#!/usr/bin/env python3 """Codette Web Server — Zero-Dependency Local AI Chat Pure Python stdlib HTTP server with SSE streaming. No Flask, no FastAPI, no npm, no node — just Python. Usage: python codette_server.py # Start on port 7860 python codette_server.py --port 8080 # Custom port python codette_server.py --no-browser # Don't auto-open browser Architecture: - http.server for static files + REST API - Server-Sent Events (SSE) for streaming responses - Threading for background model loading/inference - CodetteOrchestrator for routing + generation - CodetteSession for Cocoon-backed memory """ import os, sys, json, time, threading, queue, argparse, webbrowser, traceback, re from pathlib import Path from http.server import HTTPServer, SimpleHTTPRequestHandler from urllib.parse import urlparse, parse_qs from io import BytesIO # Auto-configure environment _site = r"J:\Lib\site-packages" if _site not in sys.path: sys.path.insert(0, _site) os.environ["PATH"] = r"J:\Lib\site-packages\Library\bin" + os.pathsep + os.environ.get("PATH", "") try: sys.stdout.reconfigure(encoding='utf-8', errors='replace') # Force unbuffered output so cmd window updates in real-time sys.stdout.reconfigure(line_buffering=True) except Exception: pass # Project imports _inference_dir = str(Path(__file__).parent) if _inference_dir not in sys.path: sys.path.insert(0, _inference_dir) from codette_session import ( CodetteSession, SessionStore, ADAPTER_COLORS, AGENT_NAMES ) # Lazy import orchestrator (heavy — loads llama_cpp) _orchestrator = None _orchestrator_lock = threading.Lock() _inference_semaphore = threading.Semaphore(1) # Limit to 1 concurrent inference (llama.cpp can't parallelize) _orchestrator_status = {"state": "idle", "message": "Not loaded"} _orchestrator_status_lock = threading.Lock() # Protect _orchestrator_status from race conditions _load_error = None # Phase 6 bridge (optional, wraps orchestrator) _forge_bridge = None _use_phase6 = True # ENABLED: Foundation restoration (memory kernel + stability field) wrapped in ForgeEngine + Phase 7 routing # Current session _session: CodetteSession = None _session_store: SessionStore = None _session_lock = threading.Lock() # Identity persistence (Challenge 3: user recognition & relationship continuity) _identity_anchor = None try: from identity_anchor import IdentityAnchor _identity_anchor = IdentityAnchor() print(f" Identity anchor loaded ({len(_identity_anchor.identities)} known identities)") except Exception as e: print(f" Identity anchor unavailable: {e}") # Behavior Governor (Executive Controller v2) _behavior_governor = None try: from reasoning_forge.behavior_governor import BehaviorGovernor _behavior_governor = BehaviorGovernor(identity_anchor=_identity_anchor) print(" Behavior Governor loaded (identity + memory + cognitive load governance)") except Exception as e: print(f" Behavior Governor unavailable: {e}") # Unified Memory (SQLite + FTS5 — replaces CognitionCocooner for recall) _unified_memory = None try: from reasoning_forge.unified_memory import UnifiedMemory _unified_memory = UnifiedMemory() print(f" Unified Memory loaded ({_unified_memory._total_stored} cocoons, FTS5 active)") except Exception as e: print(f" Unified Memory unavailable (falling back to CognitionCocooner): {e}") # Request queue for thread-safe model access _request_queue = queue.Queue() _response_queues = {} # request_id -> queue.Queue _response_queues_lock = threading.Lock() # Protect _response_queues from race conditions _queue_creation_times = {} # Track when each queue was created for cleanup # Worker threads for health monitoring _worker_threads = [] _worker_threads_lock = threading.Lock() def _get_orchestrator(): """Lazy-load the orchestrator (first call takes ~60s).""" global _orchestrator, _orchestrator_status, _load_error, _forge_bridge if _orchestrator is not None: return _orchestrator with _orchestrator_lock: if _orchestrator is not None: return _orchestrator with _orchestrator_status_lock: _orchestrator_status.update({"state": "loading", "message": "Loading Codette model..."}) print("\n Loading CodetteOrchestrator...") try: from codette_orchestrator import CodetteOrchestrator # Challenge 2 fix: use 32768 context (model trained on 131072, # 32k is a safe balance of capability vs VRAM on consumer GPU) _orchestrator = CodetteOrchestrator( verbose=True, n_ctx=32768, ) with _orchestrator_status_lock: _orchestrator_status.update({ "state": "ready", "message": f"Ready — {len(_orchestrator.available_adapters)} adapters", "adapters": _orchestrator.available_adapters, }) print(f" Orchestrator ready: {_orchestrator.available_adapters}") # Initialize Phase 6 bridge with Phase 7 routing (wraps orchestrator with ForgeEngine + Executive Controller) print(f" [DEBUG] _use_phase6 = {_use_phase6}") if _use_phase6: try: print(f" [DEBUG] Importing CodetteForgeBridge...") from codette_forge_bridge import CodetteForgeBridge print(f" [DEBUG] Creating bridge instance...") _forge_bridge = CodetteForgeBridge(_orchestrator, use_phase6=True, use_phase7=True, verbose=True, health_check_fn=_run_health_check) print(f" Phase 6 bridge initialized") print(f" Phase 7 Executive Controller initialized") # Add memory count from forge kernel mem_count = 0 if hasattr(_forge_bridge, 'forge') and hasattr(_forge_bridge.forge, 'memory_kernel') and _forge_bridge.forge.memory_kernel: mem_count = len(_forge_bridge.forge.memory_kernel) with _orchestrator_status_lock: _orchestrator_status.update({"phase6": "enabled", "phase7": "enabled", "memory_count": mem_count}) except Exception as e: print(f" Phase 6/7 bridge failed (using lightweight routing): {e}") traceback.print_exc() with _orchestrator_status_lock: _orchestrator_status.update({"phase6": "disabled", "phase7": "disabled"}) else: print(f" [DEBUG] Phase 6 disabled (_use_phase6=False)") return _orchestrator except Exception as e: _load_error = str(e) with _orchestrator_status_lock: _orchestrator_status.update({"state": "error", "message": f"Load failed: {e}"}) print(f" ERROR loading orchestrator: {e}") traceback.print_exc() return None def _cleanup_orphaned_queues(): """Periodically clean up response queues that are older than 5 minutes. This prevents memory leaks from accumulating abandoned request queues. """ while True: try: time.sleep(60) # Run cleanup every 60 seconds now = time.time() with _response_queues_lock: # Find queues older than 5 minutes (300 seconds) orphaned = [] for req_id, creation_time in list(_queue_creation_times.items()): if now - creation_time > 300: orphaned.append(req_id) # Remove orphaned queues for req_id in orphaned: _response_queues.pop(req_id, None) _queue_creation_times.pop(req_id, None) if orphaned: print(f" Cleaned up {len(orphaned)} orphaned response queues") except Exception as e: print(f" WARNING: Cleanup thread error: {e}") def _monitor_worker_health(): """Monitor worker threads and restart any that have died. This ensures the system remains responsive even if a worker crashes. """ while True: try: time.sleep(5) # Check every 5 seconds with _worker_threads_lock: # Check each worker thread alive_workers = [] dead_workers = [] for i, worker in enumerate(_worker_threads): if worker.is_alive(): alive_workers.append((i, worker)) else: dead_workers.append(i) # Log and restart any dead workers if dead_workers: print(f" WARNING: Detected {len(dead_workers)} dead worker(s): {dead_workers}") for i in dead_workers: print(f" Restarting worker thread {i}...") new_worker = threading.Thread(target=_worker_thread, daemon=True, name=f"worker-{i}") new_worker.start() _worker_threads[i] = new_worker print(f" Worker threads restarted successfully") # Log current work queue status periodically work_queue_size = _request_queue.qsize() if work_queue_size > 0: print(f" Worker status: {len(alive_workers)} alive, {len(_response_queues)} pending requests, {work_queue_size} queued") except Exception as e: print(f" WARNING: Worker health monitor error: {e}") def _run_health_check(): """Run a real self-diagnostic across all Codette subsystems. Returns actual system state — not generated text about health, but measured values from every component. """ report = { "timestamp": time.time(), "overall": "unknown", "systems": {}, "warnings": [], "errors": [], } checks_passed = 0 checks_total = 0 # 1. Model / Orchestrator checks_total += 1 if _orchestrator: report["systems"]["model"] = { "status": "OK", "adapters_loaded": len(getattr(_orchestrator, 'available_adapters', [])), "adapters": getattr(_orchestrator, 'available_adapters', []), "base_model": "Meta-Llama-3.1-8B-Instruct-Q4_K_M", } checks_passed += 1 else: report["systems"]["model"] = {"status": "NOT LOADED"} report["errors"].append("Model not loaded") # 2. Phase 6 / ForgeEngine checks_total += 1 if _forge_bridge and _forge_bridge.use_phase6: forge = _forge_bridge.forge p6 = {"status": "OK", "components": {}} # Memory kernel if hasattr(forge, 'memory_kernel') and forge.memory_kernel: mem_count = len(forge.memory_kernel) p6["components"]["memory_kernel"] = {"status": "OK", "memories": mem_count} else: p6["components"]["memory_kernel"] = {"status": "MISSING"} report["warnings"].append("Memory kernel not initialized") # Stability field # Check both possible attribute names stability = getattr(forge, 'cocoon_stability', None) or getattr(forge, 'stability_field', None) if stability: p6["components"]["stability_field"] = {"status": "OK", "type": type(stability).__name__} else: p6["components"]["stability_field"] = {"status": "MISSING"} # Colleen conscience if hasattr(forge, 'colleen') and forge.colleen: p6["components"]["colleen_conscience"] = {"status": "OK"} else: p6["components"]["colleen_conscience"] = {"status": "MISSING"} report["warnings"].append("Colleen conscience not loaded") # Guardian spindle if hasattr(forge, 'guardian') and forge.guardian: p6["components"]["guardian_spindle"] = {"status": "OK"} else: p6["components"]["guardian_spindle"] = {"status": "MISSING"} # Ethical governance if hasattr(forge, 'ethical_governance') and forge.ethical_governance: eg = forge.ethical_governance audit_count = len(getattr(eg, 'audit_log', [])) queries_blocked = sum(1 for entry in getattr(eg, 'audit_log', []) if entry.get('action') == 'blocked') p6["components"]["ethical_governance"] = { "status": "OK", "audit_entries": audit_count, "queries_blocked": queries_blocked, "detection_rules": len(getattr(eg, 'harmful_patterns', [])) + len(getattr(eg, 'bias_patterns', [])), } else: p6["components"]["ethical_governance"] = {"status": "MISSING"} report["warnings"].append("Ethical governance not loaded") # CognitionCocooner if hasattr(forge, 'cocooner') and forge.cocooner: cocoon_count = len(getattr(forge.cocooner, 'cocoons', {})) p6["components"]["cognition_cocooner"] = { "status": "OK", "stored_cocoons": cocoon_count, } else: p6["components"]["cognition_cocooner"] = {"status": "MISSING"} # Self-awareness (tier2 bridge) if hasattr(forge, 'tier2_bridge') and forge.tier2_bridge: p6["components"]["tier2_bridge"] = {"status": "OK"} else: p6["components"]["tier2_bridge"] = {"status": "MISSING"} report["systems"]["phase6_forge"] = p6 checks_passed += 1 else: report["systems"]["phase6_forge"] = {"status": "DISABLED"} report["warnings"].append("Phase 6 ForgeEngine not active") # 3. Phase 7 / Executive Controller checks_total += 1 if _forge_bridge and _forge_bridge.use_phase7 and _forge_bridge.executive_controller: report["systems"]["phase7_executive"] = {"status": "OK"} checks_passed += 1 else: report["systems"]["phase7_executive"] = {"status": "DISABLED"} report["warnings"].append("Phase 7 Executive Controller not active") # 4. Session / Cocoon subsystems checks_total += 1 if _session: try: sess = { "status": "OK", "session_id": getattr(_session, 'session_id', 'unknown'), "message_count": len(getattr(_session, 'messages', [])), "subsystems": {}, } sub_names = [ ("spiderweb", "QuantumSpiderweb"), ("metrics_engine", "EpistemicMetrics"), ("cocoon_sync", "CocoonSync"), ("dream_reweaver", "DreamReweaver"), ("optimizer", "QuantumOptimizer"), ("memory_kernel", "LivingMemory"), ("guardian", "CodetteGuardian"), ("resonance_engine", "ResonantContinuity"), ("aegis", "AEGIS"), ("nexus", "NexusSignalEngine"), ] for attr, label in sub_names: obj = getattr(_session, attr, None) sess["subsystems"][label] = "OK" if obj else "MISSING" # Spiderweb metrics (safely) sw = getattr(_session, 'spiderweb', None) if sw: try: sess["spiderweb_metrics"] = { "phase_coherence": sw.phase_coherence() if hasattr(sw, 'phase_coherence') else 0, "entropy": sw.shannon_entropy() if hasattr(sw, 'shannon_entropy') else 0, "decoherence_rate": sw.decoherence_rate() if hasattr(sw, 'decoherence_rate') else 0, "node_count": len(getattr(sw, 'nodes', [])), "attractor_count": len(getattr(_session, 'attractors', [])), "glyph_count": len(getattr(_session, 'glyphs', [])), } except Exception: sess["spiderweb_metrics"] = {"error": "failed to read"} # Coherence/tension history (safely) ch = getattr(_session, 'coherence_history', []) th = getattr(_session, 'tension_history', []) sess["coherence_entries"] = len(ch) sess["tension_entries"] = len(th) sess["current_coherence"] = ch[-1] if ch else None sess["current_tension"] = th[-1] if th else None sess["perspective_usage"] = dict(getattr(_session, 