app.py

app.py

@@ -42,17 +42,18 @@ def encode_search_query(search_query, model):
         inputs = tokenizer([search_query],  padding=True, return_tensors="pt")
         #inputs = processor(text=[search_query], images=None, return_tensors="pt", padding=True)
     text_features =  model.get_text_features(**inputs).detach().numpy()
-    return np.array(text_features)
+    return text_features
         
 # Find all matched photos
 def find_matches(text_features, photo_features, photo_ids, results_count=4):
   # Compute the similarity between the search query and each photo using the Cosine similarity
+  text_features = np.array(text_features)
   similarities = (photo_features @ text_features.T).squeeze(1)
   # Sort the photos by their similarity score
   best_photo_idx = (-similarities).argsort()
   # Return the photo IDs of the best matches
   matches = [photo_ids[i] for i in best_photo_idx[:results_count]]
-  return np.array(matches)
+  return matches
   
 def image_search(search_text, search_image, option):