Update app.py

app.py

@@ -11,11 +11,15 @@ import cv2
 import chromadb
 from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
 import torch.nn as nn
+import warnings
+
+# Suppress specific warnings
+warnings.filterwarnings("ignore", category=UserWarning, module="transformers.utils.deprecation")
 
 # Load CLIP model and tokenizer
 @st.cache_resource
 def load_clip_model():
-    model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionSigLIP')
+    model, _, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionSigLIP')
     tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionSigLIP')
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model.to(device)
@@ -46,9 +50,14 @@ def load_image_from_url(url, max_retries=3):
             else:
                 return None
 
-#Load chromaDB
-client = chromadb.PersistentClient(path="./clothesDB")
-collection = client.get_collection(name="clothes_items_ver3")
+# Load ChromaDB
+@st.cache_resource
+def load_chromadb():
+    client = chromadb.PersistentClient(path="./clothesDB")
+    collection = client.get_collection(name="clothes_items_ver3")
+    return collection
+
+collection = load_chromadb()
 
 def get_image_embedding(image):
     image_tensor = preprocess_val(image).unsqueeze(0).to(device)
@@ -229,4 +238,7 @@ else:  # Text search
                     st.write(f"Discount: {img['info']['discount']}%")
                     st.write(f"Similarity: {img['similarity']:.2f}")
         else:
-            st.warning("Please enter a search text.")
+            st.warning("Please enter a search text.")
+
+# Display ChromaDB vacuum message
+st.sidebar.warning("If you've upgraded ChromaDB from a version below 0.6, you may benefit from vacuuming your database. Run 'chromadb utils vacuum --help' for more information.")