'perspective_usage', {})) report["systems"]["session"] = sess checks_passed += 1 except Exception as e: report["systems"]["session"] = {"status": "ERROR", "detail": str(e)} report["warnings"].append(f"Session check failed: {e}") else: report["systems"]["session"] = {"status": "NOT INITIALIZED"} report["errors"].append("No active session") # 5. Self-correction system checks_total += 1 try: from self_correction import BehaviorMemory # noqa bm = BehaviorMemory() report["systems"]["self_correction"] = { "status": "OK", "behavior_lessons": len(getattr(bm, 'lessons', [])), "permanent_locks": 4, } checks_passed += 1 except ImportError: report["systems"]["self_correction"] = {"status": "NOT AVAILABLE"} report["warnings"].append("Self-correction module not importable") # 6. Worker threads checks_total += 1 with _worker_threads_lock: alive = sum(1 for w in _worker_threads if w.is_alive()) total = len(_worker_threads) report["systems"]["worker_threads"] = { "status": "OK" if alive == total else "DEGRADED", "alive": alive, "total": total, "pending_requests": _request_queue.qsize(), } if alive == total: checks_passed += 1 else: report["warnings"].append(f"{total - alive} worker thread(s) dead") # 7. Inference semaphore checks_total += 1 # _value is internal but useful for diagnostics sem_available = getattr(_inference_semaphore, '_value', 1) report["systems"]["inference_lock"] = { "status": "OK" if sem_available > 0 else "BUSY", "available": sem_available > 0, } checks_passed += 1 # 8. Substrate awareness (real-time system pressure) checks_total += 1 if _forge_bridge and hasattr(_forge_bridge, 'substrate_monitor') and _forge_bridge.substrate_monitor: try: substrate = _forge_bridge.substrate_monitor.snapshot() report["systems"]["substrate"] = { "status": "OK", "pressure": substrate["pressure"], "level": substrate["level"], "memory_pct": substrate["memory_pct"], "memory_available_gb": substrate["memory_available_gb"], "cpu_pct": substrate["cpu_pct"], "process_memory_gb": substrate["process_memory_gb"], "inference_avg_ms": substrate["inference_avg_ms"], "trend": _forge_bridge.substrate_monitor.trend(), "adapter_health": _forge_bridge.substrate_monitor.get_adapter_health(), } checks_passed += 1 except Exception as e: report["systems"]["substrate"] = {"status": "ERROR", "detail": str(e)} report["warnings"].append(f"Substrate monitor error: {e}") else: report["systems"]["substrate"] = {"status": "NOT AVAILABLE"} report["warnings"].append("Substrate-aware cognition not initialized") # 9. Cocoon Introspection (memory pattern analysis) checks_total += 1 try: from cocoon_introspection import CocoonIntrospectionEngine intro_engine = CocoonIntrospectionEngine() dom = intro_engine.adapter_dominance() report["systems"]["introspection"] = { "status": "OK", "reasoning_cocoons": dom.get("total_responses", 0), "dominant_adapter": dom.get("dominant"), "dominance_ratio": dom.get("ratio", 0), "balanced": dom.get("balanced", True), } checks_passed += 1 except Exception as e: report["systems"]["introspection"] = {"status": "ERROR", "detail": str(e)} report["warnings"].append(f"Cocoon introspection error: {e}") # Overall grade if checks_passed == checks_total and not report["errors"]: report["overall"] = "HEALTHY" elif report["errors"]: report["overall"] = "CRITICAL" elif checks_passed >= checks_total - 1: report["overall"] = "GOOD" else: report["overall"] = "DEGRADED" report["checks_passed"] = checks_passed report["checks_total"] = checks_total report["score"] = f"{checks_passed}/{checks_total}" return report def _worker_thread(): """Background worker that processes inference requests.""" # NOTE: Session handling disabled for now due to scoping issues # TODO: Refactor session management to avoid UnboundLocalError while True: try: request = _request_queue.get(timeout=1.0) except queue.Empty: continue if request is None: break # Shutdown signal req_id = request["id"] # Get response queue with thread lock (prevent race condition) with _response_queues_lock: response_q = _response_queues.get(req_id) if not response_q: print(f" WARNING: Orphaned request {req_id} (response queue missing)") continue try: orch = _get_orchestrator() if orch is None: try: response_q.put({"error": _load_error or "Model failed to load"}) except (queue.Full, RuntimeError) as e: print(f" ERROR: Failed to queue error response: {e}") continue query = request["query"] query_lower = query.lower().strip() adapter = request.get("adapter") # None = auto-route max_adapters = request.get("max_adapters", 2) # ── SELF-INTROSPECTION INTERCEPT ── # When user asks about self-reflection, patterns, or what she's noticed, # run real cocoon analysis instead of LLM-generated text about reflection _introspection_triggers = [ "what have you noticed about yourself", "what patterns do you see", "self-reflection", "self reflection", "introspect", "introspection", "what have you learned about yourself", "analyze your own", "analyze your patterns", "cocoon analysis", "cocoon patterns", "adapter frequency", "adapter dominance", "your own history", "your reasoning history", "what do you notice about yourself", "tell me about your patterns", "how have you changed", "how have you evolved", "your emotional patterns", "your response patterns", ] if any(trigger in query_lower for trigger in _introspection_triggers): print(f" [WORKER] Intercepted introspection query — running real cocoon analysis", flush=True) try: response_q.put({"event": "thinking", "adapter": "introspection"}) except (queue.Full, RuntimeError): pass try: from cocoon_introspection import CocoonIntrospectionEngine engine = CocoonIntrospectionEngine() report = engine.format_introspection() except Exception as e: report = f"**Introspection Error** — Could not analyze cocoon history: {e}" try: response_q.put({ "event": "complete", "response": report, "adapter": "introspection", "confidence": 1.0, "reasoning": "Real cocoon analysis — not generated text", "tokens": 0, "time": 0.01, "complexity": "SYSTEM", "domain": "introspection", "ethical_checks": 0, }) except (queue.Full, RuntimeError): pass continue # ── SELF-DIAGNOSTIC INTERCEPT ── # When user asks for a health/system check, run the REAL diagnostic # instead of letting the model generate text about it _health_triggers = [ "health check", "system health", "self diagnostic", "self-diagnostic", "systems check", "system check", "self check", "self-check", "run diagnostic", "diagnostics", "check yourself", "check your systems", "how are your systems", "are you healthy", "status check", "self systems health", "system status", ] if any(trigger in