F
licenseNot graded
qualityD
maintenanceA FastMCP server that provides virtual try-on functionality through the Replicate API, allowing users to visualize how clothing items would look on models.
-
FastMCP_RecSys
by attarmau
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
This server cannot be deployed
Maintenance
ActivityInactive
ResponsivenessNo issues
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Virtual try-on and on-model AI fashion photography: catalog search, try-on grids, HD delivery.
Visual fashion search across retailers and resale, with localized prices and app handoffs.
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