import asyncio
import json
import logging
import os
import re

import httpx
from anthropic import AsyncAnthropic
from dotenv import load_dotenv
from google.generativeai import GenerativeModel  # gemini
from openai import AsyncOpenAI

from services.prompt import ONLY_GEMINI_PROMPT_TEMPLATE, SUMMARY_PROMPT_TEMPLATE

load_dotenv()

logger = logging.getLogger(__name__)
tasks_store = {}
ask_gpt_name = "gpt-4.1-mini"
ask_ollama_qwen_name = "qwen3:custom"
ask_gemini_name = "gemini-2.5-flash"
ask_claude_name = "claude-3-7-sonnet-latest"


def parse_json_safe(text: str):
    """응답 텍스트가 JSON 포맷이 아닐 수도 있으니 안전하게 파싱 시도"""
    try:
        # 혹시 ```json ... ``` 형식 포함 시 제거
        if text.startswith("```json"):
            text = text.strip("```json").strip("```").strip()
        return json.loads(text)
    except Exception:
        return {"raw_text": text}


async def ask_gpt4(text: str):
    try:
        client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
        response = await client.chat.completions.create(
            model=ask_gpt_name,
            messages=[
                {
                    "role": "user",
                    "content": SUMMARY_PROMPT_TEMPLATE.format(context=text),
                }
            ],
            temperature=0,
        )
        return ask_gpt_name, parse_json_safe(response.choices[0].message.content)
    except Exception as e:
        logger.error(f"ask_gpt4 error: {e}")
        return ask_gpt_name, {"error": str(e)}


def fix_incomplete_json(text: str) -> str:
    open_braces = text.count("{")
    close_braces = text.count("}")
    if open_braces > close_braces:
        text += "}" * (open_braces - close_braces)
    return text


async def ask_ollama_qwen(text: str):
    try:
        async with httpx.AsyncClient() as client:
            res = await client.post(
                "http://172.16.10.176:11434/api/generate",
                json={
                    "model": "qwen3:custom",
                    "prompt": SUMMARY_PROMPT_TEMPLATE.format(context=text),
                },
                timeout=300,
            )
            raw_text = res.text

            # 1. <think> 태그 제거
            raw_text = re.sub(r"</?think>", "", raw_text)

            # 2. 각 줄별 JSON 파싱 시도 (스트림 JSON 형식)
            json_objects = []
            for line in raw_text.splitlines():
                line = line.strip()
                if not line:
                    continue
                try:
                    obj = json.loads(line)
                    json_objects.append(obj)
                except json.JSONDecodeError:
                    # 무시하거나 로그 남기기
                    pass

            # 3. 여러 JSON 조각 중 'response' 필드 내용만 합치기 (필요시)
            full_response = "".join(obj.get("response", "") for obj in json_objects)

            # 4. 합쳐진 response에서 JSON 부분만 추출
            json_match = re.search(r"\{.*\}", full_response, re.DOTALL)
            if json_match:
                json_str = json_match.group(0)
                try:
                    parsed_json = json.loads(json_str)
                    return "qwen3:custom", parsed_json
                except json.JSONDecodeError:
                    return "qwen3:custom", {
                        "error": "Invalid JSON in response",
                        "raw_text": full_response,
                    }
            else:
                return "qwen3:custom", {
                    "error": "No JSON found in response",
                    "raw_text": full_response,
                }

    except Exception as e:
        return "qwen3:custom", {"error": str(e)}


async def ask_gemini(text: str):
    try:
        model = GenerativeModel(model_name=ask_gemini_name)
        response = model.generate_content(SUMMARY_PROMPT_TEMPLATE.format(context=text))
        return ask_gemini_name, parse_json_safe(response.text)
    except Exception as e:
        logger.error(f"ask_gemini error: {e}")
        return ask_gemini_name, {"error": str(e)}


async def dialog_ask_gemini(text: str):
    try:
        model = GenerativeModel(model_name=ask_gemini_name)
        response = model.generate_content(
            ONLY_GEMINI_PROMPT_TEMPLATE.format(context=text)
        )
        return ask_gemini_name, parse_json_safe(response.text)
    except Exception as e:
        logger.error(f"ask_gemini error: {e}")
        return ask_gemini_name, {"error": str(e)}


async def ask_claude(text: str):
    try:
        client = AsyncAnthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
        response = await client.messages.create(
            model=ask_claude_name,
            messages=[
                {
                    "role": "user",
                    "content": SUMMARY_PROMPT_TEMPLATE.format(context=text),
                }
            ],
            max_tokens=12800,
            stream=False,
        )
        raw = response.content[0].text
        return ask_claude_name, parse_json_safe(raw)
    except Exception as e:
        logger.error(f"ask_claude error: {e}")
        return ask_claude_name, {"error": str(e)}


async def total_summation(text: str) -> dict:
    tasks = [ask_gpt4(text), ask_ollama_qwen(text), ask_gemini(text), ask_claude(text)]
    results = await asyncio.gather(*tasks)
    return dict(results)


async def run_model_task(model_func, text, key, task_id):
    try:
        model_name, result = await model_func(text)
        tasks_store[task_id][key] = {
            "status": "completed",
            "model_name": model_name,
            "result": result,
        }
    except Exception as e:
        tasks_store[task_id][key] = {
            "status": "failed",
            "error": str(e),
        }


async def run_all_models(text: str, task_id: str):
    # 초기 상태 세팅
    tasks_store[task_id] = {
        "gpt4": {"status": "pending", "result": None},
        "qwen3": {"status": "pending", "result": None},
        "gemini": {"status": "pending", "result": None},
        "claude": {"status": "pending", "result": None},
        "finished": False,
    }

    await asyncio.gather(
        run_model_task(ask_gpt4, text, "gpt4", task_id),
        run_model_task(ask_ollama_qwen, text, "qwen3", task_id),
        run_model_task(ask_gemini, text, "gemini", task_id),
        run_model_task(ask_claude, text, "claude", task_id),
    )

    tasks_store[task_id]["finished"] = True