test cv extraction

app.py

@@ -23,8 +23,13 @@ from dotenv import load_dotenv
 from huggingface_hub import InferenceClient
 import huggingface_hub
 #zero = torch.Tensor([0]).cuda()
+
 load_dotenv()
 api_token = os.getenv("HF_TOKEN")
+huggingface_hub.login(token=api_token)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b-it')
+model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b-it').to(device)
 
 #@spaces.GPU
 def read_pdf(file_path):
@@ -54,11 +59,6 @@ def read_pdf(file_path):
 
 @spaces.GPU(duration=15)
 def LLM_Inference(cv_text):
-    huggingface_hub.login(token=api_token)
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b-it')
-    model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b-it').to(device)
-
     text = f'''
     You are an AI designed to extract structured information from unstructured text. Your task is to analyze the content of a candidate's CV and extract the following details: