from fastapi import FastAPI, File, UploadFile, HTTPException
from pydantic import BaseModel
from typing import List, Optional
import uvicorn
import os
import json
import re
import time

app = FastAPI(title="大模型微调平台 API", version="1.0.0")


# 请求模型
class UserModel(BaseModel):
    username: str
    password: str


class DatasetModel(BaseModel):
    name: str
    description: Optional[str] = None
    size: str


class ModelConfigModel(BaseModel):
    model_name: str
    learning_rate: float
    batch_size: int
    epochs: int


# 响应模型
class ResponseModel(BaseModel):
    code: int
    message: str
    data: Optional[dict] = None


# 模拟数据存储
datasets = [
    {"id": 1, "name": "中文对话数据集", "size": "1.2GB", "status": "已处理"},
    {"id": 2, "name": "英文文本分类数据集", "size": "856MB", "status": "处理中"},
    {"id": 3, "name": "图像识别数据集", "size": "2.5GB", "status": "待处理"},
]

models = [
    {"id": 1, "name": "GPT-4", "status": "训练中", "accuracy": "92%"},
    {"id": 2, "name": "BERT", "status": "已完成", "accuracy": "89%"},
    {"id": 3, "name": "LLaMA", "status": "已完成", "accuracy": "95%"},
]


@app.get("/")
async def root():
    """根路径"""
    return {"message": "大模型微调平台 API 服务"}


@app.get("/api/health")
async def health_check():
    """健康检查"""
    return ResponseModel(code=200, message="服务运行正常", data={"status": "healthy"})


@app.post("/api/login", response_model=ResponseModel)
async def login(user: UserModel):
    """用户登录"""
    if user.username == "admin" and user.password:
        return ResponseModel(
            code=200,
            message="登录成功",
            data={"token": "mock_token_12345", "user": user.username}
        )
    else:
        return ResponseModel(code=401, message="用户名或密码错误")


@app.get("/api/datasets", response_model=ResponseModel)
async def get_datasets():
    """获取数据集列表"""
    return ResponseModel(code=200, message="获取成功", data={"datasets": datasets})


@app.post("/api/datasets", response_model=ResponseModel)
async def create_dataset(dataset: DatasetModel):
    """创建数据集"""
    new_dataset = {
        "id": len(datasets) + 1,
        "name": dataset.name,
        "description": dataset.description,
        "size": "0MB",
        "status": "待处理"
    }
    datasets.append(new_dataset)
    return ResponseModel(code=201, message="创建成功", data={"dataset": new_dataset})


@app.post("/api/datasets/upload", response_model=ResponseModel)
async def upload_dataset(file: UploadFile = File(...), description: Optional[str] = None):
    """上传数据集文件（仅支持 JSON 和 JSONL 格式）"""
    # 检查文件类型
    allowed_extensions = ['.json', '.jsonl']
    file_extension = os.path.splitext(file.filename)[1].lower()

    if file_extension not in allowed_extensions:
        raise HTTPException(
            status_code=400,
            detail=f"不支持的文件类型。只能上传 {', '.join(allowed_extensions)} 格式的文件"
        )

    # 检查文件大小（限制为 100MB）
    max_size = 100 * 1024 * 1024  # 100MB
    contents = await file.read()
    file_size = len(contents)

    if file_size > max_size:
        raise HTTPException(
            status_code=400,
            detail=f"文件大小超过限制。最大支持 100MB，当前文件大小: {file_size / (1024*1024):.2f}MB"
        )

    try:
        # 验证文件内容
        if file_extension == '.json':
            # 验证 JSON 文件
            json.loads(contents.decode('utf-8'))
        elif file_extension == '.jsonl':
            # 验证 JSONL 文件（每行必须是有效的 JSON）
            lines = contents.decode('utf-8').strip().split('\n')
            for i, line in enumerate(lines):
                if line.strip():
                    try:
                        json.loads(line)
                    except json.JSONDecodeError as e:
                        raise HTTPException(
                            status_code=400,
                            detail=f"JSONL 文件格式错误：第 {i+1} 行不是有效的 JSON 格式"
                        )

        # 生成文件大小字符串
        if file_size < 1024:
            size_str = f"{file_size}B"
        elif file_size < 1024 * 1024:
            size_str = f"{file_size / 1024:.2f}KB"
        else:
            size_str = f"{file_size / (1024*1024):.2f}MB"

        # 计算行数（用于统计）
        lines_count = len(contents.decode('utf-8').strip().split('\n')) if contents else 0

        # 保存文件到 data 目录
        data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data')
        os.makedirs(data_dir, exist_ok=True)

        # 生成唯一文件名（避免冲突）
        base_name = os.path.splitext(file.filename)[0]
        timestamp = int(time.time())
        saved_filename = f"{base_name}_{timestamp}{file_extension}"
        saved_path = os.path.join(data_dir, saved_filename)

        # 写入文件
        with open(saved_path, 'wb') as f:
            f.write(contents)

        # 创建新数据集记录
        new_dataset = {
            "id": len(datasets) + 1,
            "name": file.filename,
            "description": description or f"上传的数据集文件，包含 {lines_count} 行数据",
            "size": size_str,
            "status": "已处理",
            "upload_time": "刚刚",
            "file_extension": file_extension,
            "records_count": lines_count,
            "saved_path": saved_path  # 添加保存路径信息
        }

