MenuMate AI: A Local-AI Restaurant Menu Translator 🍽️🌿
This is my submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
What I Built
I'm a first-year Computer Science Engineering student, and I built MenuMate AI, a multilingual restaurant menu translator powered by a locally running AI model.
Restaurant owners and small cafés may need to make their menus understandable to customers who speak different languages. Professional translation can add time and cost, especially for small businesses.
MenuMate AI lets users enter a restaurant name, dish name, and description, select a target language, and generate a translation using local AI. Users can copy the translated text or download it as a text file.
Features
- Multilingual menu translation
- Local AI inference using Ollama and Qwen2.5:1.5B
- A clean, restaurant-friendly interface
- Copy and download options for translated menus
- Customizable restaurant name
My goal is to explore a practical tool that small restaurants could use to prepare multilingual menu content. This is an early prototype, and translations should be reviewed before being used commercially.
Demo
Demo video:https://drive.google.com/file/d/1ZxoJWzaPJX9U50WThRvGWyBx586lIfkp/view?usp=drivesdk
The application currently runs locally on my Windows laptop. I haven't deployed a public live version yet, so I'm sharing a demo video rather than claiming the local URL is publicly accessible.
Code
GitHub repository: https://github.com/vyshu1230/Menumate-AI
The repository contains the project code, dependencies, and setup instructions.
How I Built It
I built MenuMate AI using:
- Python for the application logic
- Streamlit for the web interface
- Ollama to run the model locally
- Qwen2.5:1.5B as the language model
The workflow is straightforward:
- The user enters a restaurant name and menu details.
- The user selects a target language.
- The application sends the translation request to the local model through Ollama.
- The model generates the translated description.
- The interface displays the result and provides copy and download options.
I tested the application locally and verified that the translation interface works.
Why Does Open Innovation Matter?
Open innovation made this project possible as a beginner-friendly experiment.
Using an open-weight model through local inference gave me a way to build an AI-powered application without depending on a hosted AI API for every translation request.
It also gave me the opportunity to learn how a language model connects to a real application, how to design a useful interface around AI, and how to test the complete workflow.
Local inference can offer more control over deployment and help reduce dependence on external inference services. However, it still requires suitable hardware, and model quality and speed depend on the computer and model being used.
I believe open-source tools and open-weight models give students and small developers more opportunities to experiment, learn, and build practical solutions.
Prize Categories
I'm entering for the overall Hacktoberfest Open-Source AI Challenge. I'm not claiming an additional partner prize category because this project currently uses Ollama and Qwen2.5:1.5B rather than a verified challenge partner technology.
Team
@vyshu1230 :Error debugging
@pashya_rishitha_0610 :Designed and build the app|Language translation|My restaurant.
@himacharan69:Helped to design the App
AI assistance disclosure: I used AI assistance while preparing this article. I reviewed the content against my project and testing experience before publication.
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