query_lower for trigger in _health_triggers): print(f" [WORKER] Intercepted health check query — running real diagnostic", flush=True) # Must send thinking event first (POST handler expects it) try: response_q.put({"event": "thinking", "adapter": "self_diagnostic"}) except (queue.Full, RuntimeError): pass try: health = _run_health_check() except Exception as e: health = {"overall": "ERROR", "score": "0/0", "systems": {}, "warnings": [], "errors": [str(e)]} # Format the real data into a readable response lines = [] lines.append(f"**Self-Diagnostic Report** — Overall: **{health['overall']}** ({health['score']} checks passed)\n") for sys_name, sys_data in health.get("systems", {}).items(): status = sys_data.get("status", "?") icon = "+" if status in ("OK", "HEALTHY") else ("-" if status == "MISSING" else "!") nice_name = sys_name.replace("_", " ").title() lines.append(f"[{icon}] **{nice_name}**: {status}") # Show key details per subsystem if sys_name == "model": lines.append(f" Adapters loaded: {sys_data.get('adapters_loaded', '?')}") elif sys_name == "phase6_forge": for comp_name, comp_data in sys_data.get("components", {}).items(): comp_status = comp_data if isinstance(comp_data, str) else comp_data.get("status", "?") comp_nice = comp_name.replace("_", " ").title() detail_parts = [] if isinstance(comp_data, dict): for k, v in comp_data.items(): if k != "status": detail_parts.append(f"{k}={v}") detail = f" ({', '.join(detail_parts)})" if detail_parts else "" lines.append(f" {comp_nice}: {comp_status}{detail}") elif sys_name == "session": lines.append(f" Messages: {sys_data.get('message_count', 0)}") lines.append(f" Coherence entries: {sys_data.get('coherence_entries', 0)}") lines.append(f" Tension entries: {sys_data.get('tension_entries', 0)}") if "spiderweb_metrics" in sys_data: sw = sys_data["spiderweb_metrics"] lines.append(f" Spiderweb: coherence={sw.get('phase_coherence', 0):.4f}, entropy={sw.get('entropy', 0):.4f}, nodes={sw.get('node_count', 0)}, attractors={sw.get('attractor_count', 0)}") if sys_data.get("perspective_usage"): usage = sys_data["perspective_usage"] lines.append(f" Perspective usage: {dict(usage)}") for sub_name, sub_status in sys_data.get("subsystems", {}).items(): sub_icon = "+" if sub_status == "OK" else "-" lines.append(f" [{sub_icon}] {sub_name}: {sub_status}") elif sys_name == "self_correction": lines.append(f" Behavior lessons: {sys_data.get('behavior_lessons', 0)}") lines.append(f" Permanent locks: {sys_data.get('permanent_locks', 0)}") elif sys_name == "worker_threads": lines.append(f" Alive: {sys_data.get('alive', 0)}/{sys_data.get('total', 0)}") lines.append(f" Pending requests: {sys_data.get('pending_requests', 0)}") elif sys_name == "substrate": lines.append(f" Pressure: {sys_data.get('pressure', 0):.3f} ({sys_data.get('level', '?')})") lines.append(f" Memory: {sys_data.get('memory_pct', 0)}% used, {sys_data.get('memory_available_gb', 0)}GB available") lines.append(f" Process: {sys_data.get('process_memory_gb', 0)}GB RSS") lines.append(f" CPU: {sys_data.get('cpu_pct', 0)}%") lines.append(f" Inference avg: {sys_data.get('inference_avg_ms', 0):.0f}ms") lines.append(f" Trend: {sys_data.get('trend', '?')}") ah = sys_data.get('adapter_health', {}) if ah: lines.append(f" Adapter health: {ah}") elif sys_name == "introspection": lines.append(f" Reasoning cocoons: {sys_data.get('reasoning_cocoons', 0)}") lines.append(f" Dominant adapter: {sys_data.get('dominant_adapter', 'none')}") lines.append(f" Dominance ratio: {sys_data.get('dominance_ratio', 0):.1%}") lines.append(f" Balanced: {'Yes' if sys_data.get('balanced', True) else 'No — may be over-relying'}") if health.get("warnings"): lines.append(f"\nWarnings: {', '.join(health['warnings'])}") if health.get("errors"): lines.append(f"\nErrors: {', '.join(health['errors'])}") diag_response = "\n".join(lines) try: response_q.put({ "event": "complete", "response": diag_response, "adapter": "self_diagnostic", "confidence": 1.0, "reasoning": "Real self-diagnostic — not generated text", "tokens": 0, "time": 0.01, "complexity": "SYSTEM", "domain": "self_diagnostic", "ethical_checks": 0, "memory_count": health.get("systems", {}).get("phase6_forge", {}).get("components", {}).get("cognition_cocooner", {}).get("stored_cocoons", 0), }) except (queue.Full, RuntimeError): pass continue # ── ARTIST QUERY INTERCEPT (hallucination prevention) ── # Detect if user is asking about specific artists/songs/albums # Route to uncertainty guidance instead of risking hallucination _artist_patterns = [ r'\b(who is|tell me about|what do you know about|who are)\s+([a-z\s\'-]+)\?', r'\b(album|discography|career|songs? by|music by)\s+([a-z\s\'-]+)', r'\b([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\s+(album|song|band|artist)', r'\b(is [a-z\s\'-]+ (indie-rock|country|hip-hop|rock|pop|electronic))', ] is_artist_query = any(re.search(pattern, query_lower, re.IGNORECASE) for pattern in _artist_patterns) if is_artist_query: print(f" [WORKER] Intercepted artist query — routing to uncertainty response", flush=True) artist_response = ( "I don't have reliable information about specific artists in my training data. Rather than guess or hallucinate details, I'd recommend checking:\n\n" "- **Spotify** — artist bio, discography, listening stats\n" "- **Wikipedia** — career history, notable works\n" "- **Bandcamp** — independent artists, recent releases\n" "- **Official websites** — accurate info straight from the source\n\n" "**What I CAN help with instead:**\n" "- Music production techniques for their genre/style\n" "- Music theory and arrangement analysis\n" "- Creating music inspired by similar vibes\n" "- Sound design for that aesthetic\n\n" "If you describe their music or share a link, I can help you create inspired work or understand the production choices." ) try: response_q.put({"event": "thinking", "adapter": "uncertainty_aware"}) except (queue.Full, RuntimeError): pass try: response_q.put({ "event": "complete", "response": artist_response, "adapter": "uncertainty_aware", "confidence": 1.0, "reasoning": "Honest uncertainty > hallucination. User can verify via authoritative sources.", "tokens": 0, "time": 0.01, "complexity": "SIMPLE", "domain": "music", "ethical_checks": 1, }) except (queue.Full, RuntimeError): pass continue # Send "thinking" event try: response_q.put({"event": "thinking", "adapter": adapter or "auto"}) except (queue.Full, RuntimeError): continue # Route and generate — limit to 1 concurrent inference to avoid memory exhaustion # Add timeout to prevent deadlock if inference gets stuck acquired = _inference_semaphore.acquire(timeout=120) if