Remove UMAP tooling from core

.github/workflows/ci.yml

@@ -14,4 +14,4 @@ jobs:
           python-version: "3.11"
       - name: Compile Python (syntax check)
         run: |
-          python -m py_compile image_database.py visualize_umap.py
+          python -m py_compile image_database.py

CONTRIBUTING.md

@@ -10,4 +10,4 @@ Please open a pull request for any change.
 
 ### Checks
 
-CI runs a lightweight syntax check (`python -m py_compile`) on `image_database.py` and `visualize_umap.py`.
+CI runs a lightweight syntax check (`python -m py_compile`) on `image_database.py`.

README.md

@@ -11,7 +11,6 @@ A searchable image database using SigLIP 2 (CLIP) embeddings and SQLite-vec for
 - **Image Search**: Find similar images using a reference image
 - **Combined Search**: Combine text and image queries with weighted blending
 - **Interactive Mode**: Load model once and run multiple queries
-- **3D Visualization**: UMAP-based 3D visualization of image embeddings with clustering
 - **HTML Gallery**: Beautiful search results with image previews and direct file access
 
 ![Search Results Example](browser.png)
@@ -34,7 +33,7 @@ cd CLIP-database
 
 2. Install dependencies:
 ```bash
-cd github
+cd core
 pip install -r requirements.txt
 ```
 
@@ -53,7 +52,7 @@ pip install sqlite-vec
 Scan a directory and build the image database:
 
 ```bash
-cd github
+cd core
 python image_database.py scan /path/to/images --db "/path/to/database.db"
 ```
 
@@ -64,15 +63,15 @@ Options:
 - `--limit`: Limit number of images to process (for testing)
 
 ```bash
-cd github
+cd core
 python image_database.py scan /path/to/images --batch-size 75 --inference-batch-size 16 --profile --limit 100
 ```
 
 ### Searching Images
 
 #### Text Search
 ```bash
-cd github
+cd core
 python image_database.py search "a red car" -k 20 --db "/path/to/database.db"
 ```
 
@@ -83,25 +82,25 @@ python image_database.py search "a red car" --db "/path/to/database.db" -k 20
 
 #### Image Search
 ```bash
-cd github
+cd core
 python image_database.py search /path/to/image.jpg --image -k 20
 ```
 
 #### Combined Search
 ```bash
-cd github
+cd core
 python image_database.py search "sunset" --query2 /path/to/image.jpg --weights 0.7 0.3 -k 20
 ```
 
 #### Negative Prompts
 ```bash
-cd github
+cd core
 python image_database.py search "nature" --negative "buildings" -k 20
 ```
 
 #### Interactive Mode
 ```bash
-cd github
+cd core
 python image_database.py search --interactive
 ```
 
@@ -114,25 +113,6 @@ In interactive mode:
 - Type `quit` or `exit` to end session
 
 
-### 3D Visualization
-
-Generate a UMAP 3D visualization of all image embeddings:
-
-```bash
-cd github
-python visualize_umap.py
-```
-
-This will:
-1. Load embeddings from the database
-2. Compute UMAP projections (cached for future runs)
-3. Cluster embeddings for color coding
-4. Generate an interactive HTML visualization
-
-Open the generated HTML file in your browser and click on points to see image previews.
-
-**Note:** The HTML results include "Open Image" and "Open Folder" links that use the `localexplorer:` protocol. To use these links, install a browser extension like [Local Explorer](https://chrome.google.com/webstore/detail/local-explorer/llbiblehpbpeflfgjcdfcpcakjhddedi) for Chrome/Edge. Without the extension, images will still display, but the file/folder links won't work.
-
 ## Model
 
 This project uses [SigLIP 2 SO400M](https://huggingface.co/google/siglip2-so400m-patch14-224) from Google, which provides:
@@ -154,7 +134,6 @@ The SQLite database contains:
 - Use `--inference-batch-size` to optimize GPU memory usage
 - Enable `--profile` to identify bottlenecks
 - The database uses WAL mode for better concurrent access
-- UMAP projections are cached to avoid recomputation
 
 ## License
 

config.json.example

@@ -2,8 +2,5 @@
   "database_dir": "C:\\MyExampleProject",
   "model_cache_dir": "C:\\MyExampleProject\\models",
   "results_dir": "results",
-  "thumbnails_dir": "thumbnails",
-  "umap_output_file": "umap_3d_visualization.html",
-  "umap_cache_file": "umap_projections_cache.pkl",
-  "umap_metadata_file": "umap_image_metadata.json"
+  "thumbnails_dir": "thumbnails"
 }

requirements.txt

@@ -5,7 +5,4 @@ tqdm>=4.66.0
 numpy>=1.24.0
 sqlite-vec>=0.0.1
 sentencepiece>=0.1.99
-umap-learn>=0.5.5
-plotly>=5.18.0
-pandas>=2.0.0
 PyMuPDF>=1.23.0