Update model.py

model.py

@@ -43,9 +43,9 @@ def generate_answer(model, tokenizer, context, question, max_length=256):
 # print(model)
 
 def find_context(pos_sentences,question,model,embedings='similarity_embeddings.npy'):
-    pos_embeddings = np.load(embedings,allow_pickle=True)
+    data = np.load(embedings)
+    pos_embeddings = data["embeddings"]
     query_embedding = model.encode(question)
-    # pos_sentences = dataset_a["context"]
     similarities = cosine_similarity([query_embedding], pos_embeddings)
     most_similar_idx = np.argmax(similarities)
     return pos_sentences[most_similar_idx]