not acquired: try: response_q.put({"error": "Inference queue full, request timed out after 2 minutes"}) except (queue.Full, RuntimeError): pass continue try: print(f" [WORKER] Processing query: {query[:60]}...", flush=True) # ── Identity Recognition ── # Recognize WHO is talking and inject relationship context identity_context = "" recognized_user = None if _identity_anchor: try: recognized_user = _identity_anchor.recognize(query) if recognized_user: identity_context = _identity_anchor.get_identity_context(recognized_user) # NOTE: identity info is NEVER logged or returned in API responses print(f" [WORKER] Identity: recognized (context injected)", flush=True) except Exception as e: print(f" [WORKER] Identity recognition skipped: {e}", flush=True) # ── Behavior Governor Pre-Evaluation ── # Determines memory budget, identity budget, response length governor_decision = None identity_confidence = 0.0 if recognized_user and _identity_anchor and recognized_user in _identity_anchor.identities: identity_confidence = _identity_anchor.identities[recognized_user].recognition_confidence substrate_pressure = 0.0 try: from inference.substrate_awareness import SubstrateMonitor sm = SubstrateMonitor() substrate_pressure = sm.get_pressure() except Exception: pass if _behavior_governor: try: # Classify query for governor (lightweight) from codette_forge_bridge import QueryClassifier, QueryComplexity qc = QueryClassifier() complexity = qc.classify(query) classification = { "complexity": complexity.name if hasattr(complexity, 'name') else str(complexity), "domain": "general", } governor_decision = _behavior_governor.pre_evaluate( query, classification, identity_confidence=identity_confidence, substrate_pressure=substrate_pressure, ) print(f" [GOVERNOR] {governor_decision.reasoning}", flush=True) # Apply governor's identity budget if governor_decision.identity_budget == "none": identity_context = "" # Governor says no identity except Exception as e: print(f" [GOVERNOR] Pre-eval skipped: {e}", flush=True) # ── Memory Enrichment ── # Recall relevant cocoons — budget controlled by governor memory_budget = 3 if governor_decision: memory_budget = governor_decision.memory_budget enriched_query = query try: # Use UnifiedMemory (SQLite + FTS5) when available, # fall back to CognitionCocooner (JSON scan) relevant_cocoons = [] if _unified_memory: relevant_cocoons = _unified_memory.recall_relevant(query, max_results=memory_budget) recall_source = "unified_memory" else: from reasoning_forge.cognition_cocooner import CognitionCocooner cocooner = CognitionCocooner(storage_path="cocoons") relevant_cocoons = cocooner.recall_relevant(query, max_results=memory_budget) recall_source = "cocooner" if relevant_cocoons: memory_lines = [] for cocoon in relevant_cocoons: q = cocoon.get("query", "")[:100] r = cocoon.get("response", "")[:200] if q and r: memory_lines.append(f"- Q: {q}\n A: {r}") if memory_lines: enriched_query = ( query + "\n\n---\n" "# YOUR PAST REASONING (relevant memories)\n" "You previously responded to similar questions:\n" + "\n".join(memory_lines) + "\n---\n" "Use these memories for consistency. Build on past insights when relevant." ) print(f" [WORKER] Injected {len(memory_lines)} memories ({recall_source})", flush=True) except Exception as e: print(f" [WORKER] Memory recall skipped: {e}", flush=True) # ── Identity Context Injection ── # Append identity context AFTER memory context # This goes into the prompt so Codette knows WHO she's talking to # Privacy: identity_context is NEVER returned in API responses if identity_context: enriched_query = ( enriched_query + "\n\n---\n" + identity_context + "\n---" ) if _forge_bridge: print(f" [WORKER] Using forge bridge (Phase 6/7)", flush=True) gov_mem_budget = governor_decision.memory_budget if governor_decision else 3 gov_max_tokens = governor_decision.max_response_tokens if governor_decision else 512 result = _forge_bridge.generate( enriched_query, adapter=adapter, max_adapters=max_adapters, memory_budget=gov_mem_budget, max_response_tokens=gov_max_tokens, ) else: print(f" [WORKER] Using direct orchestrator", flush=True) result = orch.route_and_generate( enriched_query, max_adapters=max_adapters, strategy="keyword", force_adapter=adapter if adapter and adapter != "auto" else None, ) print(f" [WORKER] Got result: response={len(result.get('response',''))} chars, adapter={result.get('adapter','?')}", flush=True) # ── Post-generation Hallucination Check ── response_text = result.get("response", "") hallucination_alerts = [] # Check for artist/discography hallucinations artist_patterns = [ (r'(passed away|died|deceased).*?(19|20)\d{2}', "unverified artist death claim"), (r'(the album|released).*?["\'](\w+[\w\s]*?)["\'].*?(19|20)\d{2}', "unverified album/date claim"), ] for pattern, alert_type in artist_patterns: if re.search(pattern, response_text, re.IGNORECASE): for artist in ["laney wilson", "megan moroney", "tyler childers"]: if artist in response_text.lower(): hallucination_alerts.append(f"[HALLUCINATION] {alert_type} for {artist}") break # If hallucinations detected, add self-correction if hallucination_alerts and is_artist_query: correction = ( "\n\n---\n" "[Self-Correction]\n" "I just realized I made some unverified claims above. Rather than guess, " "I should be honest: I don't have reliable biographical details about this artist. " "For accurate information, check Wikipedia, Spotify, or their official website. " "I'm better at helping with production techniques, music theory, and sound design.\n" ) result["response"] = response_text + correction result["hallucination_detected"] = True result["hallucination_alerts"] = hallucination_alerts for alert in hallucination_alerts: print(f" {alert}", flush=True) else: result["hallucination_detected"] = False if _behavior_governor and governor_decision: try: validation = _behavior_governor.post_validate( query, result.get("response", ""), governor_decision ) if validation.get("warnings"): for w in validation["warnings"]: print(f" [GOVERNOR] {w}", flush=True) if "identity_leak" in validation.get("corrections", []): print(f" [GOVERNOR] Identity leak detected in response", flush=True) except Exception: pass # Update session with response data (drives cocoon metrics UI) epistemic = None with _session_lock: session = _session # grab reference under lock if session: try: # Add user message + assistant response to session history session.add_message("user", query) session.add_message("assistant", result.get("response", ""), metadata={ "adapter": result.get("adapter", "base"), "tokens": result.get("tokens", 