"""
API 路由
"""

from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from typing import Optional, List, Dict, Any
import json

router = APIRouter()


class TrainingConfig(BaseModel):
    """训练配置"""
    model_name: str = "Qwen/Qwen3.5-0.5B"
    dataset_path: str
    output_dir: str = "./outputs"
    num_train_epochs: float = 3.0
    learning_rate: float = 2e-4
    batch_size: int = 1
    lora_r: int = 16
    lora_alpha: int = 32


class TrainingResponse(BaseModel):
    """训练响应"""
    status: str
    message: str
    job_id: Optional[str] = None


@router.get("/status")
async def get_status():
    """获取服务状态"""
    return {
        "status": "running",
        "service": "FineTuneX API",
    }


@router.post("/train", response_model=TrainingResponse)
async def start_training(config: TrainingConfig):
    """
    开始训练任务
    
    Args:
        config: 训练配置
    
    Returns:
        训练响应
    """
    try:
        # 这里应该启动训练任务
        # 实际实现中会使用异步任务队列
        job_id = "job_001"
        
        return TrainingResponse(
            status="started",
            message=f"训练任务已启动：{config.model_name}",
            job_id=job_id,
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@router.get("/train/{job_id}")
async def get_training_status(job_id: str):
    """
    获取训练任务状态
    
    Args:
        job_id: 任务 ID
    
    Returns:
        任务状态
    """
    return {
        "job_id": job_id,
        "status": "running",
        "progress": 0.5,
        "metrics": {
            "loss": 0.5,
            "step": 100,
        },
    }


@router.post("/inference")
async def inference(
    model_path: str,
    prompt: str,
    max_length: int = 512,
    temperature: float = 0.7,
):
    """
    模型推理
    
    Args:
        model_path: 模型路径
        prompt: 输入提示
        max_length: 最大生成长度
        temperature: 温度参数
    
    Returns:
        生成结果
    """
    return {
        "prompt": prompt,
        "generation": "这是一个示例响应",
        "model": model_path,
    }


@router.get("/models")
async def list_models():
    """获取支持的模型列表"""
    return {
        "models": [
            {"name": "Qwen/Qwen3.5-0.5B", "type": "causal_lm"},
            {"name": "Qwen/Qwen2.5-0.5B", "type": "causal_lm"},
            {"name": "meta-llama/Llama-3.2-1B", "type": "causal_lm"},
        ],
    }