README.md

metadata

title: Multimodal AI Search Engine
emoji: 🔍
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
license: mit

🔍 Multimodal AI Search Engine

A sophisticated image search engine that enables both text-to-image and image-to-image similarity search using state-of-the-art deep learning models.

🌟 Features

• 🔤 Text-to-Image Search: Find images using natural language descriptions
• 🖼️ Image-to-Image Search: Upload an image to find visually similar ones
• ⚡ Fast Search: Sub-second query response times using FAISS indexing
• 🎯 High Accuracy: Powered by OpenAI's CLIP-ViT-B-32 model
• 🎨 Modern UI: Clean, responsive Gradio interface

🚀 How It Works

1. First Visit: The app automatically downloads 500 images from Caltech101 dataset
2. Embedding Generation: Creates CLIP embeddings for all images using ViT-B-32 model
3. Index Building: Builds FAISS index for fast similarity search
4. Ready to Search: Use text descriptions or upload images to find similar content

🔧 Technology Stack

• CLIP-ViT-B-32: OpenAI's vision-language model
• FAISS: Facebook's similarity search library
• Gradio: Interactive web interface
• Caltech101: 500 diverse images across 101 categories

📊 Dataset

• Source: Caltech101 via HuggingFace
• Size: 500 randomly sampled images
• Categories: 101 different object classes
• Auto-Setup: Downloads and processes on first run

💡 Usage Tips

• Text Search: Use descriptive phrases like "red car on road" or "cat sitting"
• Image Search: Upload any image to find visually similar ones
• Results: Adjust the number of results using the slider (1-20)
• First Load: May take 5-10 minutes to set up dataset initially

Note: First-time setup may take several minutes as the app downloads and processes the image dataset.