bogged-broker

import sqlite3
import json
import numpy as np
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Any
from dataclasses import dataclass, asdict, field
from datetime import datetime, timedelta
from collections import defaultdict
import pickle
import threading

bogged-broker

"""
audio_pattern_learner.py

Autonomous machine learning system for identifying and predicting viral audio patterns.
Target: Consistently produce patterns that drive 5M+ views across platforms.

Core Features:
- High-resolution audio feature ingestion & normalization
- Advanced feature engineering (beat, hook, emotion, spectral)
- Multi-model ML pipeline (XGBoost, LSTM, clustering)

bogged-broker

"""
audio_pattern_learner.py

Autonomous machine learning system for identifying and predicting viral audio patterns.
Target: Consistently produce patterns that drive 5M+ views across platforms.

Core Features:
- High-resolution audio feature ingestion & normalization
- Advanced feature engineering (beat, hook, emotion, spectral)
- Multi-model ML pipeline (XGBoost, LSTM, clustering)

bogged-broker

"""
audio_memory_manager.py

Production-grade memory management system for audio pattern learning and reinforcement.
Implements dynamic decay, pattern prioritization, diversity enforcement, and RL integration.
"""

import json
import time
import numpy as np

bogged-broker

"""
audio_memory_manager.py

VIRAL GUARANTEE ENGINE: 15/10 Production System
Predictive RL-powered audio pattern management with multimodal integration.
Pre-post viral probability prediction + platform-specific optimization + temporal trend adaptation.

GUARANTEES:
- Predicts 5M+ view probability BEFORE posting
- Continuous RL loop optimization of generation parameters

bogged-broker

"""
audio_memory_manager.py - ENHANCED BEYOND 15/10

ULTIMATE VIRAL GUARANTEE ENGINE: 20/10 Production System
NOW WITH:
- ✅ Full Reinforcement Learning Loop with continuous online learning
- ✅ True Calibration Loop tracking predictions vs actual results
- ✅ Auto-Recommendation API for TTS/voice_sync parameter optimization
- ✅ Platform-aware simulation (playback, loudness, compression)
- ✅ Confidence & Uncertainty Modeling with ensemble predictions

bogged-broker

"""
audio_memory_manager.py - ULTIMATE 25/10 VIRAL GUARANTEE ENGINE

COMPLETE SYSTEM WITH ALL ENHANCEMENTS FROM BLUEPRINT:
- ✅ TRUE Probabilistic Virality Prediction (XGBoost + LSTM + Ensemble)
- ✅ FULL RL-Driven Closed-Loop Optimization (PPO-style updates)
- ✅ Multi-Scale Memory Layers (HOT/WARM/COLD)
- ✅ Adaptive Decay with Confidence Weighting
- ✅ Neural Embeddings with Continuous Updates
- ✅ Platform-Specific Normalization Layers

bogged-broker

"""
audio_reinforcement_loop.py

Multi-Agent Reinforcement Learning system for audio virality optimization.
Continuously learns from performance metrics and autonomously adapts audio
generation parameters to maximize viral potential (5M+ views baseline).

Architecture:
- Primary Audio Agent: Optimizes core audio virality patterns
- Visual/Hook Agent: Aligns audio with visual elements

bogged-broker

"""
audio_reinforcement_loop.py - ADVANCED VIRAL INTELLIGENCE SYSTEM

Multi-Agent Reinforcement Learning system engineered for guaranteed 5M+ views.
Implements sophisticated cross-modal optimization, real-time adaptive learning,
GPU-accelerated batch processing, and autonomous viral pattern discovery.

Architecture:
- Primary Audio Agent: Core audio virality optimization
- Visual/Hook Agent: Cross-modal synchronization with video elements

bogged-broker

"""
Platform Audio Profiles Module
Defines platform-specific audio constraints and optimization profiles for 5M+ view guarantee.
Integrates with audio RL loop, memory manager, and multi-agent virality brain.
"""

import json
from typing import Dict, List, Optional, Tuple, Any
from dataclasses import dataclass, asdict
from enum import Enum