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What technologies are used for this project?

This project is built with:

• Vite
• TypeScript
• React
• shadcn-ui
• Tailwind CSS

Fameuxarte

Connecting local artists with global audiences through AI-powered art discovery

📋 Table of Contents

• Overview
• Screenshots
• Tech Stack
• AI & ML Integration
• Features
• Installation & Setup
• Project Structure
• Contributing
• License
• Contact

🎨 Overview

Fameuxarte is an innovative AI-powered art eCommerce platform designed to bridge the gap between talented local artists and art enthusiasts worldwide. Our mission is to democratize art discovery and sales, making it easier for artists to showcase their work and for collectors to find unique, handmade artworks that resonate with them.

In a world where mass-produced art dominates retail spaces, Fameuxarte creates a digital gallery experience that celebrates authenticity and craftsmanship. Our platform leverages artificial intelligence to connect users with artworks that match their preferences, while providing artists with tools to reach a broader audience than traditional galleries allow.

Whether you're an artist looking to expand your reach or an art lover searching for that perfect piece, Fameuxarte offers a seamless, personalized experience that honors the value of genuine artistic expression.

📸 Screenshots

Fameuxarte homepage featuring curated collections and featured artists

Artist profile page showcasing portfolio and biography

Browse page with AI-powered filtering and recommendation system

Secure checkout process with multiple payment options

💻 Tech Stack

Frontend

• Next.js: React framework for server-side rendering and static site generation
• React: JavaScript library for building user interfaces
• Tailwind CSS: Utility-first CSS framework for custom designs
• Redux: State management for complex UI interactions
• Framer Motion: Animation library for enhanced user experience

Backend

• Django: High-level Python web framework
• Django REST Framework: Toolkit for building Web APIs
• Celery: Distributed task queue for background processing
• Django Channels: WebSocket support for real-time features

Database

• PostgreSQL: Primary relational database
• Redis: In-memory data structure store for caching and session management
• Vector Database: For storing and querying artwork embeddings

DevOps & Infrastructure

• Docker: Containerization for consistent development and deployment
• Nginx: Web server for static content and reverse proxy
• AWS/GCP: Cloud hosting and services

Payment Processing

• PayPal: Alternative payment method

🧠 AI & ML Integration

Fameuxarte is integrating cutting-edge AI/ML technologies to create a more personalized and visually intuitive experience for art lovers. Our AI integration focuses on three core areas:

1. Recommendation Engine

We're building a sophisticated recommendation system using a hybrid approach that combines:

• Content-Based Filtering: Using TensorFlow to analyze artwork attributes (style, color palette, subject matter, medium) and match them with user preferences.

• Collaborative Filtering: Implementing matrix factorization with PyTorch to identify patterns in user behavior and recommend artworks based on similar users' preferences.

• Deep Learning Models: Utilizing neural networks to understand complex relationships between artwork features and user interactions:

  • Convolutional Neural Networks (CNNs) for image feature extraction
  • Recurrent Neural Networks (RNNs) for sequential user behavior analysis
  • Transformer models for contextual understanding of art descriptions

The recommendation engine processes multiple data points:

• User browsing history
• Purchase patterns
• Explicit ratings and favorites
• Time spent viewing specific artworks
• Demographic information
• Stated preferences from user profiles

2. Computer Vision for Art Analysis

Our computer vision pipeline leverages several technologies:

• Automatic Artwork Categorization:

  • Style classification (Impressionist, Abstract, Contemporary, etc.)
  • Subject matter detection (Landscape, Portrait, Still Life, etc.)
  • Medium identification (Oil, Acrylic, Watercolor, Digital, etc.)
  • Color palette extraction and mood analysis
  • Composition analysis (Rule of thirds, golden ratio, etc.)

• Visual Search Capabilities:

  • "Find similar artworks" functionality
  • Search by uploading an image
  • Color-based search
  • Texture and pattern matching
  • Style transfer previews ("Show me this landscape in an impressionist style")

• Authentication and Verification:

  • Artwork signature verification
  • Forgery detection algorithms
  • Provenance tracking

3. Natural Language Processing

• Intelligent Search:

  • Semantic understanding of search queries
  • Art-specific entity recognition
  • Contextual query expansion

• Automated Content Generation:

  • Artwork description enhancement
  • SEO-optimized content suggestions for artists
  • Multilingual translation of artwork details

Technical Implementation

Our AI/ML infrastructure includes:

• Model Training Pipeline:

  • Data collection and preprocessing workflows
  • Feature engineering for artwork attributes
  • Transfer learning from pre-trained art recognition models
  • Continuous model improvement through feedback loops

• Deployment Architecture:

  • Model serving via TensorFlow Serving
  • Real-time inference API endpoints
  • Batch processing for recommendation updates
  • A/B testing framework for algorithm optimization

• Integration Points:

  • Recommendation microservice with REST API
  • Computer vision processing queue
  • User preference learning system
  • Artist analytics dashboard

✨ Features

Completed Features

• User Authentication

  • Secure signup/login system
  • Social media authentication
  • Role-based access control (buyers, artists, admins)

• Artist Profiles

  • Customizable portfolio pages
  • Artist biography and statement
  • Exhibition history and credentials
  • Commission availability settings

• Artwork Listings

  • High-resolution image galleries
  • Detailed artwork information (medium, dimensions, year)
  • Price and availability management
  • Categorization and tagging system

• Shopping Experience

  • Intuitive shopping cart
  • Secure checkout process
  • Order tracking
  • Wishlist functionality

