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| title: Image Search | |
| sdk: gradio | |
| emoji: 📚 | |
| colorFrom: pink | |
| colorTo: red | |
| # Image Search with SigLIP2 | |
| An image search application powered by Google's SigLIP2 model (`google/siglip2-so400m-patch16-naflex`). | |
| ## Features | |
| - 🔍 Natural language image search | |
| - 🖼️ Search through a curated database of images | |
| - 📊 Similarity scores for each result | |
| - 🎯 Adjustable number of results (top-k) | |
| - 📁 Easy image management via Excel spreadsheet | |
| ## How It Works | |
| The app uses the SigLIP2 vision-language model to: | |
| 1. Load image URLs from an Excel spreadsheet (`image_database.xlsx`) | |
| 2. Encode all images in the database into embeddings | |
| 3. Encode your text query into an embedding | |
| 4. Find images with the highest similarity to your query | |
| 5. Display the top matching results with similarity scores | |
| ## Image Database Format | |
| The app reads image URLs from an Excel file named `image_database.xlsx`. The Excel file should have: | |
| - **Required:** A column named `url` (or `URL`, `image_url`, `urls`, `link`, or `image`) containing the image URLs | |
| - **Optional:** Additional columns like `description`, `category`, etc. for your own reference | |
| ### Example Excel Format: | |
| | url | description | | |
| |-----|-------------| | |
| | https://example.com/image1.jpg | Mountain landscape | | |
| | https://example.com/image2.jpg | Cat photo | | |
| | https://example.com/image3.jpg | Beach sunset | | |
| ### To Update Your Image Database: | |
| 1. Edit `image_database.xlsx` with your own image URLs | |
| 2. Save the file | |
| 3. Restart the Gradio app | |
| The app will automatically load all URLs from the Excel file at startup. | |
| ## Usage | |
| 1. Enter a search query (e.g., "sunset", "cat", "mountains", "technology") | |
| 2. Adjust the number of results you want to see | |
| 3. Click "Search" or press Enter | |
| 4. View the matching images with their similarity scores | |
| ## Example Queries | |
| - "a cat" | |
| - "mountain landscape" | |
| - "beach sunset" | |
| - "city skyline at night" | |
| - "person running" | |
| - "food on a plate" | |
| - "car on the road" | |
| - "flowers in bloom" | |
| ## Model | |
| This space uses the **google/siglip2-so400m-patch16-naflex** model, a state-of-the-art vision-language model from Google. | |
| ## Local Setup | |
| To run this locally: | |
| ```bash | |
| pip install -r requirements.txt | |
| python app.py | |
| ``` | |
| Make sure you have `image_database.xlsx` in the same directory. | |
| ## Deployment on Hugging Face Spaces | |
| 1. Create a new Space on Hugging Face | |
| 2. Select "Gradio" as the SDK | |
| 3. Upload `app.py`, `requirements.txt`, and `image_database.xlsx` | |
| 4. The Space will automatically build and deploy | |
| ## Files Included | |
| - `app.py` - Main Gradio application | |
| - `requirements.txt` - Python dependencies | |
| - `image_database.xlsx` - Excel spreadsheet containing image URLs | |
| - `README.md` - This file | |
| ## License | |
| This application is provided as-is for demonstration purposes. The SigLIP2 model is provided by Google and subject to its own license terms. |