_sale(self, sale_data: dict, marketplace: str): """Process NFT sale and distribute royalties""" # Extract sale details price_eth = float(sale_data["total_price"]) / 10**18 usd_price = self.eth_to_usd(price_eth) royalty = usd_price * self.royalty_rate # Record transaction transaction = YieldTransaction( meme_id=sale_data.get("token_id", "unknown"), platform=marketplace, revenue_source="nft_royalty", amount_usd=royalty, amount_ltn=royalty / 0.1, # $0.10 per LTN transaction_hash=sale_data.get("transaction_hash"), timestamp=datetime.datetime.now().isoformat() ) db.record_yield(transaction) yield_engine.process_yield_transaction(transaction) def eth_to_usd(self, eth_amount: float) -> float: """Convert ETH to USD""" try: response = requests.get("https://api.coingecko.com/api/v3/simple/price?ids=ethereum&vs_currencies=usd") eth_price = response.json()["ethereum"]["usd"] return eth_amount * eth_price except: return eth_amount * 1800 # Fallback estimateclass StableYieldRouter: """Routes yield to stablecoin vaults""" def __init__(self): self.vault_allocation = { "usdc": 0.6, # 60% to stablecoins "lolatron": 0.3, # 30% to LTN staking "risk": 0.1 # 10% to high-risk assets } self.vault_apis = { "aave": "https://aave-api.com", "yearn": "https://yearn.finance/api" } def distribute_yield(self, amount_usd: float): """Distribute yield according to allocation strategy""" distributions = {} for asset, allocation in self.vault_allocation.items(): amount = amount_usd * allocation if asset == "usdc": distributions["usdc"] = self.deposit_to_stable_vault(amount) elif asset == "lolatron": distributions["lolatron"] = self.stake_lolatron(amount) elif asset == "risk": distributions["risk"] = self.invest_risk(amount) log(f"📊 Yield distributed: {json.dumps(distributions, indent=2)}") return distributions def deposit_to_stable_vault(self, amount: float) -> dict: """Deposit to stablecoin vault (simulated)""" return { "vault": "Aave USDC", "amount": amount, "apy": 0.05, "status": "success" } def stake_lolatron(self, amount: float) -> dict: """Stake LTN tokens (simulated)""" ltn_amount = amount / 0.10 # $0.10 per LTN return { "action": "staked", "amount_ltn": ltn_amount, "apy": 0.15, "status": "success" } def invest_risk(self, amount: float) -> dict: """Invest in high-risk assets (simulated)""" return { "action": "invested", "amount": amount, "assets": ["DOGE", "SHIB", "PEPE"], "status": "success" }class RentNFTManager: """Manages Rent Freedom NFTs""" def __init__(self): self.rent_nft_contract = "0xRentNFTContractAddress" self.rent_thresholds = [500, 1000, 1500, 2000, 2500] # Milestone thresholds def check_rent_milestone(self, total_yield: float): """Mint NFT when rent milestone reached""" for threshold in self.rent_thresholds: if total_yield >= threshold: # Mint NFT token_id = self.mint_rent_nft(threshold) log(f"🏆 Rent milestone NFT minted: ${threshold} - Token #{token_id}") # Remove threshold to prevent duplicate minting self.rent_thresholds.remove(threshold) def mint_rent_nft(self, amount: float) -> str: """Mint Rent Freedom NFT (simulated)""" token_id = f"RENT-{amount}-{uuid.uuid4().hex[:6]}" # Actual blockchain minting would go here return token_idclass SponsorAutoPitcher: """Automates sponsor outreach""" def __init__(self): self.brand_db = [ {"name": "PetCo", "email": "marketing@petco.com", "products": ["dog food", "toys"]}, {"name": "Crypto.com", "email": "partnerships@crypto.com", "products": ["wallets", "exchanges"]}, {"name": "Coinbase", "email": "brand@coinbase.com", "products": ["exchange", "earn"]} ] self.pitch_interval = 86400 # 24 hours self.last_pitch = 0 def pitch_brands(self): """Pitch to brands if enough time has passed""" if time.time() - self.last_pitch > self.pitch_interval: for brand in self.brand_db: self.send_pitch(brand) self.last_pitch = time.time() def send_pitch(self, brand: dict): """Send automated pitch to brand""" try: # Generate performance report report = self.generate_performance_report() # Send email subject = f"Partnership Opportunity: Viral Dog Memes x {brand['name']}" body = f"""Dear {brand['name']} Team,Our viral meme platform LOLAnthropy generates {report['avg_views']} views per meme across TikTok, Instagram, and Twitter. We'd love to discuss how {brand['name']} can leverage our audience through:- Sponsored meme slots- Product placement- Branded contentOur recent performance:- 30-day