A newer version of the Gradio SDK is available:
5.49.1
title: Min StyleMatch AI
emoji: 📉
colorFrom: gray
colorTo: green
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: LLM-Powered Styling & Color Analysis via MCP Server + Gradio
tags:
- Agents-MCP-Hackathon
- agent-demo-track
- mcp-server-track
Project Name: Min-StyleMatch AI – LLM-Powered Styling & Color Analysis via MCP Server + Gradio Client
🎬 Demo Video
📚 Table of Contents
- 🧠 Project Overview
- 🔧 Tools Integrated
- ⚠️ Project Status
- 🔍 Models Used
- 🧩 System Architecture Overview
- 🚀 Quickstart
- 🔐 Your Data & Privacy
- ✅ Dataset Overview
🧠 Project Overview
This project uses FastMCP for custom MCP (Model Context Protocol) server and a Gradio Chatbot as an MCP Client, designed to demonstrate how Language Models (LLMs) can interact with tools for advanced reasoning and personalization.
🔧 Tools Integrated
🧑💻 Developer Info Tool
- Returns information about the developer (you).
- Purpose: Demonstrates how MCP tool calling supports prompt decomposition, allowing the LLM to delegate parts of the task to improve response accuracy and structure.
🛍️ Product Filtering Tool
- Connects the LLM with product data stored in
.csvfiles (used as a lightweight database). - Purpose: Showcases how LLMs can support product recommendation systems by filtering based on user preferences, color suitability, or product attributes.
- Connects the LLM with product data stored in
🎨 Color Season Analysis Tool
Operates in an Agent-based setup using two LLMs:
- One extracts facial feature colors (e.g., skin, eyes, hair, lips) from a user-uploaded image.
- The second uses this data to predict the user’s color season.
Purpose: Demonstrates multi-model collaboration, improving accuracy by separating image understanding from season classification. (Combining both steps into one model often resulted in inconsistent or random outputs.)
⚠️ Note: This project is not production-ready. It is a demo prototype built to:
- Showcase how to integrate product data with LLMs using tool calling.
- Demonstrate multi-model collaboration (e.g., using image and text models in tandem).
- Illustrate prompt decomposition and how MCP (Model Context Protocol) enables modular reasoning through tool-based workflows.
While functional, the current version has non-optimized system flow, basic tool logic, and initial prompt configurations. These areas can be significantly improved for better performance, scalability, and production-readiness.
This project is intended as a learning resource and a practical starting point for junior developers, LLM enthusiasts, or teams exploring how to combine AI tools with real-world datasets.
With future iterations—such as improved orchestration, tighter prompt design, and scalable backend architecture—it can evolve into a robust, intelligent personalization system.
🔍 Models Used
📦 Open-Source Models (via Nebius)
- Qwen2.5-VL-72B-Instruct – For visual-linguistic processing.
- Mistral-Nemo-Instruct-2407 – For general instruction-following tasks.
🔐 Closed-Source Models (via OpenAI)
- GPT-4o-mini (primary for tool-calling)
- GPT-4.1-mini
⚠️ Note: Currently, only OpenAI models are used for tool calling, as their API is more mature and familiar. I have not explored whether Nebius supports tool calling.
🧩 System Architecture Overview
graph TD
User["🧑 User"]
Gradio["🤖 Gradio MCP Client"]
FastMCP["🛠️ FastMCP Server"]
Tool1["🧑💻 Developer Info Tool"]
Tool2["🛍️ Product Filter Tool"]
Tool3["🎨 Color Season Tool"]
LLM["🧠 LLM (OpenAI / Nebius)"]
User --> Gradio
Gradio --> FastMCP
FastMCP --> Tool1
FastMCP --> Tool2
FastMCP --> Tool3
Tool3 --> LLM
Tool1 --> LLM
Tool2 --> LLM
🚀 Quickstart
- Clone the repository
git clone https://github.com/yourusername/stylematch-assistant.git
cd stylematch-assistant
pip install -r requirements.txt
🔐 Your Data & Privacy
This project requires the use of the OpenAI API and optionally the Nebius AI Studio API. All API keys are encrypted and never exposed in plain text.
This is a demo project, and for your safety and control, I recommend cloning the repository and running it locally on your machine.
🧠 Chat History
- Your conversation is not stored permanently.
- Chat history will be cleared if you refresh the browser or close the session.
🖼️ Image Handling
- When you upload a face image, it is stored temporarily for analysis.
- If you're inactive or stop interacting with the assistant, the image will be automatically deleted.
- No facial image is permanently saved.
🎨 Facial Color Data
- The extracted skin, hair, eyes, lips colors, and your color season result are handled just like your image—temporary and private.
- This information is only used to enhance your personalized experience and is not retained after your session ends.
✅ Dataset Overview
This project uses the Fashion Product Images Dataset from Kaggle, which contains metadata and image filenames for over 44,000 fashion products.
The dataset consists of two key files:
styles.csv– contains product metadata such as category, gender, and usage.images.csv– contains filenames of product images, linked to the product via theidcolumn.
🔄 Data Processing Steps
The function load_combined_dataset(styles_path, images_path) performs the following operations:
Load CSV Files
- Reads
styles.csvandimages.csv. - Skips malformed lines automatically to avoid read errors.
- Reads
Prepare for Merge
- Cleans the
filenamecolumn inimages.csvby stripping.jpgand converting it into integeridfor merging.
- Cleans the
Data Cleaning
- Drops all rows with missing (
NaN) values from both DataFrames. - Merges the two DataFrames on the
idcolumn using an inner join. - Ensures the resulting combined dataset has no null values.
- Drops all rows with missing (
Filter Relevant Categories
- Since this is a demo, I keeps only the products that belong to Apparel or Footwear categories (ignores others such as Accessories).
📦 Output
The function returns a cleaned and filtered pandas.DataFrame containing only products ready for personalized styling and analysis.