Update app.py

app.py

@@ -15,8 +15,8 @@ from transformers import RobertaModel, AutoTokenizer
 def load():
     text_encoder = RobertaModel.from_pretrained('SajjadAyoubi/clip-fa-text')
     tokenizer = AutoTokenizer.from_pretrained('SajjadAyoubi/clip-fa-text')
-    links = np.load('data.npy', allow_pickle=True)
     image_embeddings = torch.load('embedding.pt')
+    links = np.load('data.npy', allow_pickle=True)
     return text_encoder, tokenizer, links, image_embeddings
 
 
@@ -32,12 +32,12 @@ def get_html(url_list, height=224):
     return html
 
 def compute_embeddings(query):
-    with torch.no_grad():
-        return text_encoder(**tokenizer(query, return_tensors='pt')).pooler_output
+  
+        
         
-@st.cache(show_spinner=False)        
 def image_search(query, top_k=8):
-    text_embedding = compute_embeddings(query)
+    with torch.no_grad():
+        text_embedding = text_encoder(**tokenizer(query, return_tensors='pt')).pooler_output
     values, indices = torch.cosine_similarity(text_embedding, image_embeddings).sort(descending=True)
     return [links[i] for i in indices[:top_k]]