views: {report['month_views']}- Engagement rate: {report['engagement']}%- Top meme: {report['top_meme']}Let's schedule a call to discuss partnership opportunities!Best,The LOLAnthropy Team """ # Actual email sending would go here log(f"📤 Sent partnership pitch to {brand['email']}") return True except Exception as e: err(f"❌ Brand pitch failed: {e}") return False def generate_performance_report(self) -> dict: """Generate performance metrics for pitching""" # In a real implementation, this would query the database return { "avg_views": "250K", "month_views": "7.5M", "engagement": "8.2%", "top_meme": "When the dogecoin moon mission gets delayed" }class DogNodeOnboarding: """Onboards other dog owners to the network""" def __init__(self): self.rev_share = 0.20 # 20% revenue share self.node_queue = queue.Queue() def add_node(self, owner: str, camera_url: str): """Add new dog node to the network""" self.node_queue.put((owner, camera_url)) log(f"🐶 New dog node added: {owner}") def process_nodes(self): """Process new node onboarding""" while not self.node_queue.empty(): owner, camera_url = self.node_queue.get() self.configure_node(owner, camera_url) def configure_node(self, owner: str, camera_url: str): """Configure new surveillance node""" # Add to camera list new_camera = { "id": f"node_{owner[:5]}", "rtsp": camera_url } RING_CAMERAS.append(new_camera) # Restart surveillance with new camera surveillance_engine.cameras = {} surveillance_engine.initialize_cameras() log(f"📹 Added new node camera: {new_camera['id']}")class TipInjector: """Adds crypto tipping to memes""" def __init__(self): self.tip_addresses = { "eth": "0xLolanthropyETH", "sol": "LolanthropySOLaddress", "ltn": "LolanthropyLTNaddress" } def add_tip_overlay(self, meme: GeneratedMeme) -> GeneratedMeme: """Add QR tip overlay to meme""" try: img = Image.open(meme.image_path) draw = ImageDraw.Draw(img) # Generate QR code qr_img = self.generate_qr_code() # Position QR in bottom right qr_size = 100 img.paste(qr_img, (img.width - qr_size - 10, img.height - qr_size - 10)) # Add tip text draw.text((img.width - qr_size - 10, img.height - qr_size - 30), "Tip Crypto", fill="white", font=meme_forge.fonts["impact_small"]) # Save new version tip_path = meme.image_path.replace(".jpg", "_tip.jpg") img.save(tip_path) # Create enhanced meme object return GeneratedMeme( event_id=meme.event_id, template_id=meme.template_id, caption=f"{meme.caption}\n\n💸 Support more dog memes!", image_path=tip_path, video_path=meme.video_path, hashtags=meme.hashtags + ["#crypto", "#tipping"], target_platforms=meme.target_platforms, engagement_prediction=meme.engagement_prediction * 1.1, # Boost from tipping profit_potential=meme.profit_potential, created_at=datetime.datetime.now().isoformat(), published=meme.published ) except Exception as e: err(f"❌ Tip overlay failed: {e}") return meme def generate_qr_code(self) -> Image: """Generate QR code for crypto tipping (simulated)""" # In real implementation, use qrcode library qr = Image.new('RGB', (100, 100), color='white') draw = ImageDraw.Draw(qr) draw.rectangle([20, 20, 80, 80], fill="black") return qrclass ReplayDaemon: """Replays viral memes with variations""" def __init__(self): self.replay_threshold = 50000 # 50k views self.replay_interval = 604800 # 1 week def check_for_replays(self): """Check database for memes eligible for replay""" with sqlite3.connect(DATABASE_PATH) as conn: cursor = conn.execute(""" SELECT * FROM generated_memes WHERE engagement_prediction > 0.7 AND created_at < datetime('now', '-7 days') """) for meme_data in cursor.fetchall(): self.replay_meme(meme_data) def replay_meme(self, meme_data: dict): """Create replay variation of meme""" try: # Create variation caption original_caption = meme_data["caption"] variation = self.create_variation(original_caption) # Create new meme version new_meme = GeneratedMeme( event_id=meme_data["event_id"] + "_replay", template_id=meme_data["template_id"], caption=variation, image_path=meme_data["image_path"], # Would create variation image in real implementation video_path=meme_data["video_path"], hashtags=meme_data["hashtags"], target_platforms=json.loads(meme_data["target_platforms"]), engagement_prediction=meme_data["engagement_prediction"] * 0.8, # Reduced expectation profit_potential=meme_data["profit_potential"] * 0.5, created_at=datetime.datetime.now().isoformat(), published={p: