Adora ML Core Model

Adora ML Core Model is a comprehensive AI-powered platform for automated creative validation, layout correction, and final ad rendering in retail media. The system provides end-to-end creative intelligence for marketing automation, integrating OCR, object detection, compliance checking, and auto-layout optimization.

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📋 Table of Contents

• 🎯 Overview
• ⭐ Key Features
• 🏗️ Architecture
• 🚀 Quick Start
• 📚 Documentation
• 🔧 Installation
• 🎨 Usage
• 🔌 API Reference
• 📊 System Requirements
• 🤝 Contributing
• 📄 License
• 🙏 Acknowledgments

🎯 Overview

Adora is designed for retail media creative management, providing AI-powered tools for:

• Smart Creative Validation: OCR, YOLO object detection, banned-phrase scanning, brand compliance
• AI Auto-Fix Engine: Automatic adjustments for font size, color, contrast, and safe-zone placement
• Background Removal: Clean product cutouts using advanced segmentation
• Multi-Format Generation: Instagram Stories, Feed posts, Facebook banners
• Version Control: Asset versioning with rollback capabilities
• Compliance Automation: Tesco brand guidelines and legal requirements validation

⭐ Key Features

🤖 AI-Powered Analysis

• Object Detection: Facebook DETR model for identifying objects and people
• OCR Processing: Tesseract-powered text extraction from images
• Color Analysis: RGB values, brightness, and contrast evaluation
• Complexity Scoring: Image detail and composition analysis
• Auto-Tagging: Intelligent content categorization

🎨 Creative Generation

• Stable Diffusion XL: High-quality image generation for advertising
• Marketing Text: AI-generated headlines, subheads, and disclaimers
• Multi-Format Support: Optimized outputs for different social platforms
• Quality Evaluation: Automated assessment of generated content

✅ Compliance & Validation

• Tesco Guidelines: Automated brand compliance checking
• Alcohol Content: Drinkaware disclaimer validation
• Accessibility: Font size and readability requirements
• Safe Zones: Platform-specific spacing validation

📊 Analytics & Monitoring

• Real-time Dashboard: Live KPIs and performance metrics
• Upload Trends: Daily and weekly activity analysis
• System Health: CPU, memory, and API response monitoring
• CSV Export: Comprehensive reporting capabilities

🔧 Developer Features

• RESTful API: Complete programmatic access via FastAPI
• Version Control: Asset history with rollback capabilities
• Batch Operations: Bulk processing for multiple assets
• Environment Configuration: Flexible deployment settings

🏗️ Architecture

System Components

Technology Stack

Backend:

• Framework: FastAPI (async Python web framework)
• Database: SQLite3 with custom ORM layer
• Authentication: JWT tokens with bcrypt password hashing
• AI/ML: PyTorch, Transformers, Diffusers, OpenCV
• OCR: Tesseract OCR engine

Frontend:

• Framework: Streamlit with custom CSS styling
• Visualization: Matplotlib, Plotly integration
• HTTP Client: Requests library for API communication

Infrastructure:

• Containerization: Docker support with multi-stage builds
• Process Management: Uvicorn ASGI server
• Logging: Rotating file handler with configurable levels
• Environment: Configurable via .env files

🚀 Quick Start

Prerequisites

• Python 3.9+
• Git
• Tesseract OCR (for text recognition)
• Optional: CUDA-compatible GPU

Installation

1. Clone the repository:

   git clone https://github.com/UjjwalSaini07/Adora-ML-CoreModel.git
   cd Adora-ML-CoreModel

2. Install Tesseract OCR:

   # Windows
   winget install -e --id UB-Mannheim.TesseractOCR
   
   # Verify installation
   tesseract --version

3. Set up Backend:

   cd backend
   
   # Create virtual environment
   python -m venv .venv
   .\.venv\Scripts\Activate.ps1
   
   # Install dependencies
   pip install -r requirements.txt
   
   # Configure environment
   cp .env.example .env

4. Set up Frontend:

   cd ../frontend
   pip install -r requirements.txt

Running the Application

Terminal 1 - Backend:

cd backend
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Terminal 2 - Frontend:

cd frontend
streamlit run streamlit_app.py

Access the application:

• Frontend UI: http://localhost:8501
• API Documentation: http://localhost:8000/docs
• API Base URL: http://localhost:8000

First Steps

1. Register a new account
2. Upload your first asset
3. Explore the dashboard analytics
4. Try AI analysis on your assets
5. Generate creative variations

📚 Documentation

Comprehensive documentation is available in the Docs/ folder:

• 📖 Project Architecture - Technical architecture and data flow
• 🚀 Quick Start Guide - Step-by-step setup instructions
• 👥 User Guide - Complete user manual and best practices
• 🔌 API Reference - Complete API documentation
• 📋 Release Notes - Version history and updates
• 📚 Documentation Hub - Central documentation index

🔧 Installation

Development Setup

# Clone repository
git clone https://github.com/UjjwalSaini07/Adora-ML-CoreModel.git
cd Adora-ML-CoreModel

# Backend setup
cd backend
python -m venv venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
cp .env.example .env

# Frontend setup
cd ../frontend
pip install -r requirements.txt

Docker Setup (Future)