• Content Pages

  • Blog with art news and features
  • About page with platform mission
  • Contact form for inquiries
  • Gallery of featured collections

AI-Enhanced Features (In Development)

• Personalized Art Discovery

  • AI-curated collections based on user preferences
  • "Art DNA" profile that learns from user interactions
  • Style affinity detection
  • Personalized homepage featuring recommended artworks
  • "Weekly Discoveries" email with new art recommendations

• Visual Search and Exploration

  • Search by image upload ("Find art like this")
  • Color palette-based search
  • Style-based filtering with visual examples
  • "Art Journey" feature that creates paths of related artworks
  • Mood-based recommendations

• Artist Intelligence Tools

  • Market trend analysis for pricing guidance
  • Audience insights dashboard
  • Style analysis of artist's portfolio
  • Recommendation optimization suggestions
  • Comparative performance metrics

• Enhanced Artwork Presentation

  • Automatic tagging and categorization
  • Smart cropping for optimal thumbnail generation
  • AR preview of artwork in customer's space
  • Style-matched framing recommendations
  • Lighting condition simulations

Additional Upcoming Features

• Enhanced Artist Tools

  • Analytics dashboard for artists
  • Automated pricing assistance
  • Marketing toolkit for promotion

• Virtual Gallery Experience

  • 3D virtual exhibition spaces
  • AR view to preview art in your space
  • Virtual gallery tours

• Community Features

  • Artist collaboration opportunities
  • Collector profiles and public collections
  • Art events calendar and ticketing

• Mobile Applications

  • Native iOS and Android apps
  • Mobile-optimized browsing and purchasing

🚀 Installation & Setup

Prerequisites

• Python 3.8+
• Node.js 14+
• PostgreSQL 12+
• pip and npm package managers
• TensorFlow 2.x and PyTorch (for AI features) coming soon
• CUDA-compatible GPU (recommended for model training)

Backend Setup

# Clone the repository
git clone https://github.com/yourusername/fameuxarte.git
cd fameuxarte

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install AI/ML dependencies
pip install -r requirements-ai.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Run migrations
python manage.py migrate

# Create superuser
python manage.py createsuperuser

# Start Django development server
python manage.py runserver

Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Run development server
npm run dev

AI Model Setup

# Download pre-trained models
python scripts/download_models.py

# Run initial model training (optional)
python scripts/train_recommendation_model.py

# Start model serving
python scripts/start_model_server.py

Running with Docker

# Build and start containers
docker-compose up -d

# Run migrations
docker-compose exec backend python manage.py migrate

# Create superuser
docker-compose exec backend python manage.py createsuperuser

📁 Project Structure

Backend (Django)

fameuxarte/
├── fameuxarte/          # Project settings
├── accounts/            # User authentication and profiles
├── artists/             # Artist profiles and portfolios
├── artworks/            # Artwork listings and management
├── cart/                # Shopping cart functionality
├── checkout/            # Order processing and payment
├── blog/                # Blog posts and articles
├── api/                 # REST API endpoints
├── recommendations/     # AI recommendation engine
│   ├── models/          # ML model definitions
│   ├── training/        # Training scripts and data pipelines
│   ├── inference/       # Inference services
│   └── evaluation/      # Model evaluation tools
├── vision/              # Computer vision services
│   ├── categorization/  # Artwork categorization models
│   ├── search/          # Visual search implementation
│   └── processing/      # Image processing utilities
└── utils/               # Utility functions and helpers

Frontend (Next.js)

frontend/
├── pages/               # Application pages
├── components/          # Reusable React components
│   ├── ai/              # AI-powered UI components
│   └── recommendations/ # Recommendation display components
├── public/              # Static assets
├── styles/              # CSS and styling
├── lib/                 # Utility functions
├── store/               # Redux store configuration
├── hooks/               # Custom React hooks
└── contexts/            # React context providers

AI/ML Components

ml/
├── models/              # Model architecture definitions
├── data/                # Data processing and pipelines
├── training/            # Training scripts and configurations
├── evaluation/          # Evaluation metrics and tools
├── serving/             # Model serving infrastructure
└── notebooks/           # Jupyter notebooks for experimentation

API Endpoints

• /api/auth/ - Authentication endpoints
• /api/artists/ - Artist profile management
• /api/artworks/ - Artwork CRUD operations
• /api/cart/ - Shopping cart operations
• /api/orders/ - Order management
• /api/blog/ - Blog content
• /api/recommendations/ - AI recommendation endpoints
• /api/vision/ - Computer vision services
• /api/search/ - Advanced search capabilities

👥 Contributing

We welcome contributions to Fameuxarte! Whether you're fixing bugs, adding features, or improving documentation, your help is appreciated.

How to Contribute

1. Fork the repository
2. Create a feature branch

   git checkout -b feature/amazing-feature

3. Commit your changes

   git commit -m 'Add some amazing feature'

4. Push to the branch

   git push origin feature/amazing-feature

5. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guide for Python code
• Use ESLint and Prettier for JavaScript/React code
• Write tests for new features
• Update documentation as needed
• For AI/ML contributions:
  • Include model cards for new models
  • Document training procedures and hyperparameters
  • Provide evaluation metrics
  • Consider model efficiency and performance

Reporting Issues

• Use the GitHub issue tracker
• Provide detailed reproduction steps
• Include relevant logs and screenshots

📄 License

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

📞 Contact

Project Maintainer: Your Name - arunkumar.ai.engineer@example.com

Social Media

• Twitter
• Instagram
• LinkedIn

Community

• Discord Server
• Artist Forum

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Made with ❤️ for artists and art lovers everywhere