Update app.py

app.py

@@ -20,7 +20,6 @@ nltk.download('punkt_tab')
 index = None
 chunks = None
 embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-rerank_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
 generator = None
 
 # --- PDF Processing and Embedding ---
@@ -123,7 +122,7 @@ def rerank(query, results, keyword_weight=0.3, cross_encoder_weight=0.7):
     keyword_scores = [score_chunk_keywords(chunk) for chunk in results]
 
     # Cross-encoder scoring
-    rerank_model = CrossEncoder(rerank_model)
+    rerank_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
     query_results = [[query, f"Document: {result['document_id']}, Section: {result['section_header']}, Text: {result['text']}"] for result in results]
     cross_encoder_scores = rerank_model.predict(query_results)
 
@@ -145,8 +144,6 @@ def merge_chunks(retrieved_chunks):
 # --- Confidence Calculation ---
 def calculate_confidence(query, context, answer):
     """Calculates confidence score based on question-context and context-answer similarity."""
-    embedding_model = SentenceTransformer(embedding_model)
-
     query_embedding = embedding_model.encode([query], convert_to_numpy=True)
     context_embedding = embedding_model.encode([context], convert_to_numpy=True)
     answer_embedding = embedding_model.encode([answer], convert_to_numpy=True)