Quest 3 - Implementing Text-to-Image Vector Search
$1,000
$2
763
Learning Outcomes
By the end of this quest, you will be able to:
- Integrate the Gradio framework into a machine learning pipeline to create an interactive interface for vector search.
- Implement a text-to-image search system that allows users to input natural language queries and retrieve visually similar images.
- Leverage the CLIP model to generate embeddings for both text and images, and use ChromaDB to perform efficient vector searches.
- Design a real-time image retrieval system with a graphical interface, making it easier for users to interact with the underlying machine learning model.
Quest Details
Introduction
In this quest, you'll take your vector search skills to the next level by integrating a user-friendly interface using Gradio, a Python library for building machine learning and data science demos. This quest will guide you through building an interactive text-to-image search application, where users can enter a natural language description and retrieve the most similar image from a collection. You’ll continue using the CLIP model for generating embeddings and ChromaDB for storing and searching those embeddings, but this time with an easy-to-use interface that allows real-time interaction.
By the end of this quest, you'll be able to deploy a full-fledged application that demonstrates the power of vector search with a modern UI. This quest will give you practical experience in combining powerful machine learning models with intuitive interfaces, enabling you to build solutions that are not only functional but also accessible to users.
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Deliverables
This quest has 1 deliverable.
- Screenshot
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