Hua
788ddcae1a
- Add 57 unit tests covering generators, models, and pipeline components - Implement FastAPI HTTP service (server.py) with POST /solve and GET /health - Add checkpoint resume (断点续训) to both CTC and regression training utils - Fix device mismatch bug in CTC training (targets/input_lengths on GPU) - Add pytest dev dependency to pyproject.toml - Update .gitignore with data/solver/, data/real/, *.log - Remove PyCharm template main.py - Update training/__init__.py docs for solver training scripts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
"""
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FastAPI HTTP 推理服务
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提供 REST API 识别验证码:
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POST /solve - 上传图片，返回识别结果
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GET /health - 健康检查
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启动方式:
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uv run python cli.py serve --port 8080
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请求示例:
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curl -X POST http://localhost:8080/solve -F "image=@captcha.png"
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响应示例:
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{"type": "normal", "result": "A3B8", "confidence": 0.95, "time_ms": 45}
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"""
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from __future__ import annotations
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def create_app():
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"""
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创建 FastAPI 应用实例（工厂函数）。
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cli.py 的 cmd_serve 依赖此签名。
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需要安装 server 可选依赖: uv sync --extra server
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"""
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from fastapi import FastAPI, File, Query, UploadFile
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from fastapi.responses import JSONResponse
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from inference.pipeline import CaptchaPipeline
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app = FastAPI(
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title="CaptchaBreaker",
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description="验证码识别多模型系统 - HTTP 推理服务",
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version="0.1.0",
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)
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pipeline: CaptchaPipeline | None = None
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@app.on_event("startup")
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def _load_models():
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nonlocal pipeline
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try:
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pipeline = CaptchaPipeline()
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except FileNotFoundError:
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# 模型未导出时允许启动，但 /solve 会返回 503
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pipeline = None
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@app.get("/health")
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def health():
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models_loaded = pipeline is not None
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return {"status": "ok" if models_loaded else "no_models", "models_loaded": models_loaded}
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@app.post("/solve")
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async def solve(
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image: UploadFile = File(...),
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type: str | None = Query(None, description="指定类型跳过分类"),
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):
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if pipeline is None:
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return JSONResponse(
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status_code=503,
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content={"error": "模型未加载，请先训练并导出 ONNX 模型"},
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)
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data = await image.read()
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if not data:
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return JSONResponse(
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status_code=400,
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content={"error": "空文件"},
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)
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try:
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result = pipeline.solve(data, captcha_type=type)
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except RuntimeError as e:
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return JSONResponse(
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status_code=400,
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content={"error": str(e)},
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)
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return {
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"type": result["type"],
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"result": result["result"],
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"raw": result["raw"],
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"time_ms": result["time_ms"],
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}
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return app
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