slight changes to algorithm

app.py

@@ -14,14 +14,14 @@ from faiss_file import model, tokenizer, embeddings_dataset, embed_bert_cls, rec
 
 device = 'cpu'
 
-tokenizer_k = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny")
-model_k = AutoModel.from_pretrained("cointegrated/rubert-tiny")
+#tokenizer_k = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny")
+#model_k = AutoModel.from_pretrained("cointegrated/rubert-tiny")
 kmeans = load('kmeans.joblib')
 emb = load('final_emb.joblib')
 
 def recomendation(input):
 
-    user_input = embed_bert_cls(input, model_k, tokenizer_k)
+    user_input = embed_bert_cls(input, model, tokenizer)#embed_bert_cls(input, model_k, tokenizer_k)
     label = kmeans.predict(user_input.reshape(1, -1))[0]
     sample_df = emb[emb['labels'] == label].copy()
     sample_df['cosine'] = sample_df['embeddings'].apply(lambda x: np.dot(x, user_input) / (np.linalg.norm(x) * np.linalg.norm(user_input)))
@@ -71,7 +71,7 @@ with tab1:
 
 with tab2:
 
-    book_recs = recommend(user_prompt, books_per_page)
+    #book_recs = recommend(user_prompt, books_per_page)
     if button and len(user_prompt) > 1:
         
         book_recs = recomendation(user_prompt)