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Artisan Pizza AI

The Future of Taste: Traditional Wood-Fired Excellence meets Artificial Intelligence

🍕 Live Site • 📊 AI Dashboard • Architecture • Setup Guide


Artisan Pizza AI is a premium, AI-driven platform designed to revolutionize the way you experience pizza. From our wood-fired kitchens in Kampala to our deep-learning taste recommendations, we engineer every bite for perfection.

Project Structure

  • frontend/: High-performance multi-page application
    • css/: Centralized professional branding
    • js/: Persistent cart and AI assistant logic
    • assets/: Custom SVG branding and media
  • streamlit/: Modular dev tools and documentation dashboard
  • api/: FastAPI backend powering the order management system
  • pizza_agent/: Core LLaMA 3.1-powered intelligence and tools

Key Features

  • AI Taste Assistant: Personalized recommendations using advanced NLP.
  • Universal Persistence: Conversations and carts stay with you across all pages.
  • Localized Experience: Now serving Ugandan communities in Kampala, Entebbe, and Jinja.
  • Premium Interface: Minimalist design with Lucide integration and custom notifications.

Local Setup

  1. Clone and Install:
    pip install -r requirements.txt
  2. Configure: Create a .env file with your GROQ_API_KEY.
  3. Launch:
    python launch.py

Deployment

Frontend — Vercel

Live at https://pizza-customer-service-agent.vercel.app/

To deploy your own fork:

  1. Import the repo on vercel.com
  2. Vercel auto-detects vercel.json and serves the frontend/ folder
  3. Every push to main triggers an automatic redeploy

AI Dashboard — Streamlit Cloud

Live at https://pizza-customerservice-agent.streamlit.app/

To deploy your own fork:

  1. Link the repo on streamlit.io/cloud
  2. Set the main file path to streamlit/app.py
  3. Add your GROQ_API_KEY under the app Secrets

Built with precision in Uganda by Artisan AI Lab

About

Artisan Pizza AI - Customer Service Agent A premium, AI-powered customer service platform for a modern pizzeria. Featuring a high-performance static frontend and a modular Streamlit documentation/tool dashboard.

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