File size: 72,737 Bytes
74f2af5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 | #!/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()
|