chore: cleanup @gr .funcs

app.py

@@ -6,21 +6,17 @@ model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
 device = 'cuda'
 torch_dtype = torch.bfloat16
 
-@gr.funcs
 def load_model() -> AutoModelForCausalLM:
     return AutoModelForCausalLM.from_pretrained(model_name, device=device, torch_dtype=torch_dtype)
 
-@gr.funcs
 def load_tokenizer() -> AutoTokenizer:
     return AutoTokenizer.from_pretrained(model_name)
 
-@gr.funcs
 def preprocess_messages(message: str, history: list, system_prompt: str) -> dict:
     messages = [{'role': 'system', 'content': system_prompt}, {'role': 'user', 'content': message}]
     prompt = load_tokenizer().apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
     return prompt
 
-@gr.funcs
 def generate_text(prompt: str, max_new_tokens: int, temperature: float) -> str:
     model = load_model()
     terminators = [load_tokenizer().eos_token_id, load_tokenizer().convert_tokens_to_ids(['\n'])]
@@ -35,7 +31,6 @@ def generate_text(prompt: str, max_new_tokens: int, temperature: float) -> str:
      )
     return load_tokenizer().decode(outputs[0], skip_special_tokens=True)
 
-@gr.funcs
 def chat_function(
     message: str,
     history: list,