False for p in json.loads(meme_data["target_platforms"])} ) # Add to distribution queue db.insert_meme(new_meme) social_bot_army.queue_meme_for_distribution(new_meme) log(f"🔄 Replaying meme: {variation[:30]}...") except Exception as e: err(f"❌ Meme replay failed: {e}") def create_variation(self, caption: str) -> str: """Create variation of meme caption""" variations = [ f"Throwback to when my dog was trending: {caption}", f"Classic moment: {caption}", f"Still laughing at this: {caption}", f"#{np.random.choice(['TBT', 'FlashbackFriday'])}: {caption}" ] return np.random.choice(variations)# Initialize all income modulesad_revenue_daemon = AdRevenueDaemon()affiliate_injector = AffiliateInjector()trend_aligner = TrendAligner()cpm_optimizer = CPMOptimizer()sponsor_broker = SponsorSlotBroker()nft_tracker = NFTRevenueTracker()yield_router = StableYieldRouter()rent_nft_manager = RentNFTManager()sponsor_pitcher = SponsorAutoPitcher()dog_node_onboarding = DogNodeOnboarding()tip_injector = TipInjector()replay_daemon = ReplayDaemon()# === 🔄 INTEGRATION WITH EXISTING SYSTEM ===class EnhancedMemeForge(MemeForge): def generate_meme(self, event: DogEvent) -> Optional[GeneratedMeme]: meme = super().generate_meme(event) if meme: # Apply all enhancements meme = affiliate_injector.inject_link(meme) meme = trend_aligner.align_meme(meme) meme = sponsor_broker.inject_sponsorship(meme) meme = tip_injector.add_tip_overlay(meme) # Update platform selection based on CPM optimization meme.target_platforms = cpm_optimizer.select_platforms(meme) return memeclass EnhancedSocialBotArmy(SocialBotArmy): def _simulate_revenue(self, meme: GeneratedMeme, platform: str): # Base revenue simulation super()._simulate_revenue(meme, platform) # Ad revenue calculation simulated_views = int(meme.engagement_prediction * 100000) ad_revenue = ad_revenue_daemon.calculate_earnings(platform, simulated_views) if ad_revenue > 0: ad_transaction = YieldTransaction( meme_id=meme.event_id, platform=platform, revenue_source="ad_revenue", amount_usd=ad_revenue, amount_ltn=ad_revenue / 0.1, transaction_hash=None, timestamp=datetime.datetime.now().isoformat() ) db.record_yield(ad_transaction) yield_engine.process_yield_transaction(ad_transaction) # Request payout if threshold reached ad_revenue_daemon.request_payout(platform, ad_revenue)class EnhancedYieldEngine(YieldEngine): def process_yield_transaction(self, transaction: YieldTransaction): super().process_yield_transaction(transaction) # Distribute to stable yield vaults yield_router.distribute_yield(transaction.amount_usd) # Check for rent NFT milestones rent_nft_manager.check_rent_milestone(self.total_yield_usd) # Automatically pitch sponsors when crossing thresholds if int(self.total_yield_usd) % 500 == 0: sponsor_pitcher.pitch_brands()# Replace original components with enhanced versionsMemeForge = EnhancedMemeForgeSocialBotArmy = EnhancedSocialBotArmyYieldEngine = EnhancedYieldEngine# === 🕒 DAEMON SCHEDULER ===class IncomeDaemonScheduler: """Schedules and runs all income daemons""" def __init__(self): self.daemons = { "replay": threading.Thread(target=self.run_replay_daemon, daemon=True), "sponsor_processing": threading.Thread(target=self.run_sponsor_processing, daemon=True), "node_processing": threading.Thread(target=self.run_node_processing, daemon=True), "nft_tracking": threading.Thread(target=self.run_nft_tracking, daemon=True) } def start_all(self): """Start all income daemons""" for name, thread in self.daemons.items(): thread.start() log(f"🔄 Started {name} daemon") def run_replay_daemon(self): """Run replay daemon on schedule""" while True: replay_daemon.check_for_replays() time.sleep(86400) # Daily def run_sponsor_processing(self): """Process sponsor slots""" while True: sponsor_broker.process_sponsor_queue() time.sleep(3600) # Hourly def run_node_processing(self): """Process new dog nodes""" while True: dog_node_onboarding.process_nodes() time.sleep(300) # Every 5 minutes def run_nft_tracking(self): """Track NFT sales""" while True: # Get all NFTs from database with sqlite3.connect(DATABASE_PATH) as conn: cursor = conn.execute("SELECT id, nft_tx_hash FROM generated_memes WHERE nft_tx_hash IS NOT NULL") for row in cursor.fetchall(): nft_tracker.track_sales(row[1]) time.sleep(86400) # Daily# Start all daemons when system initializesdaemon_scheduler = IncomeDaemonScheduler()