        # 添加到数据集列表
        datasets.append(new_dataset)

        return ResponseModel(
            code=200,
            message="文件上传成功",
            data={
                "dataset": new_dataset,
                "file_info": {
                    "filename": file.filename,
                    "size": size_str,
                    "extension": file_extension,
                    "records": lines_count
                }
            }
        )

    except json.JSONDecodeError:
        raise HTTPException(
            status_code=400,
            detail="JSON 文件格式错误：文件内容不是有效的 JSON 格式"
        )
    except UnicodeDecodeError:
        raise HTTPException(
            status_code=400,
            detail="文件编码错误：请确保文件使用 UTF-8 编码"
        )
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"文件处理错误：{str(e)}"
        )


@app.get("/api/datasets/files", response_model=ResponseModel)
async def list_dataset_files():
    """列出data目录中所有保存的数据集文件"""
    try:
        data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data')

        if not os.path.exists(data_dir):
            return ResponseModel(
                code=200,
                message="获取成功",
                data={"files": [], "total": 0, "directory": data_dir}
            )

        files = []
        for filename in os.listdir(data_dir):
            file_path = os.path.join(data_dir, filename)
            if os.path.isfile(file_path):
                stat = os.stat(file_path)
                files.append({
                    "filename": filename,
                    "size": stat.st_size,
                    "size_human": format_size(stat.st_size),
                    "modified_time": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(stat.st_mtime)),
                    "path": file_path
                })

        # 按修改时间排序（最新的在前）
        files.sort(key=lambda x: x["modified_time"], reverse=True)

        return ResponseModel(
            code=200,
            message="获取成功",
            data={
                "files": files,
                "total": len(files),
                "directory": data_dir
            }
        )
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"获取文件列表失败：{str(e)}"
        )


def format_size(size_bytes):
    """格式化文件大小"""
    if size_bytes < 1024:
        return f"{size_bytes}B"
    elif size_bytes < 1024 * 1024:
        return f"{size_bytes / 1024:.2f}KB"
    else:
        return f"{size_bytes / (1024*1024):.2f}MB"


@app.delete("/api/datasets/{dataset_id}", response_model=ResponseModel)
async def delete_dataset(dataset_id: int):
    """删除数据集"""
    global datasets
    for i, dataset in enumerate(datasets):
        if dataset["id"] == dataset_id:
            deleted_dataset = datasets.pop(i)
            return ResponseModel(
                code=200,
                message="删除成功",
                data={"deleted_dataset": deleted_dataset}
            )
    raise HTTPException(status_code=404, detail="数据集不存在")


@app.get("/api/models", response_model=ResponseModel)
async def get_models():
    """获取模型列表"""
    return ResponseModel(code=200, message="获取成功", data={"models": models})


@app.post("/api/models/config", response_model=ResponseModel)
async def config_model(config: ModelConfigModel):
    """配置模型参数"""
    return ResponseModel(
        code=200,
        message="配置成功",
        data={
            "model_name": config.model_name,
            "learning_rate": config.learning_rate,
            "batch_size": config.batch_size,
            "epochs": config.epochs,
            "status": "已配置"
        }
    )


@app.get("/api/training/status")
async def get_training_status():
    """获取训练状态"""
    return ResponseModel(
        code=200,
        message="获取成功",
        data={
            "current_task": "GPT-4微调",
            "progress": 75,
            "eta": "2小时",
            "loss": 0.23,
            "accuracy": 0.89
        }
    )


@app.get("/api/system/stats")
async def get_system_stats():
    """获取系统统计信息"""
    import random
    return ResponseModel(
        code=200,
        message="获取成功",
        data={
            "cpu_usage": random.randint(30, 80),
            "memory_usage": random.randint(40, 70),
            "gpu_usage": random.randint(50, 90),
            "active_tasks": 5,
            "completed_tasks": 158
        }
    )


@app.post("/api/training/start")
async def start_training(model_name: str, dataset_id: int):
    """开始训练任务"""
    return ResponseModel(
        code=200,
        message="训练任务已启动",
        data={
            "task_id": random.randint(1000, 9999),
            "model_name": model_name,
            "dataset_id": dataset_id,
            "status": "running"
        }
    )


@app.post("/api/training/stop/{task_id}")
async def stop_training(task_id: int):
    """停止训练任务"""
    return ResponseModel(
        code=200,
        message=f"训练任务 {task_id} 已停止",
        data={"task_id": task_id, "status": "stopped"}
    )


@app.get("/api/model/{model_id}/metrics")
async def get_model_metrics(model_id: int):
    """获取模型指标"""
    return ResponseModel(
        code=200,
        message="获取成功",
        data={
            "model_id": model_id,
            "accuracy": round(random.uniform(0.85, 0.98), 3),
            "precision": round(random.uniform(0.80, 0.95), 3),
            "recall": round(random.uniform(0.82, 0.96), 3),
            "f1_score": round(random.uniform(0.83, 0.97), 3),
            "training_time": f"{random.randint(2, 24)}小时",
            "parameters": random.randint(1000000, 100000000)
        }
    )


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8001)