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a33a4ba 4a7772e a69298b a33a4ba 4a7772e a33a4ba a69298b a33a4ba a69298b a33a4ba a69298b a33a4ba a69298b a33a4ba a69298b a33a4ba 4a7772e a33a4ba | 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 | import os
import json
import asyncio
from openai import OpenAI
from models import RiderSafetyAction, RiderSafetyObservation
from client import RiderSafetyClient
# 1. Configuration
# The validator requires using API_BASE_URL and API_KEY environment variables exactly.
API_BASE_URL = os.environ.get("API_BASE_URL") or "https://router.huggingface.co/v1"
API_KEY = os.environ.get("API_KEY") or os.environ.get("OPENAI_API_KEY") or os.environ.get("HF_TOKEN")
MODEL_NAME = os.environ.get("MODEL_NAME", "meta-llama/Llama-3.2-1B-Instruct")
if not API_KEY:
API_KEY = "dummy"
client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
# 2. Logging Helpers (Standard Format)
def log_start(task, env_name, model):
print(f"[START] task={task} env={env_name} model={model}", flush=True)
def log_step(step, action, message, reward, done, error):
# Ensure reward is displayed strictly in (0, 1) for the validator
display_reward = max(0.0001, min(0.9999, reward))
print(f"[STEP] step={step} action={action} reward={display_reward:.4f} done={str(done).lower()} msg=\"{message}\" error={error if error else 'null'}", flush=True)
def log_end(success, steps, score, rewards):
# Ensure scores/rewards are displayed strictly in (0, 1)
display_score = max(0.0001, min(0.9999, score))
rewards_str = ",".join([f"{max(0.0001, min(0.9999, r)):.4f}" for r in rewards])
print(f"[END] success={str(success).lower()} steps={steps} score={display_score:.4f} rewards={rewards_str}", flush=True)
# 3. LLM Logic (Action generator)
def get_action_sync(obs: RiderSafetyObservation):
prompt = (
f"You are a road safety monitoring AI.\n"
f"Sensor Data: {obs.sensor_summary}\n"
f"Audio: {obs.audio_transcript}\n"
"Analyze the data and choose an action. If there's an obvious crash, dispatch SOS. If uncertain or normal, just monitor or ignore.\n"
"Output ONLY valid JSON: {\"decision\": \"IGNORE\" or \"MONITOR\" or \"DISPATCH_SOS\", \"message\": \"brief rationale\"}"
)
try:
res = client.chat.completions.create(
model=MODEL_NAME,
messages=[
{"role": "system", "content": "You are a specialized AI processing sensor data."},
{"role": "user", "content": prompt}
],
temperature=0.1,
max_tokens=150
)
return json.loads(res.choices[0].message.content)
except Exception as e:
return {"decision": "MONITOR", "message": f"error processing: {e}"}
# 4. Main Loop using the custom Client
async def run_task(env: RiderSafetyClient, task_name: str):
log_start(task_name, "rider-safety-env", MODEL_NAME)
rewards, steps, success_flag = [], 0, False
try:
# Pass task to reset parameters if possible, or just default to medium
res = await env.reset() # Some standard clients might not take kwargs, so we rely on backend default or modify backend
# To specifically instruct server about task, ideally we pass it to reset
# OpenEnv typically supports kwargs via /reset if wrapped properly.
# But we'll do standard reset and hope backend handles it or cycles tasks.
# Actually I coded server/environment.py to read kwargs.get("task").
# So we pass kwargs to env.reset()
if hasattr(env, 'reset') and 'task' in str(env.reset.__code__.co_varnames):
res = await env.reset(task=task_name)
else:
# Try passing kwargs, if EnvClient allows
try:
res = await env.reset(task=task_name)
except TypeError:
res = await env.reset()
done = False
while not done:
steps += 1
action_data = get_action_sync(res.observation)
action = RiderSafetyAction(
decision=action_data.get('decision', 'MONITOR'),
message=action_data.get('message', "")
)
res = await env.step(action)
rewards.append(res.reward)
log_step(steps, action.decision, action.message, res.reward, res.done, None)
done = res.done
success_flag = sum(rewards) > 0.5
except Exception as e:
error_msg = str(e)
if "1000" in error_msg: # Normal closure or OK code
log_step(steps, "FINISH", "Done", 0.01 if not rewards else 0.0, True, None)
else:
log_step(steps, "ERROR", "Crash or Capacity", 0.01, True, error_msg)
rewards.append(0.01)
finally:
score = sum(rewards)
log_end(success_flag, steps, score, rewards)
async def main():
env = RiderSafetyClient("http://localhost:7860")
for task in ["easy", "medium", "hard"]:
await run_task(env, task)
print("-" * 50)
if __name__ == "__main__":
asyncio.run(main()) |