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f2e28ca | 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 | """FastAPI app that streams live LLM episodes to the React visualizer.
This is intentionally a *separate* FastAPI app from
:mod:`server.app` (which mounts the OpenEnv environment for RL training and
remote rollout clients). It exposes a small, browser-friendly REST + SSE
surface so the React dashboard at ``visualizer/`` can:
- List the rule-based and LLM-backed inference modes.
- Start a new live run with a chosen seed and policy.
- Stream per-round JSON records over Server-Sent Events while the run is
still in progress.
- Re-fetch the full episode-so-far when the page is refreshed mid-run.
Run it locally with::
uvicorn server.visualizer_server:app --host 0.0.0.0 --port 8001
Or via the convenience wrapper::
python -m scripts.run_visualizer_server
"""
from __future__ import annotations
import json
import logging
import os
import time
from typing import Any, Optional
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, Field
from server.live_runner import (
LiveRun,
LiveRunManager,
build_config_dict,
)
load_dotenv()
LOG = logging.getLogger(__name__)
# ββ Mode metadata exposed to the UI βββββββββββββββββββββββββββββββββββββββββ
_RULE_BASED_MODES = (
{
"id": "equal_split",
"label": "Equal split",
"description": "Each minister proposes treasury / 6 every round.",
"requires_credentials": False,
},
{
"id": "optimal_zone",
"label": "Optimal zone (1.3Γ baseline)",
"description": "Targets the profit zone above demand without entering wastage.",
"requires_credentials": False,
},
{
"id": "conservative",
"label": "Conservative (baseline)",
"description": "Each minister proposes its own department baseline.",
"requires_credentials": False,
},
{
"id": "greedy",
"label": "Greedy",
"description": "Greedy proposer baseline; useful for adversarial demos.",
"requires_credentials": False,
},
)
def _llm_mode_entry() -> dict[str, Any]:
return {
"id": "llm",
"label": "LLM (Hugging Face)",
"description": "Drives every minister with the configured HF model.",
"requires_credentials": True,
"default_model_id": os.environ.get("HF_MODEL_ID"),
"credentials_present": bool(os.environ.get("HF_TOKEN"))
and bool(os.environ.get("HF_MODEL_ID")),
}
# ββ Request/response models βββββββββββββββββββββββββββββββββββββββββββββββββ
class StartRunBody(BaseModel):
mode: str = Field(
description="One of the ids returned by GET /api/modes."
)
model_id: Optional[str] = Field(default=None, description="HF model id (LLM mode).")
seed: int = Field(default=42, ge=0)
max_rounds: int = Field(default=20, ge=1, le=50)
temperature: float = Field(default=0.2, ge=0.0, le=2.0)
class StartRunResponse(BaseModel):
run_id: str
mode: str
policy: str
seed: int
max_rounds: int
config: dict[str, Any]
# ββ App + manager βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
app = FastAPI(
title="Nation Optimizer Β· Visualizer",
description="Streams live LLM-driven parliamentary episodes to the React dashboard.",
version="0.1.0",
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
manager = LiveRunManager()
# ββ Routes ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
@app.get("/api/health")
def health() -> dict[str, Any]:
return {"ok": True, "ts": time.time()}
@app.get("/api/config")
def config() -> dict[str, Any]:
"""Static game config (sectors, baselines, etc.) for the empty-state UI."""
return build_config_dict()
@app.get("/api/modes")
def modes() -> dict[str, Any]:
return {
"modes": [_llm_mode_entry(), *_RULE_BASED_MODES],
}
@app.get("/api/runs")
def list_runs() -> dict[str, Any]:
return {"runs": manager.list_runs()}
@app.post("/api/runs", response_model=StartRunResponse)
def start_run(body: StartRunBody) -> StartRunResponse:
if body.mode == "llm":
token = os.environ.get("HF_TOKEN")
if not token:
raise HTTPException(
status_code=400,
detail=(
"LLM mode requires HF_TOKEN in the server environment. "
"Either set it in .env or pick a rule-based mode."
),
)
model_id = body.model_id or os.environ.get("HF_MODEL_ID")
if not model_id:
raise HTTPException(
status_code=400,
detail=(
"LLM mode requires model_id (or HF_MODEL_ID env var)."
),
)
else:
token = None
model_id = body.model_id
try:
run = manager.create(
mode=body.mode,
model_id=model_id,
seed=body.seed,
max_rounds=body.max_rounds,
temperature=body.temperature,
token=token,
)
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc))
return StartRunResponse(
run_id=run.run_id,
mode=run.mode,
policy=run.policy,
seed=run.seed,
max_rounds=run.max_rounds,
config=run.config,
)
@app.get("/api/runs/{run_id}/snapshot")
def snapshot(run_id: str) -> dict[str, Any]:
run = manager.get(run_id)
if run is None:
raise HTTPException(status_code=404, detail=f"Unknown run_id {run_id!r}.")
return run.snapshot()
@app.get("/api/runs/{run_id}/stream")
async def stream(run_id: str, request: Request) -> StreamingResponse:
"""SSE stream of ``start | round | summary | error | done`` events."""
run = manager.get(run_id)
if run is None:
raise HTTPException(status_code=404, detail=f"Unknown run_id {run_id!r}.")
return StreamingResponse(
_sse_iterator(run, request),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"X-Accel-Buffering": "no",
"Connection": "keep-alive",
},
)
# ββ SSE plumbing ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
async def _sse_iterator(run: LiveRun, request: Request):
"""Yield Server-Sent Events for one client subscriber.
A heartbeat comment (``:keep-alive``) is sent every ~15s so proxies don't
cut the connection during long LLM generations.
"""
import asyncio
queue = run.subscribe()
try:
last_heartbeat = time.time()
while True:
if await request.is_disconnected():
break
event = await asyncio.get_event_loop().run_in_executor(
None, _drain, queue, 1.0
)
if event is not None:
yield _format_sse(event)
if event.get("type") == "done":
break
continue
now = time.time()
if now - last_heartbeat > 15:
yield ": keep-alive\n\n"
last_heartbeat = now
finally:
run.unsubscribe(queue)
def _drain(queue: Any, timeout: float) -> Optional[dict[str, Any]]:
from queue import Empty
try:
return queue.get(timeout=timeout)
except Empty:
return None
def _format_sse(event: dict[str, Any]) -> str:
"""Format a dict as a Server-Sent Event with an explicit ``event:`` name."""
event_type = event.get("type", "message")
payload = event.get("data", {})
body = json.dumps(payload, default=_json_default)
return f"event: {event_type}\ndata: {body}\n\n"
def _json_default(value: Any) -> Any:
if hasattr(value, "to_dict") and callable(value.to_dict):
return value.to_dict()
if hasattr(value, "value"):
return value.value
return str(value)
__all__ = ["app", "manager"]
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