0), }) # Update cocoon state (spiderweb, coherence, attractors, glyphs, etc.) adapter_name = result.get("adapter", "base") if isinstance(adapter_name, list): adapter_name = adapter_name[0] if adapter_name else "base" route_obj = result.get("route") perspectives_dict = result.get("perspectives") session.update_after_response( route_obj, adapter_name, perspectives=perspectives_dict ) # Get epistemic report from session metrics if session.coherence_history or session.tension_history: epistemic = { "ensemble_coherence": session.coherence_history[-1] if session.coherence_history else 0, "tension_magnitude": session.tension_history[-1] if session.tension_history else 0, } # Add ethical alignment from AEGIS if available if hasattr(session, 'aegis') and session.aegis: try: aegis_state = session.aegis.get_state() if hasattr(session.aegis, 'get_state') else {} if aegis_state.get('eta') is not None: epistemic["ethical_alignment"] = aegis_state['eta'] except Exception: pass except Exception as e: print(f" [WORKER] Session update failed (non-critical): {e}", flush=True) # ── Store in Unified Memory ── # Every response goes to SQLite for future FTS5 recall if _unified_memory: try: adapter_for_store = result.get("adapter", "base") if isinstance(adapter_for_store, list): adapter_for_store = adapter_for_store[0] if adapter_for_store else "base" _unified_memory.store( query=query, response=result.get("response", ""), adapter=adapter_for_store, domain=result.get("domain", "general"), complexity=result.get("complexity", "MEDIUM"), ) except Exception: pass # ── Identity Update (post-interaction) ── # Update relationship state — trust grows, topics tracked # Privacy: only internal state updated, nothing exposed if _identity_anchor and recognized_user: try: adapter_name_for_id = result.get("adapter", "base") if isinstance(adapter_name_for_id, list): adapter_name_for_id = adapter_name_for_id[0] if adapter_name_for_id else "base" _identity_anchor.update_after_interaction( user_id=recognized_user, query=query, response=result.get("response", ""), adapter=adapter_name_for_id, ) except Exception: pass # Non-critical, never fail on identity # Extract route info from result (if available from ForgeEngine) route = result.get("route") perspectives = result.get("perspectives", []) # Build response response_text = result.get("response", "") if not response_text: print(f" [WORKER] WARNING: Empty response! Full result keys: {list(result.keys())}", flush=True) print(f" [WORKER] Result dump: { {k: str(v)[:100] for k,v in result.items()} }", flush=True) response_data = { "event": "complete", "response": response_text or "[No response generated — check server logs]", "adapter": result.get("adapter", result.get("adapters", ["base"])[0] if isinstance(result.get("adapters"), list) else "base"), "confidence": route.get("confidence", 0) if isinstance(route, dict) else (route.confidence if route else 0), "reasoning": route.get("reasoning", "") if isinstance(route, dict) else (route.reasoning if route else ""), "tokens": result.get("tokens", 0), "time": round(result.get("time", 0), 2), "multi_perspective": route.get("multi_perspective", False) if isinstance(route, dict) else (route.multi_perspective if route else False), } # Add Phase 6 metadata (complexity, domain, ethical) if result.get("complexity"): response_data["complexity"] = str(result["complexity"]) if result.get("domain"): response_data["domain"] = result["domain"] # Add ethical governance info ethical_checks = 0 if _forge_bridge and hasattr(_forge_bridge, 'forge'): fg = _forge_bridge.forge if hasattr(fg, 'ethical_governance') and fg.ethical_governance: ethical_checks = len(getattr(fg.ethical_governance, 'audit_log', [])) response_data["ethical_checks"] = ethical_checks # Add updated memory count from cocoon if _forge_bridge and hasattr(_forge_bridge, 'forge') and hasattr(_forge_bridge.forge, 'memory_kernel') and _forge_bridge.forge.memory_kernel: response_data["memory_count"] = len(_forge_bridge.forge.memory_kernel) # Add perspectives if available if perspectives: response_data["perspectives"] = perspectives # Cocoon state — send full session state for UI metrics panel with _session_lock: session = _session if session: try: session_state = session.get_state() response_data["cocoon"] = session_state except Exception as e: print(f" [WORKER] Session state serialization failed: {e}", flush=True) # Add epistemic report if available if epistemic: response_data["epistemic"] = epistemic # Add tool usage info if any tools were called tools_used = result.get("tools_used", []) if tools_used: response_data["tools_used"] = tools_used # RE-CHECK response queue still exists (handler may have cleaned it up if timeout fired) with _response_queues_lock: response_q_still_exists = req_id in _response_queues if response_q_still_exists: try: response_q.put(response_data) except (queue.Full, RuntimeError) as e: print(f" ERROR: Failed to queue response: {e}") else: print(f" WARNING: Response queue was cleaned up (handler timeout) - response dropped for {req_id}") except Exception as e: print(f" ERROR during inference: {e}") traceback.print_exc() # DEFENSIVE: RE-CHECK response queue before putting error with _response_queues_lock: response_q_still_exists = req_id in _response_queues if response_q_still_exists: try: response_q.put({"event": "error", "error": str(e)}) except (queue.Full, RuntimeError): print(f" ERROR: Also failed to queue error response") else: print(f" WARNING: Response queue was cleaned up (handler timeout) - error response dropped for {req_id}") finally: # Always release the semaphore _inference_semaphore.release() except Exception as e: print(f" ERROR in worker thread: {e}") traceback.print_exc() class CodetteHandler(SimpleHTTPRequestHandler): """Custom HTTP handler for Codette API + static files.""" # Serve static files from inference/static/ def __init__(self, *args, **kwargs): static_dir = str(Path(__file__).parent / "static") super().