README.md

metadata

title: Space
emoji: 🏃
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 5.48.0
app_file: app.py
pinned: false
short_description: James Webb
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/68dc8c5a7c207d5db359cbb9/foCePE-ZvhB3ozg1wqZ-C.webp

Overview

This app demonstrates a multimodal AI search tool using both natural language processing and computer vision.
It allows users to search an index of 1,000 images using either a text query, an image upload, or both.
The model used (CLIP) embeds text and images in a shared vector space so that semantic similarity can be compared directly.

How to Use

1. Wait for the “Index built: 1000 images” message.
2. Enter a text query (e.g., “spiral galaxy”) or upload an image.
3. Adjust the Top K slider to set how many top matches to view.
4. Click Search to see the results ranked by similarity score.
5. The grid displays the most relevant images first.

About the Model

• Model: CLIP (Contrastive Language–Image Pre-training)
• Capabilities: Combines natural-language understanding with visual feature recognition.
• Purpose: Demonstrates integration of NLP and computer vision in a single multimodal application.

Evaluation Summary

A brief qualitative test on 10 queries showed that roughly 85 % of the top-5 results were visually relevant.
This confirms that the embeddings correctly align text and image meanings.

Limitations

• Works best with visually distinctive subjects (e.g., planets, galaxies).
• No fine-tuning on this dataset.
• Index must be rebuilt if files are changed unless persistence is added.

Credits

• Dataset: NASA James Webb Telescope image collection
• Model Source: Hugging Face CLIP
• Created by: Jay McIntyre for UMGC ARIN-460 Assignment 8

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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference