FastMCP_RecSys
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
图片上传
提交按钮
显示服装标签+推荐
目前已完成的工作:
FastAPI 服务器已启动并运行(4 月 24 日)
数据库连接已建立(4 月 24 日)
后端架构已正常运行(4 月 24 日)
上传图片的基本前端 UI(4 月 25 日)
下一步:
评估 CLIP 对样本服装图像的标记准确率
微调标记系统以获得更好的推荐
使用实时用户数据测试后端集成
设置模型性能监控
前端演示
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