# app.py
import time
import uuid
import json
from typing import Any, Dict

from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse, StreamingResponse

from agent.llm import DeepSeekClient
from agent.memory import Memory
from agent.agent import Agent
from agent.commands import CommandRegistry

app = FastAPI(
    title="LibreChat Agent Backend",
    version="0.1.0",
)

llm = DeepSeekClient()
memory = Memory()
agent = Agent(llm=llm, memory=memory)
commands_registry = CommandRegistry(memory=memory)


def extract_last_user_content(messages: list[Dict[str, Any]]) -> str:
    last = ""
    for m in reversed(messages):
        if m.get("role") == "user":
            last = m.get("content") or ""
            break
    return last


@app.post("/v1/chat/completions")
async def chat_completions(request: Request):
    body = await request.json()
    model = body.get("model", "local-deepseek-agent")
    messages = body.get("messages", [])
    stream = body.get("stream", False)

    user_input = extract_last_user_content(messages)
    print("Received chat completion request, stream =", stream, "input:", user_input)

    # 🔹 1) Si c'est une commande, on ne fait PAS intervenir le LLM
    if user_input.strip().startswith("/"):
        answer = commands_registry.handle(user_input)
    else:
        # 🔹 2) Sinon, logique agent + LLM comme avant
        answer = agent.step(user_input)

    # Ensuite = même logique de réponse (non-stream ou stream)
    created_ts = int(time.time())
    completion_id = f"chatcmpl-{uuid.uuid4().hex}"

    if not stream:
        resp = {
            "id": completion_id,
            "object": "chat.completion",
            "created": created_ts,
            "model": model,
            "choices": [
                {
                    "index": 0,
                    "finish_reason": "stop",
                    "message": {
                        "role": "assistant",
                        "content": answer or "",
                    },
                }
            ],
            "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
        }
        return JSONResponse(resp)

    async def event_generator():
        chunk = {
            "id": completion_id,
            "object": "chat.completion.chunk",
            "created": created_ts,
            "model": model,
            "choices": [
                {
                    "index": 0,
                    "delta": {
                        "role": "assistant",
                        "content": answer or "",
                    },
                    "finish_reason": "stop",
                }
            ],
        }
        yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
        yield "data: [DONE]\n\n"

    return StreamingResponse(event_generator(), media_type="text/event-stream")