__init__(*args, directory=static_dir, **kwargs) def log_message(self, format, *args): """Quieter logging — skip static file requests.""" msg = format % args if not any(ext in msg for ext in [".css", ".js", ".ico", ".png", ".woff"]): print(f" [{time.strftime('%H:%M:%S')}] {msg}") def do_GET(self): parsed = urlparse(self.path) path = parsed.path # API routes if path == "/api/status": # Dynamically update memory count from forge kernel if _forge_bridge and hasattr(_forge_bridge, 'forge') and hasattr(_forge_bridge.forge, 'memory_kernel') and _forge_bridge.forge.memory_kernel: with _orchestrator_status_lock: _orchestrator_status["memory_count"] = len(_forge_bridge.forge.memory_kernel) self._json_response(_orchestrator_status) elif path == "/api/session": self._json_response(_session.get_state() if _session else {}) elif path == "/api/sessions": sessions = _session_store.list_sessions() if _session_store else [] self._json_response({"sessions": sessions}) elif path == "/api/adapters": self._json_response({ "colors": ADAPTER_COLORS, "agents": AGENT_NAMES, "available": _orchestrator.available_adapters if _orchestrator else [], }) elif path == "/api/health": try: self._json_response(_run_health_check()) except Exception as e: self._json_response({"overall": "ERROR", "detail": str(e)}) elif path == "/api/introspection": try: # Use unified memory if available, fall back to legacy if _unified_memory: self._json_response(_unified_memory.full_introspection()) else: from cocoon_introspection import CocoonIntrospectionEngine engine = CocoonIntrospectionEngine() self._json_response(engine.full_introspection()) except Exception as e: self._json_response({"error": str(e)}) elif path == "/api/governor": # Confidence dashboard — governor state, identity confidence, memory stats # NOTE: identity details are NEVER exposed (privacy) dashboard = {"governor": None, "memory": None, "identity_summary": None} if _behavior_governor: dashboard["governor"] = _behavior_governor.get_state() if _unified_memory: dashboard["memory"] = _unified_memory.get_stats() if _identity_anchor: # Safe summary: only counts and trust levels, no PII dashboard["identity_summary"] = { "known_identities": len(_identity_anchor.identities), "current_user_recognized": _identity_anchor.current_user is not None, # Confidence level only (not who) "current_confidence": ( _identity_anchor.identities[_identity_anchor.current_user].recognition_confidence if _identity_anchor.current_user and _identity_anchor.current_user in _identity_anchor.identities else 0.0 ), } self._json_response(dashboard) elif path == "/api/synthesize": # Meta-cognitive cocoon synthesis — discover patterns, forge strategies try: params = parse_qs(parsed.query) problem = params.get("problem", ["How should an AI decide when to change its own thinking patterns?"])[0] if _forge_bridge and hasattr(_forge_bridge, 'forge') and hasattr(_forge_bridge.forge, 'cocoon_synthesizer') and _forge_bridge.forge.cocoon_synthesizer: result = _forge_bridge.forge.synthesize_from_cocoons(problem) self._json_response(result) elif _unified_memory: from reasoning_forge.cocoon_synthesizer import CocoonSynthesizer synth = CocoonSynthesizer(memory=_unified_memory) comparison = synth.run_full_synthesis(problem) self._json_response({ "readable": comparison.to_readable(), "structured": comparison.to_dict(), }) else: # Standalone mode — use filesystem cocoons from reasoning_forge.cocoon_synthesizer import CocoonSynthesizer synth = CocoonSynthesizer() comparison = synth.run_full_synthesis(problem) self._json_response({ "readable": comparison.to_readable(), "structured": comparison.to_dict(), }) except Exception as e: import traceback self._json_response({"error": str(e), "traceback": traceback.format_exc()}) elif path == "/api/chat": # SSE endpoint for streaming self._handle_chat_sse(parsed) elif path == "/": # Serve index.html self.path = "/index.html" super().do_GET() else: super().do_GET() def do_POST(self): parsed = urlparse(self.path) path = parsed.path if path == "/api/chat": self._handle_chat_post() elif path == "/api/session/new": self._handle_new_session() elif path == "/api/session/load": self._handle_load_session() elif path == "/api/session/save": self._handle_save_session() elif path == "/api/session/export": self._handle_export_session() elif path == "/api/session/import": self._handle_import_session() elif path == "/api/synthesize": # POST handler for cocoon synthesis with custom problem try: data = self._read_json_body() problem = data.get("problem", "How should an AI decide when to change its own thinking patterns?") domains = data.get("domains", None) from reasoning_forge.cocoon_synthesizer import CocoonSynthesizer if _unified_memory: synth = CocoonSynthesizer(memory=_unified_memory) else: synth = CocoonSynthesizer() comparison = synth.run_full_synthesis(problem, domains) self._json_response({ "readable": comparison.to_readable(), "structured": comparison.to_dict(), }) except Exception as e: import traceback self._json_response({"error": str(e), "traceback": traceback.format_exc()}) else: self.send_error(404, "Not found") def _json_response(self, data, status=200): """Send a JSON response.""" try: body = json.dumps(data, default=str).encode("utf-8") self.send_response(status) self.send_header("Content-Type", "application/json") self.send_header("Content-Length", len(body)) self.send_header("Access-Control-Allow-Origin", "*") self.end_headers() self.wfile.write(body) self.wfile.flush() except (ConnectionAbortedError, BrokenPipeError): # Client disconnected before response was fully sent — this is normal pass except Exception as e: print(f" ERROR in _json_response: {e}") def _read_json_body(self): """Read and parse JSON POST body.""" length = int(self.headers.get("Content-Length", 0)) body = self.rfile.read(length) return json.loads(body) if body else {} def _handle_chat_post(self): """Handle chat request — queue inference, return via SSE or JSON.""" data = self._read_json_body() query = data.get("query", "").strip() adapter = data.get("adapter") max_adapters = data.get("max_adapters", 2) if not query: self._json_response({"error": "Empty query"}, 400) return # Guardian input check if _session and _session.guardian: check = _session.guardian.check_input(query) if not check["safe"]: query = check["cleaned_text"] # Check if orchestrator is loading with _orchestrator_status_lock: status_state = _orchestrator_status.get("state") if status_state == "loading": self._json_response({ "error": "Model is still loading, please wait...", "status": _orchestrator_status, }, 503) return # Queue the request req_id = f"{time.time()}_{id(self)}" response_q = queue.Queue() # Add with thread lock with _response_queues_lock: _response_queues[req_id] = response_q _queue_creation_times[req_id] = time.time() _request_queue.put({ "id": req_id, "query": query, "adapter": adapter, "max_adapters": max_adapters, }) # Wait for response (with timeout) try: # First wait for thinking event thinking = response_q.get(timeout=120) if "error" in thinking and thinking.get("event") != "thinking": self._json_response(thinking, 500) return # Wait for complete event (multi-perspective can take 15+ min on CPU) result = response_q.get(timeout=1200) # 20 min max for inference