first app files

backend.py

@@ -22,18 +22,6 @@ Settings.embed_model = InstructorEmbedding(model_name="hkunlp/instructor-base")
 Settings.llm  = GemmaLLMInterface(model=model, tokenizer=tokenizer)
 
 
-"""os.environ["KAGGLE_USERNAME"] = "middi0"
-os.environ["KAGGLE_KEY"] = "b7eed1ea5cfb30e8eb13b085af2e427b"
-
-# Let's load Gemma using Keras
-gemma_model_id = "gemma2_instruct_2b_en"
-gemma = keras_nlp.models.GemmaCausalLM.from_preset(gemma_model_id)
-
-# This settings define what models will be used by LlamaIndex
-Settings.embed_model = InstructorEmbedding(model_name="hkunlp/instructor-base")
-Settings.llm = GemmaLLMInterface(model=gemma)"""
-
-
 ############################---------------------------------
 
 # CHUNKING