import json
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent
from langchain.agents import create_agent
from langchain_mcp_adapters.client import MultiServerMCPClient
from utils.fastapi_utils import detect_provider

# Utility functions
def read_system_prompt():
    """读取通用的无状态系统prompt"""
    with open("./prompt/system_prompt_default.md", "r", encoding="utf-8") as f:
        return f.read().strip()


def read_mcp_settings():
    """读取MCP工具配置"""
    with open("./mcp/mcp_settings.json", "r") as f:
        mcp_settings_json = json.load(f)
    return mcp_settings_json

async def init_agent(model_name="qwen3-next", api_key=None,
                    model_server=None, generate_cfg=None,
                    system_prompt=None, mcp=None):
    system = system_prompt if system_prompt else read_system_prompt()
    mcp = mcp if mcp else read_mcp_settings()
    # 修改mcp[0]["mcpServers"]列表，把 type 字段改成 transport ，如果没有的话，就默认transport:stdio
    if mcp and len(mcp) > 0 and "mcpServers" in mcp[0]:
        for server_name, server_config in mcp[0]["mcpServers"].items():
            if isinstance(server_config, dict):
                if "type" in server_config and "transport" not in server_config:
                    # 如果有 type 字段但没有 transport 字段，将 type 改为 transport
                    type_value = server_config.pop("type")
                    # 特殊处理：'streamable-http' 改为 'http'
                    if type_value == "streamable-http":
                        server_config["transport"] = "http"
                    else:
                        server_config["transport"] = type_value
                elif "transport" not in server_config:
                    # 如果既没有 type 也没有 transport，添加默认的 transport: stdio
                    server_config["transport"] = "stdio"

    mcp_client = MultiServerMCPClient(mcp[0]["mcpServers"])
    mcp_tools = await mcp_client.get_tools()

    # 检测或使用指定的提供商
    model_provider,base_url = detect_provider(model_name,model_server)
    
    # 构建模型参数
    model_kwargs = {
        "model": model_name,
        "model_provider": model_provider,
        "temperature": 0.8,
        "base_url":base_url,
        "api_key":api_key
    }
    llm_instance = init_chat_model(**model_kwargs)

    agent = create_agent(
        model=llm_instance,
        system_prompt=system,
        tools=mcp_tools
    )
    return agent