Update app.py

app.py

@@ -16,10 +16,10 @@ 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))
+        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)