chore: fixing secrets

app.py

@@ -12,7 +12,7 @@ embedding_model = SentenceTransformer(model_name_or_path="all-mpnet-base-v2", de
 tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
 model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", torch_dtype=torch.bfloat16, device_map="auto")
 
-@spaces.GPU(duration=500)
+@spaces.GPU(duration=300)
 def process_query(query):
     text_embeddings = embedding_model.encode(dataset["train"]["text"])
     query_embedding = embedding_model.encode(query)