"""
FastAPI HTTP 推理服务 (纯推理，不依赖 torch/训练代码)

仅依赖: fastapi, uvicorn, python-multipart, onnxruntime, pillow, numpy

API:
    POST /solve         JSON base64 图片识别
    POST /solve/upload  multipart 文件上传识别
    GET  /health        健康检查

启动:
    uv sync --extra server
    python cli.py serve --port 8080

请求示例 (base64):
    curl -X POST http://localhost:8080/solve \
      -H "Content-Type: application/json" \
      -d '{"image": "<base64>", "type": "normal"}'

请求示例 (文件上传):
    curl -X POST http://localhost:8080/solve/upload -F "image=@captcha.png"

响应:
    {"type": "normal", "result": "A3B8", "raw": "A3B8", "time_ms": 12.3}
"""

import base64


def create_app():
    """
    创建 FastAPI 应用实例（工厂函数）。

    cli.py 的 cmd_serve 依赖此签名。
    """
    from typing import Optional

    from fastapi import FastAPI, File, Query, UploadFile
    from fastapi.responses import JSONResponse
    from pydantic import BaseModel

    from inference.pipeline import CaptchaPipeline

    app = FastAPI(
        title="CaptchaBreaker",
        description="验证码识别多模型系统 - HTTP 推理服务",
        version="0.1.0",
    )

    pipeline: Optional[CaptchaPipeline] = None

    # ---- 启动时加载模型 ----
    @app.on_event("startup")
    def _load_models():
        nonlocal pipeline
        try:
            pipeline = CaptchaPipeline()
        except FileNotFoundError:
            pipeline = None

    # ---- 请求体定义 ----
    class SolveRequest(BaseModel):
        image: str  # base64 编码的图片
        type: Optional[str] = None  # 指定类型可跳过分类

    # ---- 通用推理逻辑 ----
    def _solve(image_bytes: bytes, captcha_type: Optional[str]) -> dict:
        if pipeline is None:
            return JSONResponse(
                status_code=503,
                content={"error": "模型未加载，请先训练并导出 ONNX 模型"},
            )
        if not image_bytes:
            return JSONResponse(status_code=400, content={"error": "空图片"})

        try:
            result = pipeline.solve(image_bytes, captcha_type=captcha_type)
        except (RuntimeError, TypeError) as e:
            return JSONResponse(status_code=400, content={"error": str(e)})
        except Exception as e:
            return JSONResponse(status_code=400, content={"error": f"图片解析失败: {e}"})

        return {
            "type": result["type"],
            "result": result["result"],
            "raw": result["raw"],
            "time_ms": result["time_ms"],
        }

    # ---- 路由 ----
    @app.get("/health")
    def health():
        models_loaded = pipeline is not None
        return {
            "status": "ok" if models_loaded else "no_models",
            "models_loaded": models_loaded,
        }

    @app.post("/solve")
    async def solve_base64(req: SolveRequest):
        """JSON 请求，图片通过 base64 传输。"""
        try:
            image_bytes = base64.b64decode(req.image)
        except Exception:
            return JSONResponse(
                status_code=400,
                content={"error": "base64 解码失败"},
            )
        return _solve(image_bytes, req.type)

    @app.post("/solve/upload")
    async def solve_upload(
        image: UploadFile = File(...),
        type: Optional[str] = Query(None, description="指定类型跳过分类"),
    ):
        """multipart 文件上传。"""
        data = await image.read()
        return _solve(data, type)

    return app