Update app.py

app.py

@@ -64,28 +64,11 @@ def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, pr
     logger.info("Initializing LLM chain...")
 
     # Define the default LLMS based on the language
-    if language == "italiano":
+    if language == "italian":
         default_llm = "google/gemma-7b-it"
     else:
         default_llm = "google/gemma-7b"  # English version
 
-    # Try to load the tokenizer and model with authentication
-    try:
-        # Option 1: Using HF_TOKEN environment variable
-        hf_token = os.getenv("HF_TOKEN")
-        if not hf_token:
-            raise ValueError("HF_TOKEN environment variable is not set")
-
-        tokenizer = AutoTokenizer.from_pretrained(default_llm, token=hf_token)
-        model = AutoModelForCausalLM.from_pretrained(default_llm, token=hf_token)
-    except Exception as e:
-        logger.error(f"Error initializing LLM: {e}")
-        return None, "Failed to initialize LLM"
-
-    # Resize token embeddings if needed
-    if len(tokenizer) > model.config.max_position_embeddings:
-        model.resize_token_embeddings(len(tokenizer))
-
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm=model,
         retriever=vector_db.as_retriever(),