"""
FastAPI HTTP 推理服务

提供 REST API 识别验证码:
    POST /solve  - 上传图片，返回识别结果
    GET  /health - 健康检查

启动方式:
    uv run python cli.py serve --port 8080

请求示例:
    curl -X POST http://localhost:8080/solve -F "image=@captcha.png"

响应示例:
    {"type": "normal", "result": "A3B8", "confidence": 0.95, "time_ms": 45}
"""

from __future__ import annotations


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

    cli.py 的 cmd_serve 依赖此签名。
    需要安装 server 可选依赖: uv sync --extra server
    """
    from fastapi import FastAPI, File, Query, UploadFile
    from fastapi.responses import JSONResponse

    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: CaptchaPipeline | None = None

    @app.on_event("startup")
    def _load_models():
        nonlocal pipeline
        try:
            pipeline = CaptchaPipeline()
        except FileNotFoundError:
            # 模型未导出时允许启动，但 /solve 会返回 503
            pipeline = None

    @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(
        image: UploadFile = File(...),
        type: str | None = Query(None, description="指定类型跳过分类"),
    ):
        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"},
            )

        data = await image.read()
        if not data:
            return JSONResponse(
                status_code=400,
                content={"error": "空文件"},
            )

        try:
            result = pipeline.solve(data, captcha_type=type)
        except RuntimeError as e:
            return JSONResponse(
                status_code=400,
                content={"error": str(e)},
            )

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

    return app