feat(visualizer): add cosine similarity computation for image comparison

mini-nav/tests/test_visualizer.py

@@ -5,6 +5,7 @@ import io
 
 import numpy as np
 from PIL import Image
+from sklearn.metrics.pairwise import cosine_similarity
 
 
 class TestImageUploadSimilaritySearch:
@@ -33,3 +34,44 @@ class TestImageUploadSimilaritySearch:
         # Verify the image is valid
         assert parsed_img.size == (224, 224)
         assert parsed_img.mode == "RGB"
+
+
+class TestCosineSimilarity:
+    """Test suite for cosine similarity computation between feature vectors."""
+
+    def test_identical_vectors_return_one(self):
+        """Identical vectors should have cosine similarity of 1.0."""
+        vec = np.random.randn(1024).tolist()
+        similarity = cosine_similarity([vec], [vec])[0][0]
+        assert np.isclose(similarity, 1.0)
+
+    def test_orthogonal_vectors_return_zero(self):
+        """Orthogonal vectors should have cosine similarity of 0.0."""
+        vec_a = [1.0, 0.0]
+        vec_b = [0.0, 1.0]
+        similarity = cosine_similarity([vec_a], [vec_b])[0][0]
+        assert np.isclose(similarity, 0.0)
+
+    def test_opposite_vectors_return_negative_one(self):
+        """Opposite vectors should have cosine similarity of -1.0."""
+        vec_a = [1.0, 0.0, 0.0]
+        vec_b = [-1.0, 0.0, 0.0]
+        similarity = cosine_similarity([vec_a], [vec_b])[0][0]
+        assert np.isclose(similarity, -1.0)
+
+    def test_similarity_range(self):
+        """Cosine similarity should always be within [-1, 1]."""
+        # Random vectors
+        for _ in range(10):
+            vec_a = np.random.randn(1024).tolist()
+            vec_b = np.random.randn(1024).tolist()
+            similarity = cosine_similarity([vec_a], [vec_b])[0][0]
+            assert -1.0 <= similarity <= 1.0
+
+    def test_similarity_with_list_input(self):
+        """Cosine similarity should work with Python list inputs (as stored in dcc.Store)."""
+        # Simulate feature vectors stored as Python lists in dcc.Store
+        vec_a = [0.1, 0.2, 0.3, 0.4, 0.5]
+        vec_b = [0.1, 0.2, 0.3, 0.4, 0.5]
+        similarity = cosine_similarity([vec_a], [vec_b])[0][0]
+        assert np.isclose(similarity, 1.0)