updated playback aware
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@@ -1,3 +1,115 @@
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#currently a consecpt
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#!/bin/bash
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# ==============================================================================================
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# ================================== DECISION ENGINE ===========================================
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# ==============================================================================================
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# Central behavioral decision engine for the media ecosystem.
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#
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# This engine transforms recent user activity into weighted decision output used by
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# downstream automation systems such as Lidarr, Sonarr, Radarr, and future services.
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#
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# The engine is:
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# Time-aware — recent activity matters more than historical ownership
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# Behavior-driven — decisions are based on actual usage patterns
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# Family-aware — balances influence across active users dynamically
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# Domain-agnostic — policies can adapt behavior per media type
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# Constraint-based — no single user can dominate system-wide discovery
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#
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# ── CORE PHILOSOPHY ────────────────────────────────────────────────────────────────────────────
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#
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# This is NOT:
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# • a recommendation engine
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# • a media AI
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# • a centralized content manager
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#
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# This IS:
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# • a decision layer
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# • a weighting system
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# • a behavioral context engine
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#
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# Recent activity defines current context.
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#
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# Discovery influence is earned through participation, not ownership.
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# Users who actively engage with media contribute more strongly to discovery weighting,
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# while configurable caps prevent any single user from overwhelming the ecosystem.
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#
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# The result is a continuously adapting media environment that reflects the current
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# behavioral state of the household rather than static long-term bias.
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#
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# ── RESPONSIBILITIES ───────────────────────────────────────────────────────────────────────────
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#
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# • Ingest recent activity from media systems and APIs
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# • Normalize activity across users and domains
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# • Apply time-decay and recency weighting
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# • Enforce per-user influence caps
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# • Generate ranked discovery candidates
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# • Return structured decision output to consumer scripts
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#
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# ── NON-RESPONSIBILITIES ───────────────────────────────────────────────────────────────────────
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#
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# The decision engine NEVER:
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# • Executes downloads
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# • Calls arr APIs directly
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# • Starts/stops containers
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# • Performs filesystem operations
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# • Handles infrastructure orchestration
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#
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# Infrastructure execution belongs to:
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# • adapter scripts
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# • orchestrators
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# • common.sh shared runtime functions
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#
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# ── ARCHITECTURE ROLE ─────────────────────────────────────────────────────────────────────────
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#
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# Ecosystem Layers:
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#
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# Config Layer
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# master.conf + host configs
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# ↓
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# Runtime Layer
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# common.sh
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# ↓
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# Decision Layer
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# decision_engine.sh ← THIS FILE
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# ↓
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# Domain Adapters
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# lidarr_discovery.sh
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# sonarr_discovery.sh
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# radarr_discovery.sh
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# transcoding_manager.sh
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#
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# This separation keeps decision logic centralized while allowing execution systems
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# to evolve independently.
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#
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# ── FUTURE EXPANSION ──────────────────────────────────────────────────────────────────────────
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#
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# Planned consumers:
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# • Lidarr music discovery
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# • Sonarr TV discovery balancing
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# • Radarr movie discovery weighting
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# • Transcoding priority orchestration
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# • Queue scheduling systems
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# • Resource-aware automation policies
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#
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# Shared decision primitives may eventually include:
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# • Recency decay models
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# • User weighting models
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# • Fairness constraints
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# • Diversity scoring
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# • Load-sensitive prioritization
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#
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# ── DESIGN RULES ──────────────────────────────────────────────────────────────────────────────
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#
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# • Engines decide — adapters execute
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# • Policies tune behavior — engines apply logic
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# • Shared infrastructure belongs in common.sh
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# • Domain logic belongs in policy modules
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# • No infrastructure execution inside the engine
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#
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# ── VERSION ───────────────────────────────────────────────────────────────────────────────────
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# v1.0 — Initial decision engine architecture
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# ==============================================================================================
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import requests
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from collections import defaultdict
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from dataclasses import dataclass
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