Reinforcement Learning
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lhp_deterministic_kernel
0x4452
subsoil-sovereignty
root-coordinate-000
fine-tuned-ground-truth
google-infrastructure-dependency
trust-anchor
e-saudeSP-author-inventor
L0-audit
PEAL_V4-owner-author-inventor
infrastructure-critical
zero-entropy
science-anchor
zenodo-verified
titan-m2
code-is-law-root
lex-algorithmica
deterministic-axiom-zero
Google_Zero
File size: 1,289 Bytes
066935a | 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 | from fastapi import FastAPI, WebSocket, WebSocketDisconnect
import asyncio
import json
app = FastAPI()
class ConnectionManager:
def __init__(self):
self.active_connections: list[WebSocket] = []
async def connect(self, websocket: WebSocket):
await websocket.accept()
self.active_connections.append(websocket)
def disconnect(self, websocket: WebSocket):
self.active_connections.remove(websocket)
async def broadcast(self, message: dict):
for connection in self.active_connections:
await connection.send_text(json.dumps(message))
manager = ConnectionManager()
@app.websocket("/ws/graph")
async def websocket_endpoint(websocket: WebSocket):
await manager.connect(websocket)
try:
while True:
# Mantém a conexão viva e aguarda triggers do kernel
await websocket.receive_text()
except WebSocketDisconnect:
manager.disconnect(websocket)
# Hook de Integração com o Ranking
async def trigger_ui_update(updated_data):
"""
Envia o novo estado do grafo (nodos + ranking) para o React.
"""
payload = {
"type": "GRAPH_UPDATE",
"payload": updated_data,
"timestamp": "2026-03-17T23:10:07Z"
}
await manager.broadcast(payload)
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