"""
Быстрый решатель заданий BroJS.
Схема: читаем задание (MCP) → 1 LLM-вызов → пушим на Gitea → сабмитим (MCP).
Автономный — не импортирует src.agent, нет двойной загрузки MCP.

Использование:
    python solve_task.py <full_task_id>
"""
import asyncio
import base64
import json
import os
import sys

# Обходим локальный прокси
os.environ["NO_PROXY"] = "openrouter.ai,platform.brojs.ru,git.brojs.ru," + os.environ.get("NO_PROXY", "")

from dotenv import load_dotenv
load_dotenv()

import httpx
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI

GITEA_BASE_URL = "https://git.brojs.ru"
GITEA_OWNER    = os.getenv("GITEA_OWNER", "glevelll")
GITEA_TOKEN    = os.getenv("GITEA_TOKEN", "")
JOURNAL_TOKEN  = os.getenv("JOURNAL_TOKEN", "")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")

MCP_URL = "https://platform.brojs.ru/jrnl-bh/api/mcp"

# ---------------------------------------------------------------------------
# LLM
# ---------------------------------------------------------------------------

llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENAI_API_KEY,
    temperature=0.0,
    max_tokens=4096,
)

# ---------------------------------------------------------------------------
# MCP — один клиент на весь запуск
# ---------------------------------------------------------------------------

_mcp_tools: dict = {}


async def _load_mcp(retries=5, pause=30):
    global _mcp_tools
    if _mcp_tools:
        return
    config = {
        "journal": {
            "transport": "streamable_http",
            "url": MCP_URL,
            "headers": {"Authorization": f"Bearer {JOURNAL_TOKEN}"},
        }
    }
    client = MultiServerMCPClient(config)
    for attempt in range(1, retries + 1):
        try:
            tools = await client.get_tools(server_name="journal")
            _mcp_tools = {t.name: t for t in tools}
            print(f"  [mcp] Загружено {len(_mcp_tools)} инструментов")
            return
        except Exception as e:
            if "429" in str(e) and attempt < retries:
                print(f"  [mcp] 429 при загрузке, жду {pause}с...")
                await asyncio.sleep(pause)
            else:
                raise


async def mcp_call(name: str, args: dict, retries=5, pause=30):
    await _load_mcp()
    tool = _mcp_tools.get(name)
    if not tool:
        raise RuntimeError(f"MCP tool '{name}' not found. Available: {list(_mcp_tools.keys())}")
    for attempt in range(1, retries + 1):
        try:
            result = await tool.ainvoke(args)
            if isinstance(result, list):
                return next((x["text"] for x in result if x.get("type") == "text"), str(result))
            return str(result)
        except Exception as e:
            if "429" in str(e) and attempt < retries:
                print(f"  [mcp] {name} → 429, жду {pause}с (попытка {attempt}/{retries})...")
                await asyncio.sleep(pause)
            else:
                raise


# ---------------------------------------------------------------------------
# Gitea
# ---------------------------------------------------------------------------

def _gh():
    return {"Authorization": f"token {GITEA_TOKEN}", "Content-Type": "application/json"}


def gitea_create_repo(name: str) -> str:
    with httpx.Client(timeout=30) as c:
        r = c.post(f"{GITEA_BASE_URL}/api/v1/user/repos", headers=_gh(),
                   json={"name": name, "private": False, "auto_init": False})
        if r.status_code == 409:
            return f"{GITEA_BASE_URL}/{GITEA_OWNER}/{name}"
        r.raise_for_status()
        return r.json().get("html_url", f"{GITEA_BASE_URL}/{GITEA_OWNER}/{name}")


def gitea_write(repo: str, path: str, content: str, msg: str):
    encoded = base64.b64encode(content.encode()).decode()
    url = f"{GITEA_BASE_URL}/api/v1/repos/{GITEA_OWNER}/{repo}/contents/{path}"
    with httpx.Client(timeout=30) as c:
        r = c.get(url, headers=_gh())
        if r.status_code == 200:
            sha = r.json().get("sha", "")
            c.put(url, headers=_gh(), json={"message": msg, "content": encoded, "sha": sha}).raise_for_status()
        else:
            c.post(url, headers=_gh(), json={"message": msg, "content": encoded}).raise_for_status()


