Update src/app.py

src/app.py

@@ -77,10 +77,10 @@ def get_neighbors(query_image, selected_descriptor, selected_distance, top_k=5):
             scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples(
                 'color_embeddings', qi_np, k=top_k)
         elif selected_distance == "Chi-squared":
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(histA=qi_embedding, histB=row['color_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(qi_embedding,row['color_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         else:
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(histA=qi_embedding, histB=row['color_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(qi_embedding,row['color_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         images = retrieved_examples['image'] #retrieved images is a dict, with images and embeddings
         return images
@@ -89,12 +89,12 @@ def get_neighbors(query_image, selected_descriptor, selected_distance, top_k=5):
         qi_embedding = clip_model.encode_image(query_image)
         if selected_distance == "FAISS":
             scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples(
-                'clip_embeddings', qi_np, k=top_k)
+                'clip_embeddings', qi_embedding, k=top_k)
         elif selected_distance == "Chi-squared":
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(histA=qi_embedding, histB=row['clip_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(qi_embedding, row['clip_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         else:
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(histA=qi_embedding, histB=row['clip_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(qi_embedding, row['clip_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         images = retrieved_examples['image']
         return images
@@ -103,12 +103,12 @@ def get_neighbors(query_image, selected_descriptor, selected_distance, top_k=5):
         qi_embedding = lbp_model.describe(query_image)
         if selected_distance == "FAISS":
             scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples(
-                'lbp_embeddings', qi_np, k=top_k)
+                'lbp_embeddings', qi_embedding, k=top_k)
         elif selected_distance == "Chi-squared":
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(histA=qi_embedding, histB=row['lbp_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(qi_embedding, row['lbp_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         else:
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(histA=qi_embedding, histB=row['lbp_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(qi_embedding, row['lbp_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         images = retrieved_examples['image']
         return images
@@ -118,12 +118,12 @@ def get_neighbors(query_image, selected_descriptor, selected_distance, top_k=5):
         qi_embedding = merge_features(lbp_model.describe(query_image), cd.describe(query_image))
         if selected_distance == "FAISS":
             scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples(
-                'lbp_color_embeddings', qi_np, k=top_k)
+                'lbp_color_embeddings', qi_embedding, k=top_k)
         elif selected_distance == "Chi-squared":
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(histA=qi_embedding, histB=row['lbp_color_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': chi2_distance(qi_embedding, row['lbp_color_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         else:
-            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(histA=qi_embedding, histB=row['lbp_color_embeddings'])})
+            tmp_dataset = dataset_with_embeddings.map(lambda row: {'distance': euclidean_distance(qi_embedding, row['lbp_color_embeddings'])})
             retrieved_examples = tmp_dataset.sort("distance")[:5]
         images = retrieved_examples['image']
         return images