Update app.py

app.py

@@ -16,13 +16,13 @@ def load_model():
 
 # Cache corpus embedding generation
 @st.cache_data
-def generate_embeddings(model, dataset_file, sample_size=32):
+def generate_embeddings(_model, dataset_file, sample_size=32):
     # Prepare paper texts by combining query and answer fields
     paper_texts = [
         record['query'] + '[SEP]' + record['answer'] for record in dataset_file.select(range(sample_size))
     ]
     # Compute embeddings for all paper texts
-    return paper_texts, model.encode(paper_texts, convert_to_tensor=True, show_progress_bar=True)
+    return paper_texts, _model.encode(paper_texts, convert_to_tensor=True, show_progress_bar=True)
 
 # Cache summarization pipeline
 @st.cache_resource