Description

GroceryTracker is an innovative web application that demonstrates the power of multimodal AI models for practical everyday use. This project leverages the cutting-edge Mixtral Pixtral 12B 2409 model to automatically extract and organize grocery receipt information, transforming a simple photo into structured, actionable data.

Built as a React application and deployed on Hugging Face Spaces, GroceryTracker represents an exploration into the capabilities of modern multimodal AI systems for real-world document processing and data extraction tasks.

Key Features

  • Intelligent Receipt Scanning: Upload receipt images and let AI extract all relevant information
  • Multimodal Processing: Utilizes Pixtral 12B’s vision and language capabilities for comprehensive data extraction
  • Automatic Data Extraction: Captures products, prices, purchase dates, and store information
  • Structured Output: Organizes extracted data for easy storage and analysis
  • Real-time Processing: Fast AI-powered analysis with immediate results
  • User-friendly Interface: Clean React-based UI for seamless user experience

Technologies Used

  • React - Frontend framework for building the user interface
  • Mixtral Pixtral 12B 2409 - Advanced multimodal AI model for image and text processing
  • Hugging Face Spaces - Deployment platform for AI applications
  • Computer Vision - Image processing and optical character recognition
  • JavaScript/TypeScript - Core programming languages
  • CSS/Tailwind - Styling and responsive design
  • Node.js - Backend runtime environment

About Pixtral 12B

The project specifically utilizes Mixtral Pixtral 12B 2409, a state-of-the-art multimodal AI model featuring:

  • 12B parameter multimodal decoder + 400M parameter vision encoder
  • Native multimodal training with interleaved image and text data
  • Variable image sizes support without resizing or padding
  • 128K token context window for processing large amounts of content
  • Leading performance in multimodal benchmarks including document understanding
  • Apache 2.0 License enabling open research and development

Technical Implementation

The application demonstrates several advanced AI concepts:

  1. Multimodal Understanding: Combining visual and textual information processing
  2. Document AI: Specialized handling of receipt formats and layouts
  3. Data Extraction Pipeline: Converting unstructured images to structured data
  4. Real-time Inference: Efficient processing for immediate user feedback
  5. Web-based AI: Deploying advanced AI models in accessible web applications

Use Cases

  • Personal Finance Tracking: Automated expense logging and categorization
  • Grocery Budget Management: Understanding spending patterns and habits
  • Receipt Digitization: Converting paper receipts to digital records
  • AI Model Testing: Evaluating multimodal AI capabilities on real-world data
  • Prototype Development: Foundation for larger expense tracking applications

Live Demo

Experience GroceryTracker in action on Hugging Face Spaces, where you can upload your own receipt images and see the multimodal AI in action.

This project showcases the practical applications of modern AI technology in solving everyday problems, demonstrating how multimodal models can bridge the gap between physical documents and digital data management.