# 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.deepseek import DeepSeekClient
from agent.llm.ollama import OllamaClient
from infrastructure.persistence.memory import Memory
from agent.agent import Agent
import os

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

# Choose LLM based on environment variable
llm_provider = os.getenv("LLM_PROVIDER", "deepseek").lower()

if llm_provider == "ollama":
    print("🦙 Using Ollama LLM")
    llm = OllamaClient()
else:
    print("🤖 Using DeepSeek LLM")
    llm = DeepSeekClient()

memory = Memory()
agent = Agent(llm=llm, 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)

    # Process user input through the agent
    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")