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FastMCP_RecSys

这是一个基于 CLIP 且带有 MCP 的时尚推荐器。

小样

用户上传服装图片 → YOLO 检测服装 → CLIP 编码 → 推荐类似服装

文件夹结构

/project-root │ ├── /backend │ ├── Dockerfile │ ├── /app │ │ ├── server.py # FastAPI app code │ │ ├── /routes │ │ │ └── clothing_routes.py │ │ ├── /controllers │ │ │ └── clothing_controller.py │ │ │ └──clothing_tagging.py │ │ │ └── tag_extractor.py # Pending: define core CLIP functionality │ │ ├── schemas/ │ │ │ └── clothing_schemas.py │ │ ├── config/ │ │ │ └── tag_list_en.py $ Tool for mapping: https://jsoncrack.com/editor │ │ │ └── database.py │ │ │ └── settings.py │ │ │ └── api_keys.py │ │ └── requirements.txt │ └── .env │ ├── /fastmcp │ └── app │ └── server.py │ ├── /frontend │ ├── Dockerfile │ ├── package.json │ ├── package-lock.json │ ├── /public │ │ └── index.html │ ├── /src │ │ ├── /components │ │ │ ├── ImageUpload.jsx │ │ │ ├── DetectedTags.jsx │ │ │ └── Recommendations.jsx │ │ ├── /utils │ │ │ └── api.js │ │ ├── App.js # Main React component │ │ ├── index.js │ │ ├── index.css │ │ ├── tailwind.config.js │ │ ├── postcss.config.js │ │ └── .env │ ├── .gitignore │ ├── docker-compose.yml │ └── README.md └────── requirements.txt

快速入门指南

步骤 1:克隆 GitHub 项目

第 2 步:设置 Python 环境

python -m venv venv source venv/bin/activate # On macOS or Linux venv\Scripts\activate # On Windows

步骤3:安装依赖项

pip install -r requirements.txt

步骤 4:启动 FastAPI 服务器(后端)

uvicorn backend.app.server:app --reload

一旦服务器运行并且数据库连接,您应该在控制台中看到以下消息:

Database connected INFO: Application startup complete.

步骤5:安装依赖项

数据库连接信息:应用程序启动完成。

npm install

步骤 6:启动开发服务器(前端)

npm start

一旦运行,服务器就会记录确认并在浏览器中打开应用程序: http://localhost:3000/

📌 UI 示例组件

  1. 图片上传

  2. 提交按钮

  3. 显示服装标签+推荐

目前已完成的工作:

  1. FastAPI 服务器已启动并运行(4 月 24 日)

  2. 数据库连接已建立(4 月 24 日)

  3. 后端架构已正常运行(4 月 24 日)

  4. 上传图片的基本前端 UI(4 月 25 日)

下一步:

  1. 评估 CLIP 对样本服装图像的标记准确率

  2. 微调标记系统以获得更好的推荐

  3. 使用实时用户数据测试后端集成

  4. 设置模型性能监控

  5. 前端演示

-
security - not tested
A
license - permissive license
-
quality - not tested

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