import os
import uuid
import requests
from typing import Optional
from contextlib import asynccontextmanager

import torch
import matplotlib
# 关键：设置非交互式后端，避免服务器环境下报错
matplotlib.use('Agg') 
import matplotlib.pyplot as plt

from fastapi import FastAPI, File, UploadFile, Form, HTTPException, Request
from fastapi.staticfiles import StaticFiles
from fastapi.responses import JSONResponse
from PIL import Image

# SAM3 相关导入 (请确保你的环境中已正确安装 sam3)
from sam3.model_builder import build_sam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor
from sam3.visualization_utils import plot_results

# ------------------- 配置与路径 -------------------
STATIC_DIR = "static"
RESULT_IMAGE_DIR = os.path.join(STATIC_DIR, "results")
os.makedirs(RESULT_IMAGE_DIR, exist_ok=True)

# ------------------- 生命周期管理 -------------------
@asynccontextmanager
async def lifespan(app: FastAPI):
    """
    FastAPI 生命周期管理器：在服务启动时加载模型，关闭时清理资源
    """
    print("正在加载 SAM3 模型到 GPU...")
    
    # 1. 检测设备
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    if not torch.cuda.is_available():
        print("警告: 未检测到 GPU，将使用 CPU，速度会较慢。")

    # 2. 加载模型 (全局单例)
    model = build_sam3_image_model()
    model = model.to(device)
    model.eval() # 切换到评估模式

    # 3. 初始化 Processor
    processor = Sam3Processor(model)

    # 4. 存入 app.state 供全局访问
    app.state.model = model
    app.state.processor = processor
    app.state.device = device
    
    print(f"模型加载完成，设备: {device}")
    
    yield  # 服务运行中...
    
    # 清理资源 (如果需要)
    print("正在清理资源...")

# ------------------- FastAPI 初始化 -------------------
app = FastAPI(lifespan=lifespan, title="SAM3 Segmentation API")

# 挂载静态文件目录，用于通过 URL 访问生成的图片
app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")

# ------------------- 辅助函数 -------------------
def load_image_from_url(url: str) -> Image.Image:
    """从网络 URL 下载图片"""
    try:
        headers = {'User-Agent': 'Mozilla/5.0'}
        response = requests.get(url, headers=headers, stream=True, timeout=10)
        response.raise_for_status()
        image = Image.open(response.raw).convert("RGB")
        return image
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"无法下载图片: {str(e)}")

def generate_and_save_result(image: Image.Image, inference_state) -> str:
    """生成可视化结果图并保存，返回文件名"""
    # 生成唯一文件名防止冲突
    filename = f"seg_{uuid.uuid4().hex}.jpg"
    save_path = os.path.join(RESULT_IMAGE_DIR, filename)

    # 绘图 (复用你提供的逻辑)
    plot_results(image, inference_state)
    
    # 保存
    plt.savefig(save_path, dpi=150, bbox_inches='tight')
    plt.close() # 务必关闭，防止内存泄漏

    return filename

# ------------------- API 接口 -------------------
@app.post("/segment")
async def segment(
    request: Request,
    prompt: str = Form(...),
    file: Optional[UploadFile] = File(None),
    image_url: Optional[str] = Form(None)
):
    """
    接收图片 (文件上传 或 URL) 和 文本提示词，返回分割后的图片 URL。
    """
    
    # 1. 校验输入
    if not file and not image_url:
        raise HTTPException(status_code=400, detail="必须提供 file (图片文件) 或 image_url (图片链接)")

    # 2. 获取图片对象
    try:
        if file:
            image = Image.open(file.file).convert("RGB")
        elif image_url:
            image = load_image_from_url(image_url)
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"图片解析失败: {str(e)}")

    # 3. 获取模型
    processor = request.app.state.processor

    # 4. 执行推理
    try:
        # 这一步内部应该已经由 Sam3Processor 处理了 GPU 张量转移
        inference_state = processor.set_image(image)
        output = processor.set_text_prompt(state=inference_state, prompt=prompt)
        
        masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"模型推理错误: {str(e)}")

    # 5. 生成可视化并保存
    try:
        filename = generate_and_save_result(image, inference_state)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"绘图保存错误: {str(e)}")

    # 6. 构建返回 URL
    # request.url_for 会自动根据当前域名生成正确的访问链接
    file_url = request.url_for("static", path=f"results/{filename}")

    return JSONResponse(content={
        "status": "success",
        "result_image_url": str(file_url),
        "detected_count": len(masks),
        "scores": scores.tolist() if torch.is_tensor(scores) else scores
    })

if __name__ == "__main__":
    import uvicorn
    
    # 使用 Python 函数参数的方式传递配置
    uvicorn.run(
        "fastAPI_main:app",  # 注意：这里要改成你的文件名:app对象名
        host="0.0.0.0",
        port=55600,
        proxy_headers=True,                # 对应 --proxy-headers
        forwarded_allow_ips="*"            # 对应 --forwarded-allow-ips="*"
    )