# Build and run with Docker
docker-compose up --build

Production Deployment

• Configure environment variables in .env
• Set up reverse proxy (nginx)
• Configure SSL certificates
• Set up monitoring and logging
• Configure backup procedures

🎨 Usage

For Users

1. Access the web interface at http://localhost:8501
2. Register/Login with your credentials
3. Upload assets via the sidebar
4. Navigate between features using the sidebar menu:
   • Dashboard: Analytics and KPIs
   • Asset Library: Browse and manage assets
   • Image Editor: Transform images
   • AI Analysis: Intelligent insights
   • Creative Generation: Ad creation
   • Compliance Check: Validation tools

For Developers

• API Access: Use REST endpoints for integration
• Environment Config: Customize via .env files
• Batch Operations: Process multiple assets programmatically
• Version Control: Track asset changes and history

🔌 API Reference

The API provides comprehensive endpoints for all functionality:

Authentication

POST /register      # User registration
POST /login         # User authentication
GET  /me           # Current user info

Asset Management

POST /upload_packshot     # Upload single asset
POST /batch_upload        # Upload multiple assets
GET  /assets             # List all assets
GET  /asset/{id}         # Get specific asset
POST /manipulate_image    # Transform images

AI & Analysis

POST /analyze_image        # Comprehensive AI analysis
POST /generate_ad_assets   # Generate advertising creatives
POST /validate            # Content compliance
POST /validate_image      # Image compliance

System Management

POST /system_health    # System diagnostics
GET  /export_report    # CSV export
POST /generate_report  # Analytics report

Full API Documentation: http://localhost:8000/docs (when backend is running)

📊 System Requirements

Minimum Requirements

• OS: Windows 10+, macOS 10.15+, Ubuntu 18.04+
• RAM: 4GB
• Storage: 5GB free space
• Python: 3.9+
• Network: Stable internet connection

Recommended Requirements

• OS: Windows 11, macOS 12+, Ubuntu 20.04+
• RAM: 8GB+
• Storage: 20GB+ free space
• GPU: NVIDIA GPU with 4GB+ VRAM (optional, for faster AI processing)
• Python: 3.9+ with pip

AI Model Requirements

• Object Detection: ~1GB disk space
• Stable Diffusion: ~10GB disk space
• OCR Engine: Tesseract OCR installed
• CUDA: Optional, enables GPU acceleration

Walkthrough

Auth Screen	Hero Main Screen
Admin Screen 1	Admin Screen 2
Auth Screen 3	Auth Screen 4
Assets Library	Image Editing Tool
AI Analyser	AI Creative Assistance Post Generator
Version History Rollback	Advance Complaince Content Checker
Advance Complaince Image Checker	Advance Complaince Batch Checker

🤝 Contributing

We welcome contributions! Please follow these steps:

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

Development Guidelines

• Follow PEP 8 style guidelines
• Write comprehensive tests
• Update documentation for new features
• Ensure backward compatibility
• Test on multiple platforms

Code Structure

Adora-ML-CoreModel/
├── backend/              # FastAPI backend
│   ├── main.py          # Application entry point
│   ├── db.py            # Database operations
│   ├── utils.py         # Image processing utilities
│   ├── guidelines.py    # Compliance validation
│   └── requirements.txt # Python dependencies
├── frontend/            # Streamlit frontend
│   ├── streamlit_app.py # Main application
│   └── requirements.txt # Python dependencies
├── storage/             # Generated assets and data
├── Docs/               # Documentation
└── README.md           # This file

Team Details:

Arti Manral	Khushi Tyagi	Ujjwal Saini	Prateek Parija	Vedansh Hooda
Developer	Developer	Developer	Developer	Developer
Resume	Resume	Resume	Resume	Resume

📄 License

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

🙏 Acknowledgments

Core Technologies

• FastAPI - Modern, fast web framework
• Streamlit - Amazing frontend framework
• PyTorch - Deep learning framework
• Hugging Face - AI model hub
• OpenCV - Computer vision library
• Pillow - Image processing

AI Models & Libraries

• Facebook DETR - Object detection
• Stability AI SDXL - Image generation
• Tesseract OCR - Text recognition
• RemBG - Background removal

Community

• Open source contributors
• AI/ML research community
• FastAPI and Streamlit communities

📞 Support & Contact

• Documentation: Comprehensive guides in Docs/ folder
• Issues: Report bugs on GitHub Issues
• Discussions: Join community conversations
• API Docs: Interactive documentation at /docs

🏆 Version History

v1.0.0 (Current)

• Complete AI-powered creative platform
• Advanced image analysis and generation
• Compliance automation
• Real-time analytics dashboard
• Version control system

Previous Versions

• v0.9.0: Beta release with core AI features
• v0.8.0: Alpha release with basic functionality

Full changelog: See Release Notes

Built with ❤️ for the creative community

Adora ML Core Model - Transforming retail media creative workflows with AI