# ---------------------------------------------------------------------------
# LLM: генерация кода
# ---------------------------------------------------------------------------

_PROMPT = '''\
Ты — Python-разработчик. Напиши решение для учебного задания по LLM/AI.
Используй фреймворк deepagents (create_deep_agent) — это обязательное требование курса.

## Задание
{task_text}

## ОБЯЗАТЕЛЬНЫЕ ТЕХНИЧЕСКИЕ ПАТТЕРНЫ

### LLM — всегда OpenRouter:
```python
import os
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.0,
)
```

### Базовый агент (deepagents) — ОБЯЗАТЕЛЬНАЯ основа:
```python
import asyncio, os
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend

llm = ChatOpenAI(model="openai/gpt-oss-20b:free", base_url="https://openrouter.ai/api/v1", api_key=os.getenv("OPENAI_API_KEY"))

backend = CompositeBackend([
    LocalShellBackend(workspace_dir="./workspace"),
    FilesystemBackend(),
])

@tool
def my_tool(query: str) -> str:
    """Tool description."""
    return f"result for {{query}}"

agent = create_deep_agent(
    model=llm,
    tools=[my_tool],
    backend=backend,
    system_prompt="You are a helpful agent.",
)

async def main():
    result = await agent.ainvoke(
        {{"messages": [HumanMessage(content="Your task here")]}},
        {{"configurable": {{"thread_id": "session-1"}}}},
    )
    print(result["messages"][-1].content)

if __name__ == "__main__":
    asyncio.run(main())
```
requirements.txt: deepagents, langchain-openai>=0.3.0, langchain>=1.2.10, langgraph>=0.2.0

### RAG с Qdrant (для RAG-заданий):
```python
from langchain_openai import OpenAIEmbeddings
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams
from langchain_core.documents import Document

embeddings = OpenAIEmbeddings(model="text-embedding-3-small", base_url="https://openrouter.ai/api/v1", api_key=os.getenv("OPENAI_API_KEY"))
client = QdrantClient(":memory:")
client.create_collection("knowledge", vectors_config=VectorParams(size=1536, distance=Distance.COSINE))
vector_store = QdrantVectorStore(client=client, collection_name="knowledge", embedding=embeddings)

@tool
def search_knowledge(query: str) -> str:
    """Search the knowledge base."""
    docs = vector_store.similarity_search(query, k=3)
    return "\\n".join(d.page_content for d in docs) if docs else "No results."

@tool
def add_to_knowledge(content: str, title: str = "doc") -> str:
    """Add content to knowledge base."""
    vector_store.add_documents([Document(page_content=content, metadata={{"title": title}})])
    return f"Added: {{title}}"
```
requirements.txt добавить: langchain-qdrant, qdrant-client

### Планирующий агент (для planning-заданий):
```python
from langgraph.graph import StateGraph, START, END
from typing import TypedDict, Annotated
from langgraph.graph.message import add_messages

class PlanState(TypedDict):
    messages: Annotated[list, add_messages]
    plan: list[str]
    current_step: int

def planner_node(state):
    # LLM создаёт план
    ...

def executor_node(state):
    # LLM выполняет шаг плана
    ...
```

### Самокорректирующийся агент:
```python
# Агент проверяет свой вывод и исправляет если нужно
@tool
def validate_output(output: str) -> str:
    """Validate the output and return issues if any."""
    issues = []
    if len(output) < 10:
        issues.append("Output too short")
    return "OK" if not issues else f"Issues: {{', '.join(issues)}}"
```

