👗 StyleMe AI – Personalized Clothing Recommendations

A modern Gradio web application that leverages Google Gemini AI to generate personalized clothing recommendations based on user profile data. The app analyzes body shape, height, gender, and clothing size to provide actionable styling advice with outfit suggestions, fit tips, color guidance, and quick styling tricks.

✨ Key Features

AI-Powered Recommendations: Uses Google Gemini 2.5 Flash model for intelligent, context-aware styling advice
Light & Dark Mode: Fully responsive UI with automatic theme detection and manual toggle
Real-Time Generation: Instant clothing recommendations with structured advice sections
Example Profiles: Pre-loaded sample inputs for quick testing and exploration
Input Validation: Graceful error handling with user-friendly feedback messages
Production-Ready: Environment-based configuration for secure API key management
Modern UI/UX: Clean, intuitive interface with gradient styling and smooth interactions
Cross-Platform: Works on Windows, macOS, and Linux

🎯 What It Does

Input your profile:

👤 Gender (Male, Female, Non-binary)
👗 Body Shape (Hourglass, Pear, Rectangle, Apple, Inverted Triangle, Athletic, Round)
📏 Height (cm or feet format)
📐 Clothing Size (XS to XXL)

Get personalized styling guidance:

Best Outfit Ideas - Tailored suggestions matching your body type
Fit & Silhouette Tips - How to flatter your unique shape
Color & Pattern Suggestions - Shades and prints that enhance your look
Quick Styling Tip - One actionable advice you can use immediately

🛠️ Tech Stack

Component	Technology
Frontend	Gradio 4.44.0+
Backend	Python 3.11
AI Model	Google Gemini 2.5 Flash
API Client	google-generativeai 0.3.2+
Web Framework	Gradio Blocks

📋 Prerequisites

Before you begin, ensure you have the following:

Python 3.11+ installed on your system
pip or conda package manager
Google Gemini API Key (Get one free here)

🚀 Installation & Setup

1. Clone the Repository

git clone https://github.com/StreetCoder02/Clothing-Recommendation-app.git
cd Clothing-Recommendation-app

2. Create and Activate Virtual Environment (Recommended)

On macOS/Linux:

python3 -m venv .venv
source .venv/bin/activate

On Windows:

python -m venv .venv
.venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Your API Key

Create a .env file in the project root (use .env.example as a template):

cp .env.example .env

Then edit .env and add your Google Gemini API key:

GEMINI_API_KEY=your_api_key_here

Alternatively, set it as an environment variable:

export GEMINI_API_KEY="your_api_key_here"  # macOS/Linux
set GEMINI_API_KEY=your_api_key_here      # Windows CMD

🎨 Running the Application

Local Development

python Clothing_Gradio.py

The app will start at http://127.0.0.1:7860

Custom Port

GRADIO_SERVER_PORT=8080 python Clothing_Gradio.py

With Virtual Environment (Complete Setup)

source .venv/bin/activate  # or .venv\Scripts\activate on Windows
python Clothing_Gradio.py

📱 Usage Guide

Fill Your Profile: Select or enter your gender, body shape, height, and clothing size
Click "Generate Recommendations": Wait for AI to process your profile
Read Your Styling Tips: Get personalized outfit and color suggestions
Try Examples: Click the example profiles to see how the app works
Toggle Theme: Use the 🌙 Dark Mode button to switch between light/dark modes

Example Profiles Included:

✨ Female, Hourglass, 168 cm, Size M
💼 Male, Rectangle, 5'10", Size L
🎭 Non-binary, Pear, 160 cm, Size S

🔐 Environment Variables

Variable	Required	Description
`GEMINI_API_KEY`	✅ Yes	Your Google Gemini API key
`GRADIO_SERVER_PORT`	❌ Optional	Port number (default: 7860)
`PORT`	❌ Optional	Alternative port variable for deployment

🌐 Deployment

Deploy to Hugging Face Spaces

Create a new Space on Hugging Face
Select Gradio as the Space SDK
Upload your repository files
Add GEMINI_API_KEY to Repository Secrets
Set Clothing_Gradio.py as the entry point
Your app will deploy automatically!

Deploy to Other Platforms

The app works on any Python hosting platform:

Railway.app - Set environment variables in dashboard
Heroku - Add Procfile with web: python Clothing_Gradio.py
Render - Add deploy configuration in dashboard
AWS Lambda - Package with serverless framework
Docker - See Docker deployment section below

🐳 Docker Deployment

Create Dockerfile

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

ENV GEMINI_API_KEY=""
ENV GRADIO_SERVER_PORT=7860

EXPOSE 7860

CMD ["python", "Clothing_Gradio.py"]

Build and Run

docker build -t styleme-ai .
docker run -e GEMINI_API_KEY="your_key" -p 7860:7860 styleme-ai

📁 Project Structure

Clothing-Recommendation-app/
├── Clothing_Gradio.py       # Main application entry point
├── requirements.txt          # Python dependencies
├── runtime.txt              # Python version specification
├── .env.example             # Environment variables template
├── .gitignore               # Git ignore rules
└── README.md                # This file

🎯 How It Works

AI Prompting Strategy

The app uses a sophisticated system prompt that:

Establishes Context: Frames the AI as an expert fashion stylist
Profiles the User: Analyzes body type, height, and size data
Structures Output: Requests specific sections (outfits, fit, colors, tips)
Ensures Tone: Keeps advice respectful, practical, and actionable

Code Flow

User Input → Validation → Prompt Generation → Gemini API Call → Response Processing → Display Output

🔧 Customization

Change AI Model

Edit line 13 in Clothing_Gradio.py:

MODEL_NAME = "gemini-2.5-flash"  # Change to other models

Available models: gemini-pro, gemini-1.5-flash, gemini-1.5-pro

Modify Body Shape Options

Edit the body shape dropdown in Clothing_Gradio.py:

body_shape = gr.Dropdown(
    ["Your", "Custom", "Shapes", "Here"],
    label="Body Shape"
)

Adjust App Title & Subtitle

APP_TITLE = "Your Custom Title"
APP_SUBTITLE = "Your custom subtitle here"

⚠️ Limitations & Known Issues

API Rate Limiting: Google Gemini has usage limits (check your plan)
Deprecated Library: google-generativeai is deprecated; consider migrating to google-genai in future
Cold Start: First request may take 2-3 seconds
Internet Required: Cannot work offline (requires API connection)

🤝 Contributing

Contributions are welcome! Here's how:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙋 Support & Questions

📖 Gradio Docs: https://gradio.app/docs
🤖 Google Gemini API: https://ai.google.dev/
🐛 Report Issues: Open an issue on GitHub
💬 Discussions: Start a discussion for questions

🌟 Acknowledgments

Gradio - Beautiful web UI framework
Google AI Studio - Gemini API access
Python - Amazing programming language

Made with ❤️ for fashion enthusiasts and tech lovers

⭐ Star this repo if you found it helpful!

Name		Name	Last commit message	Last commit date
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.env.example		.env.example
.gitignore		.gitignore
Clothing_Gradio.py		Clothing_Gradio.py
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt
runtime.txt		runtime.txt

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