Update app.py

app.py

@@ -52,10 +52,10 @@ def load_data():
     return data
 
 @st.cache_resource
-def precompute_embeddings(data: pd.DataFrame, _tokenizer, model, batch_size=BATCH_SIZE):
+def precompute_embeddings(data: pd.DataFrame, _tokenizer, _model, batch_size=BATCH_SIZE):
     """
     Precompute embeddings for repository metadata to optimize query performance.
-    The tokenizer is excluded from caching as it is unhashable.
+    The tokenizer and model are excluded from caching as they are unhashable.
     """
     class TextDataset(Dataset):
         def __init__(self, texts: List[str], tokenizer, max_length=512):
@@ -108,7 +108,7 @@ def precompute_embeddings(data: pd.DataFrame, _tokenizer, model, batch_size=BATC
 
     embeddings = []
     for batch in dataloader:
-        batch_embeddings = generate_embeddings_batch(model, batch, device)
+        batch_embeddings = generate_embeddings_batch(_model, batch, device)
         embeddings.extend(batch_embeddings)
 
     data['embedding'] = embeddings