Update app.py

app.py

@@ -15,23 +15,13 @@ if hf_api_token is None:
 login(token=hf_api_token, add_to_git_credential=True)
 
 
-# Define the local directory where the model will be saved
-local_model_dir = "meta-llama/Llama-2-7b-chat"
-
-# Create the directory if it doesn't exist
-os.makedirs(local_model_dir, exist_ok=True)
-
-# Download the model and tokenizer
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat", cache_dir=local_model_dir)
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat", cache_dir=local_model_dir)
-
 # Initialize the Whisper processor and model
 whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-base")
 whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base")
 
 # Initialize the summarization model and tokenizer
 # Load LLAMA 7B model with accelerate from local path
-model_name = "./LlamaGuard-7b"
+model_name = "meta-llama/Llama-2-7b-chat"
 with init_empty_weights():
     summarization_model = AutoModelForCausalLM.from_pretrained(model_name)