"""
PyRamEx FastAPI主应用
GPU加速的拉曼光谱分析系统
"""

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List, Optional
import logging

# 配置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# 创建FastAPI应用
app = FastAPI(
    title="PyRamEx API",
    description="GPU加速的拉曼光谱分析系统",
    version="2.0.0"
)

# CORS配置
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# 数据模型
class SpectrumData(BaseModel):
    """光谱数据模型"""
    wavenumber: List[float]
    intensity: List[float]
    metadata: Optional[dict] = None


class AnalysisRequest(BaseModel):
    """分析请求模型"""
    spectra: List[SpectrumData]
    analysis_type: str  # 'preprocessing', 'qc', 'ml', 'full'


class AnalysisResponse(BaseModel):
    """分析响应模型"""
    status: str
    results: dict
    message: Optional[str] = None


# 健康检查端点
@app.get("/health")
async def health_check():
    """健康检查"""
    return {
        "status": "healthy",
        "service": "pyramex-app",
        "version": "2.0.0"
    }


# 根路径
@app.get("/")
async def root():
    """根路径"""
    return {
        "message": "PyRamEx API - GPU加速的拉曼光谱分析系统",
        "version": "2.0.0",
        "docs": "/docs"
    }


# 预处理端点
@app.post("/api/v1/preprocess", response_model=AnalysisResponse)
async def preprocess_spectra(request: AnalysisRequest):
    """预处理光谱数据"""
    try:
        logger.info(f"收到预处理请求，光谱数量: {len(request.spectra)}")

        # TODO: 实现GPU加速的预处理
        # 1. 平滑
        # 2. 基线校正
        # 3. 归一化

        return AnalysisResponse(
            status="success",
            results={
                "processed": len(request.spectra),
                "methods": ["smooth", "baseline", "normalize"]
            },
            message="预处理完成"
        )
    except Exception as e:
        logger.error(f"预处理失败: {e}")
        raise HTTPException(status_code=500, detail=str(e))


# 质控端点
@app.post("/api/v1/qc", response_model=AnalysisResponse)
async def quality_control(request: AnalysisRequest):
    """质量控制分析"""
    try:
        logger.info(f"收到质控请求，光谱数量: {len(request.spectra)}")

        # TODO: 实现质控算法
        # 1. ICOD
        # 2. MCD
        # 3. SNR

        return AnalysisResponse(
            status="success",
            results={
                "total": len(request.spectra),
                "good": int(len(request.spectra) * 0.9),
                "outliers": int(len(request.spectra) * 0.1)
            },
            message="质控完成"
        )
    except Exception as e:
        logger.error(f"质控失败: {e}")
        raise HTTPException(status_code=500, detail=str(e))


# ML分析端点
@app.post("/api/v1/analyze", response_model=AnalysisResponse)
async def ml_analysis(request: AnalysisRequest):
    """机器学习分析"""
    try:
        logger.info(f"收到ML分析请求，光谱数量: {len(request.spectra)}")

        # TODO: 实现ML分析
        # 1. 特征提取
        # 2. PCA降维
        # 3. 分类/聚类

        return AnalysisResponse(
            status="success",
            results={
                "predictions": ["class_1"] * len(request.spectra),
                "probabilities": [0.95] * len(request.spectra)
            },
            message="ML分析完成"
        )
    except Exception as e:
        logger.error(f"ML分析失败: {e}")
        raise HTTPException(status_code=500, detail=str(e))


# AI报告生成端点
@app.post("/api/v1/report", response_model=AnalysisResponse)
async def generate_report(request: AnalysisRequest):
    """使用Ollama生成分析报告"""
    try:
        logger.info(f"收到报告生成请求")

        # TODO: 调用Ollama API
        # import requests
        # response = requests.post(
        #     "http://pyramex-ollama:11434/api/generate",
        #     json={"model": "qwen:7b", "prompt": prompt}
        # )

        return AnalysisResponse(
            status="success",
            results={
                "report": "这是由AI生成的分析报告..."
            },
            message="报告生成完成"
        )
    except Exception as e:
        logger.error(f"报告生成失败: {e}")
        raise HTTPException(status_code=500, detail=str(e))


# 启动入口
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
