import os
import threading

from fastapi import FastAPI, Response
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import RedirectResponse
from pydantic import BaseModel
from typing import Optional

from .agent import ShoppingAgent
from .feedback import salvar_feedback
from .logger import salvar_log_busca


def _cors_origins():
    base = [
        "http://127.0.0.1:5173",
        "http://localhost:5173",
        "http://127.0.0.1:3000",
        "http://localhost:3000",
    ]
    extra = os.getenv("CORS_ORIGINS", "")
    if extra.strip():
        base.extend(o.strip() for o in extra.split(",") if o.strip())
    return base


app = FastAPI(title="TCC2 Agent API")

app.add_middleware(
    CORSMiddleware,
    allow_origins=_cors_origins(),
    allow_origin_regex=r"https://.*\.hf\.space$",
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

agent = None
agent_lock = threading.Lock()


def get_agent():
    global agent
    if agent is None:
        with agent_lock:
            if agent is None:
                agent = ShoppingAgent()
    return agent


class ChatRequest(BaseModel):
    query: Optional[str] = None
    message: Optional[str] = None
    top_k: int = 5


class FeedbackRequest(BaseModel):
    query: str
    product_id: str
    product_name: str
    rating: Optional[int] = None
    is_helpful: Optional[bool] = None


@app.get("/health")
def health():
    return {"status": "ok", "agent_ready": agent is not None}


@app.get("/", include_in_schema=False)
def root():
    return RedirectResponse(url="/docs")


@app.get("/favicon.ico", include_in_schema=False)
def favicon():
    return Response(status_code=204)


@app.post("/chat")
def chat(request: ChatRequest):
    texto = request.query or request.message

    if not texto:
        return {"error": "query ou message deve ser informado"}

    resultado = get_agent().responder(texto, top_k=request.top_k)
    salvar_log_busca(resultado)
    return resultado


@app.post("/feedback")
def feedback(request: FeedbackRequest):
    return salvar_feedback(
        query=request.query,
        product_id=request.product_id,
        product_name=request.product_name,
        rating=request.rating,
        is_helpful=request.is_helpful
    )