"""大语言模型工厂：支持 OpenAI 兼容的云端 API、Anthropic 兼容 API 和本地 Ollama。"""

import os
from typing import Any

from dotenv import load_dotenv

load_dotenv()


class _BaseLLM:
    """LLM 统一接口基类 — 所有后端都提供 invoke() 和 stream()。"""

    def invoke(self, prompt: str) -> Any:
        raise NotImplementedError

    def stream(self, prompt: str):
        raise NotImplementedError


def get_llm():
    backend = os.getenv("LLM_BACKEND", "cloud")
    if backend == "local":
        from langchain_ollama import ChatOllama

        model = os.getenv("LOCAL_LLM_MODEL", "qwen2.5-coder:7b")
        raw = ChatOllama(model=model, temperature=0.1)

        class OllamaWrapper(_BaseLLM):
            def invoke(self, prompt):
                return raw.invoke(prompt)

            def stream(self, prompt):
                for chunk in raw.stream(prompt):
                    yield chunk.content

        return OllamaWrapper()

    provider = os.getenv("LLM_PROVIDER", "openai")
    if provider == "anthropic":
        from anthropic import Anthropic

        api_key = os.getenv("OPENAI_API_KEY", "")
        base_url = os.getenv("OPENAI_BASE_URL", "https://api.minimaxi.com/anthropic")
        model = os.getenv("LLM_MODEL", "minimax-2.7")
        temperature = 0.1
        max_tokens = 4096

        os.environ["NO_PROXY"] = "*"

        client = Anthropic(api_key=api_key, base_url=base_url, timeout=120)

        class MiniMaxLLM(_BaseLLM):
            def invoke(self, prompt: str) -> Any:
                resp = client.messages.create(
                    model=model,
                    max_tokens=max_tokens,
                    temperature=temperature,
                    messages=[{"role": "user", "content": [{"type": "text", "text": prompt}]}],
                )
                for block in resp.content:
                    if block.type == "text":
                        return type("Response", (), {"content": block.text})()
                return type("Response", (), {"content": ""})()

            def stream(self, prompt: str):
                with client.messages.stream(
                    model=model,
                    max_tokens=max_tokens,
                    temperature=temperature,
                    messages=[{"role": "user", "content": [{"type": "text", "text": prompt}]}],
                ) as s:
                    for text in s.text_stream:
                        yield text

            def get_num_tokens(self, text: str) -> int:
                resp = client.messages.count_tokens(
                    model=model,
                    messages=[{"role": "user", "content": [{"type": "text", "text": text}]}],
                )
                return resp.input_tokens

        return MiniMaxLLM()
    else:
        from langchain_openai import ChatOpenAI

        raw = ChatOpenAI(
            model=os.getenv("LLM_MODEL", "gpt-4o"),
            api_key=os.getenv("OPENAI_API_KEY"),
            base_url=os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1"),
            temperature=0.1,
        )

        class OpenAIWrapper(_BaseLLM):
            def invoke(self, prompt):
                return raw.invoke(prompt)

            def stream(self, prompt):
                for chunk in raw.stream(prompt):
                    yield chunk.content

        return OpenAIWrapper()


def get_llm_for_correction():
    return get_llm()