inference.py

from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from pydantic import BaseModel
from env import TrafficEnv
from tasks import get_config
from baseline_agent import RuleBasedAgent
import os
import openai

class LLMAgent:
    def __init__(self):
        try:
            self.client = openai.OpenAI(
                base_url=os.environ["API_BASE_URL"], 
                api_key=os.environ["API_KEY"]
            )
        except Exception:
            self.client = None
        self.fallback = RuleBasedAgent()
        
    def select_action(self, state):
        prompt = f"Traffic state: {state}. Reply with 1 to switch phase, 0 to keep phase. Output only 1 or 0."
        try:
            response = self.client.chat.completions.create(
                model="gpt-3.5-turbo",
                messages=[
                    {"role": "system", "content": "You are a traffic signal controller."},
                    {"role": "user", "content": prompt}
                ],
                max_tokens=5,
                temperature=0.0
            )
            content = response.choices[0].message.content.strip()
            # Still call fallback to maintain its internal step counter
            self.fallback.select_action(state)
            
            if "1" in content:
                return 1
            else:
                return 0
        except Exception as e:
            return self.fallback.select_action(state)

    def reset(self):
        self.fallback.reset()

app = FastAPI()
env = TrafficEnv(get_config("medium"))
agent = LLMAgent()

class Action(BaseModel):
    action: int

@app.get("/", response_class=HTMLResponse)
def root():
    with open("index.html", "r", encoding="utf-8") as f:
        return f.read()

@app.post("/reset")
def reset():
    state = env.reset()
    try:
        state = state.tolist()
    except:
        pass
    agent.reset()
    return {"state":state}

@app.post("/step")
def step(data:Action):
    state,reward,done,info = env.step(data.action)
    try:
        state = state.tolist()
    except:
        pass
    return {
        "state":state,
        "reward":reward,
        "done":done,
        "info":info
    }

@app.post("/auto_step")
def auto_step():
    state_dict = env.get_state()
    action = agent.select_action(state_dict)
    state,reward,done,info = env.step(action)
    try:
        state = state.tolist()
    except:
        pass
    return {
        "state":state,
        "reward":reward,
        "done":done,
        "info":info,
        "action_taken": action
    }

if __name__ == "__main__":
    import sys
    tasks_to_run = ["easy", "medium", "hard"]
    if len(sys.argv) > 1:
        # e.g., if validator optionally passes a task name as argument
        task_arg = sys.argv[1].replace("--task=", "").replace("--task", "")
        if task_arg in tasks_to_run:
            tasks_to_run = [task_arg]

    for task_name in tasks_to_run:
        config = get_config(task_name)
        eval_env = TrafficEnv(config)
        eval_agent = LLMAgent()
        
        state = eval_env.reset()
        eval_agent.reset()
        
        print("[START]", flush=True)
        
        done = False
        step_idx = 0
        total_reward = 0.0
        
        while not done:
            action = eval_agent.select_action(state)
            state, reward, done, info = eval_env.step(action)
            print(f"[STEP] step={step_idx}, reward={reward}, done={done}", flush=True)
            step_idx += 1
            total_reward += reward
            
        print("[END]", flush=True)