### Структурированный вывод (Pydantic):
```python
from pydantic import BaseModel, Field
from langchain_core.output_parsers import PydanticOutputParser

class MyOutput(BaseModel):
    field1: str = Field(description="...")
    field2: int = Field(description="...")

parser = PydanticOutputParser(pydantic_object=MyOutput)
```

## Требования
- Полный рабочий код без заглушек (no pass, TODO, ...)
- ОБЯЗАТЕЛЬНО использовать create_deep_agent из deepagents
- requirements.txt: deepagents, langchain>=1.2.10, langchain-openai>=0.3.0, langgraph>=0.2.0 + нужные доп. зависимости

## Ответ — ТОЛЬКО JSON без markdown:
{{"main_py": "...", "requirements_txt": "...", "extra_files": {{}}}}

extra_files — только если нужны доп. файлы, иначе пустой объект.
'''


async def generate(task_text: str, retries=5) -> dict:
    prompt = _PROMPT.format(task_text=task_text)
    for attempt in range(1, retries + 1):
        try:
            print(f"  [llm] Генерирую решение (попытка {attempt})...")
            resp = await llm.ainvoke(prompt)
            raw = resp.content.strip()
            if raw.startswith("```"):
                raw = raw.split("```")[1]
                if raw.startswith("json"):
                    raw = raw[4:]
            return json.loads(raw.strip())
        except json.JSONDecodeError as e:
            print(f"  [llm] JSON parse error: {e}. Повтор...")
            if attempt == retries:
                raise
        except Exception as e:
            if "429" in str(e) and attempt < retries:
                wait = 90 * attempt
                print(f"  [llm] 429, жду {wait}с (попытка {attempt}/{retries})...")
                await asyncio.sleep(wait)
            else:
                raise


# ---------------------------------------------------------------------------
# Основная логика
# ---------------------------------------------------------------------------

async def solve(task_id: str):
    print(f"\n{'='*60}")
    print(f"Задание: {task_id}")
    print('='*60)

    # 1. Читаем текст задания
    print("[1/5] Читаем текст задания...")
    task_text = await mcp_call("task_text", {"taskId": task_id})
    print(f"  Получено {len(task_text)} символов")

    # 2. Генерируем код
    print("[2/5] Генерируем код (1 LLM-вызов)...")
    solution = await generate(task_text)
    main_py      = solution.get("main_py", "")
    requirements = solution.get("requirements_txt", "")
    extra        = solution.get("extra_files", {})
    print(f"  main.py: {len(main_py)} символов, requirements.txt: {len(requirements)} символов")

    # 3. Создаём репо
    repo = f"task-{task_id}"
    print(f"[3/5] Создаём репозиторий {repo}...")
    repo_url = gitea_create_repo(repo)
    print(f"  {repo_url}")

    # 4. Пушим файлы
    print("[4/5] Пушим файлы...")
    gitea_write(repo, "main.py", main_py, "add main.py")
    print("  main.py ✓")
    gitea_write(repo, "requirements.txt", requirements, "add requirements.txt")
    print("  requirements.txt ✓")
    for fname, fcontent in extra.items():
        gitea_write(repo, fname, fcontent, f"add {fname}")
        print(f"  {fname} ✓")

    # 5. Сабмитим
    print("[5/5] Сабмитим...")
    await mcp_call("task_update_answer", {
        "taskId": task_id, "answerType": "link", "content": repo_url,
    })
    print("  task_update_answer ✓")
    await asyncio.sleep(3)
    await mcp_call("task_submit", {"taskId": task_id, "confirmSubmit": True})
    print("  task_submit ✓")

    print(f"\n✅ Готово! Репозиторий: {repo_url}")
    return repo_url


async def main():
    if len(sys.argv) < 2:
        print("Использование: python solve_task.py <task_id>")
        sys.exit(1)
    await solve(sys.argv[1])


if __name__ == "__main__":
    asyncio.run(main())