Update app.py

app.py

@@ -315,13 +315,13 @@ def match_entities(query_entities, sentence_entities):
 def extract_relevant_portions(document_texts, query, max_portions=3, portion_size=1, min_query_words=1):
     relevant_portions = {}
     # Extract entities from the query
-    #ner_biobert = models['ner_pipeline']
     query_entities = extract_entities(query)
     print(f"Extracted Query Entities: {query_entities}")
     for doc_id, doc_text in enumerate(document_texts):
         sentences = nltk.sent_tokenize(doc_text)  # Split document into sentences
         doc_relevant_portions = []
         # Extract entities from the entire document
+        ner_biobert = models['ner_pipeline']
         doc_entities = extract_entities(doc_text, ner_biobert)
         print(f"Document {doc_id} Entities: {doc_entities}")
         for i, sentence in enumerate(sentences):