Fast, Intuitive, Smart: Restaurant search engine with Cloudflare AI

This project was submitted to the Cloudflare AI Challenge.

The Inspiration Behind “Cule filo”

Imagine you’re craving your favorite meal—a local specialty or a unique dish you tried once and can’t forget. Now, imagine finding the best spot for that meal near you, simply by typing the dish name and your location. That’s where “Cule filo” comes in, our AI-powered restaurant search engine that guides you to the top 3 restaurants serving exactly what you’re in the mood for. Built with powerful AI models and designed for simplicity, this free app is your smart companion to uncover culinary gems in your area.

Our Development Journey

Building “Cule filo” was an exhilarating journey that started with a brainstorm on how to leverage Cloudflare’s AI tools to bring real value to users. After exploring various ideas, we zeroed in on the concept of a restaurant search engine that uses AI to make dining discovery intuitive and exciting.

To make it happen, we brought together multiple AI models, each serving a distinct role in shaping a smooth user experience:

• Suggestions & Thumbnails: We used the llama-2-13b-chat-awq model to suggest alternative restaurants when fewer than 3 results matched the search, ensuring users always find relevant options. The same model selects thumbnails for the results, making them visually appealing.
• Summarized Descriptions: For quick overviews of each restaurant, we turned to the bart-large-cnn model, which condenses user reviews into concise descriptions that capture each place’s unique qualities.
• Image-to-Text Descriptions: Lastly, we used the uform-gen2-qwen-500m model to analyze restaurant photos and generate text descriptions, giving users a sense of the restaurant ambiance before they even step inside.

Throughout development, we prioritized creating an interface that feels both responsive and intuitive. Real-time search logs keep users engaged, and a search history feature allows easy access to previous explorations.

AI Models in Action

Our project is particularly innovative in its use of multiple AI models per task and the integration of three distinct AI task types, making it a standout submission in the challenge:

1. Multiple Models per Task:

• The llama-2-13b-chat-awq model is used both for generating suggestions when the initial search yields limited results and for selecting suitable thumbnails.
• The bart-large-cnn model generates summaries of user reviews, giving a clear snapshot of what to expect at each restaurant.
• The uform-gen2-qwen-500m model converts restaurant images into textual descriptions, adding depth and context to the search results.

1. Triple Task Types:

• Text Generation: The llama-2-13b-chat-awq model generates recommendations and captions.
• Text Summarization: The bart-large-cnn model condenses user reviews into summaries.
• Image-to-Text: The uform-gen2-qwen-500m model creates descriptions based on restaurant images.

To wrap up

This project was a true team effort, and I couldn’t have done it without the creativity and support of my awesome friends and teammates: gjhernandez and krthr. Building this together was a blast, and we’re proud of what we’ve accomplished.

If you’re curious about the inner workings or just want to poke around the code, you’re more than welcome to check out the repository: https://github.com/sjdonado/culefilo.