import os
from dotenv import load_dotenv
from fastapi import FastAPI, Header, HTTPException
from pydantic import BaseModel
from openai import OpenAI
import logging
import redis

LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO").upper()
AI_TOKEN = os.environ["AI_HANDLER_TOKEN"]

AIclient = OpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
)

r = redis.Redis.from_url(os.environ.get("REDIS_URL", "redis://redis:6379"))

# --- Logging Setup ---
numeric_level = getattr(logging, LOG_LEVEL, logging.INFO)
logging.basicConfig(
    level=numeric_level,
    format="%(asctime)s %(levelname)s %(name)s: %(message)s"
)
logger = logging.getLogger(__name__)

class MessagePayload(BaseModel):
    roomId:          str
    userId:          str
    eventId:         str
    serverTimestamp: int
    content:         str

app = FastAPI()

@app.post("/api/v1/message")
async def message(
    payload: MessagePayload,
    authorization: str = Header(None)
):
    if authorization != f"Bearer {AI_TOKEN}":
        raise HTTPException(status_code=401, detail="Unauthorized")

    # Idempotency: ignore duplicates
    if r.get(payload.eventId):
        return {"reply": r.get(payload.eventId).decode()}

    # Build prompt (very simple example)
    prompt = f"User {payload.userId} said: {payload.content}\nBot:"
    chat_response = AIclient.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ],
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.7,
    )

    reply = chat_response.choices[0].message.content.strip()

    # Cache reply for idempotency
    r.set(payload.eventId, reply, ex=3600)

    return {"reply": reply}