SavorAI: AI-driven Food Selection For Every Plate 🍽️🤖

Overview

SavorAI is an intelligent restaurant menu assistant that leverages AI to provide personalized food recommendations and enhance dining experiences. By analyzing uploaded menu PDFs, the application offers context-aware suggestions, dietary considerations, and interactive dining guidance.

Features

• 📄 PDF Menu Upload: Easily upload restaurant menus
• 🧠 Intelligent Recommendations: AI-powered food suggestions
• 💬 Conversational Interface: Natural language interactions
• 🔍 Context-Aware Responses: Understands menu-related and casual queries
• 🌿 Dietary Preference Support: Considers dietary restrictions and preferen

Project Structure

SavorAI 
│── Data/                # Folder containing restaurant menu documents  
│── venv/                # Virtual environment (not included in the repository)  
│── .gitignore           # Git ignore file  
│── app.py               # Main application file  
│── LICENSE              # License information  
│── README.md            # Project documentation  
│── requirements.txt     # Dependencies  

Project Context

Many students in my college struggle to decide what food to order within a specific price range or based on their mood. To make their dining experience easier, I collected the menus of all nearby restaurants and built this AI-powered application to provide personalized food recommendations. The menu for the restaurants near my college are in this Drive

📌 You can also contribute by adding your own restaurant menus!

Setup Instructions

Create a Virtual Environment

python -m venv venv
source venv/bin/activate    # On macOS/Linux
venv\Scripts\activate       # On Windows

Install Dependencies

pip install -r requirements.txt

Install and Run Ollama

Ollama is required to run the LLM locally.

• Download and install Ollama: Ollama Official Site
• Start Ollama:

ollama run

Run the Project

python app.py

How It Works

• Ingest Menus: The application loads menu documents from the Data/ directory.
• User Query: The user provides food preferences.
• RAG Process: The model retrieves relevant menu items and generates suggestions.
• Output: A personalized list of food recommendations.

License

This project is licensed under the GNU GENERAL PUBLIC LICENSE .