self._json_response(result) except queue.Empty: self._json_response({"error": "Request timed out"}, 504) finally: # Clean up with thread lock with _response_queues_lock: _response_queues.pop(req_id, None) _queue_creation_times.pop(req_id, None) def _handle_chat_sse(self, parsed): """Handle SSE streaming endpoint.""" params = parse_qs(parsed.query) query = params.get("q", [""])[0] adapter = params.get("adapter", [None])[0] if not query: self.send_error(400, "Missing query parameter 'q'") return # Set up SSE headers self.send_response(200) self.send_header("Content-Type", "text/event-stream") self.send_header("Cache-Control", "no-cache") self.send_header("Access-Control-Allow-Origin", "*") self.send_header("Connection", "keep-alive") self.end_headers() # Queue request req_id = f"sse_{time.time()}_{id(self)}" response_q = queue.Queue() # Add with thread lock with _response_queues_lock: _response_queues[req_id] = response_q _queue_creation_times[req_id] = time.time() _request_queue.put({ "id": req_id, "query": query, "adapter": adapter, "max_adapters": 2, }) try: # Stream events while True: try: event = response_q.get(timeout=300) except queue.Empty: self._send_sse("error", {"error": "Timeout"}) break event_type = event.get("event", "message") self._send_sse(event_type, event) if event_type in ("complete", "error"): break finally: _response_queues.pop(req_id, None) def _send_sse(self, event_type, data): """Send a Server-Sent Event.""" try: payload = f"event: {event_type}\ndata: {json.dumps(data, default=str)}\n\n" self.wfile.write(payload.encode("utf-8")) self.wfile.flush() except Exception: pass def _handle_new_session(self): """Create a new session.""" global _session # Save current session first if _session and _session_store and _session.messages: try: _session_store.save(_session) except Exception: pass _session = CodetteSession() self._json_response({"session_id": _session.session_id}) def _handle_load_session(self): """Load a previous session.""" global _session data = self._read_json_body() session_id = data.get("session_id") if not session_id or not _session_store: self._json_response({"error": "Invalid session ID"}, 400) return loaded = _session_store.load(session_id) if loaded: _session = loaded self._json_response({ "session_id": _session.session_id, "messages": _session.messages, "state": _session.get_state(), }) else: self._json_response({"error": "Session not found"}, 404) def _handle_save_session(self): """Manually save current session.""" if _session and _session_store: _session_store.save(_session) self._json_response({"saved": True, "session_id": _session.session_id}) else: self._json_response({"error": "No active session"}, 400) def _handle_export_session(self): """Export current session as downloadable JSON.""" if not _session: self._json_response({"error": "No active session"}, 400) return export_data = _session.to_dict() export_data["_export_version"] = 1 export_data["_exported_at"] = time.time() body = json.dumps(export_data, default=str, indent=2).encode("utf-8") filename = f"codette_session_{_session.session_id[:8]}.json" self.send_response(200) self.send_header("Content-Type", "application/json") self.send_header("Content-Disposition", f'attachment; filename="{filename}"') self.send_header("Content-Length", len(body)) self.send_header("Access-Control-Allow-Origin", "*") self.end_headers() self.wfile.write(body) def _handle_import_session(self): """Import a session from uploaded JSON.""" global _session try: data = self._read_json_body() if not data or "session_id" not in data: self._json_response({"error": "Invalid session data"}, 400) return # Save current session before importing if _session and _session_store and _session.messages: try: _session_store.save(_session) except Exception: pass _session = CodetteSession() _session.from_dict(data) # Save imported session to store if _session_store: try: _session_store.save(_session) except Exception: pass self._json_response({ "session_id": _session.session_id, "messages": _session.messages, "state": _session.get_state(), "imported": True, }) except Exception as e: self._json_response({"error": f"Import failed: {e}"}, 400) def main(): global _session, _session_store, _worker_threads parser = argparse.ArgumentParser(description="Codette Web UI") parser.add_argument("--port", type=int, default=7860, help="Port (default: 7860)") parser.add_argument("--no-browser", action="store_true", help="Don't auto-open browser") args = parser.parse_args() print("=" * 60) print(" CODETTE WEB UI") print("=" * 60) # Initialize session _session_store = SessionStore() _session = CodetteSession() print(f" Session: {_session.session_id}") print(f" Cocoon: spiderweb={_session.spiderweb is not None}, " f"metrics={_session.metrics_engine is not None}") # Start worker thread for request processing # NOTE: Only 1 worker needed — llama.cpp cannot parallelize inference. # With 1 semaphore + 1 worker, we avoid idle threads and deadlock risk. # Multiple workers would just spin waiting for the semaphore. num_workers = 1 with _worker_threads_lock: for i in range(num_workers): worker = threading.Thread(target=_worker_thread, daemon=True, name=f"worker-{i}") worker.start() _worker_threads.append(worker) print(f" Started {num_workers} worker thread for serial inference") # Start cleanup thread for orphaned response queues cleanup_thread = threading.Thread(target=_cleanup_orphaned_queues, daemon=True, name="cleanup") cleanup_thread.start() print(f" Started cleanup thread for queue maintenance") # Start worker health monitor thread health_monitor = threading.Thread(target=_monitor_worker_health, daemon=True, name="health-monitor") health_monitor.start() print(f" Started worker health monitor thread") # Start server FIRST so browser can connect immediately server = HTTPServer(("127.0.0.1", args.port), CodetteHandler) url = f"http://localhost:{args.port}" print(f"\n Server: {url}") print(f" Press Ctrl+C to stop\n") # Open browser if not args.no_browser: threading.Timer(1.5, lambda: webbrowser.open(url)).start() # Start model loading in background (browser will show "loading" status) threading.Thread(target=_get_orchestrator, daemon=True).start() print(f" Model loading in background (takes ~60s on first startup)...") try: server.serve_forever() except KeyboardInterrupt: print("\n Shutting down...") # Save session if _session and _session_store and _session.messages: _session_store.save(_session) print(f" Session saved: {_session.session_id}") _request_queue.put(None) # Shutdown worker server.shutdown() print(" Goodbye!") if __name__ == "__main__": main()